USENIX Security '23 Fall Accepted Papers

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USENIX Security '23 has three submission deadlines. Prepublication versions of the accepted papers from the fall submission deadline are available below.

“Employees Who Don’t Accept the Time Security Takes Are Not Aware Enough”: The CISO View of Human-Centred Security

Jonas Hielscher and Uta Menges, Ruhr University Bochum; Simon Parkin, TU Delft; Annette Kluge and M. Angela Sasse, Ruhr University Bochum

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In larger organisations, the security controls and policies that protect employees are typically managed by a Chief Information Security Officer (CISO). In research, industry, and policy, there are increasing efforts to relate principles of human behaviour interventions and influence to the practice of the CISO, despite these being complex disciplines in their own right. Here we explore how well the concepts of human-centred security (HCS) have survived exposure to the needs of practice: in an action research approach we engaged with n=30 members of a Swiss-based community of CISOs in five workshop sessions over the course of 8 months, dedicated to discussing HCS. We coded and analysed over 25 hours of notes we took during the discussions. We found that CISOs far and foremost perceive HCS as what is available on the market, namely awareness and phishing simulations. While they regularly shift responsibility either to the management (by demanding more support) or to the employees (by blaming them) we see a lack of power but also silo-thinking that prevents CISOs from considering actual human behaviour and friction that security causes for employees. We conclude that industry best practices and the state-of-the-art in HCS research are not aligned.

“Millions of people are watching you”: Understanding the Digital-Safety Needs and Practices of Creators

Patrawat Samermit, Anna Turner, Patrick Gage Kelley, Tara Matthews, Vanessia Wu, Sunny Consolvo, and Kurt Thomas, Google

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Online content creators—who create and share their content on platforms such as Instagram, TikTok, Twitch, and YouTube—are uniquely at-risk of increased digital-safety threats due to their public prominence, the diverse social norms of wide-ranging audiences, and their access to audience members as a valuable resource. We interviewed 23 creators to understand their digital-safety experiences. This includes the security, privacy, and abuse threats they have experienced across multiple platforms and how the threats have changed over time. We also examined the protective practices they have employed to stay safer, including tensions in how they adopt the practices. We found that creators have diverse threat models that take into consideration their emotional, physical, relational, and financial safety. Most adopted protections—including distancing from technology, moderating their communities, and seeking external or social support—only after experiencing a serious safety incident. Lessons from their experiences help us better prepare and protect creators and ensure a diversity of voices are present online.

“Security is not my field, I’m a stats guy”: A Qualitative Root Cause Analysis of Barriers to Adversarial Machine Learning Defenses in Industry

Jaron Mink, University of Illinois at Urbana-Champaign; Harjot Kaur, Leibniz University Hannover; Juliane Schmüser and Sascha Fahl, CISPA Helmholtz Center for Information Security; Yasemin Acar, Paderborn University and George Washington University

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Adversarial machine learning (AML) has the potential to leak training data, force arbitrary classifications, and greatly degrade overall performance of machine learning models, all of which academics and companies alike consider as serious issues. Despite this, seminal work has found that most organizations insufficiently protect against such threats. While the lack of defenses to AML is most commonly attributed to missing knowledge, it is unknown why mitigations are unrealized in industry projects. To better understand the reasons behind the lack of deployed AML defenses, we conduct semi-structured interviews (n=21) with data scientists and data engineers to explore what barriers impede the effective implementation of such defenses. We find that practitioners’ ability to deploy defenses is hampered by three primary factors: a lack of institutional motivation and educational resources for these concepts, an inability to adequately assess their AML risk and make subsequent decisions, and organizational structures and goals that discourage implementation in favor of other objectives. We conclude by discussing practical recommendations for companies and practitioners to be made more aware of these risks, and better prepared to respond.

A Data-free Backdoor Injection Approach in Neural Networks

Peizhuo Lv, Chang Yue, Ruigang Liang, and Yunfei Yang, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China; Shengzhi Zhang, Department of Computer Science, Metropolitan College, Boston University, USA; Hualong Ma, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China; Kai Chen, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China; Beijing Academy of Artificial Intelligence, China

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Recently, the backdoor attack on deep neural networks (DNNs) has been extensively studied, which causes the backdoored models to behave well on benign samples, whereas performing maliciously on controlled samples (with triggers attached). Almost all existing backdoor attacks require access to the original training/testing dataset or data relevant to the main task to inject backdoors into the target models, which is unrealistic in many scenarios, e.g., private training data. In this paper, we propose a novel backdoor injection approach in a "data-free" manner. We collect substitute data irrelevant to the main task and reduce its volume by filtering out redundant samples to improve the efficiency of backdoor injection. We design a novel loss function for fine-tuning the original model into the backdoored one using the substitute data, and optimize the fine-tuning to balance the backdoor injection and the performance on the main task. We conduct extensive experiments on various deep learning scenarios, e.g., image classification, text classification, tabular classification, image generation, and multimodal, using different models, e.g., Convolutional Neural Networks (CNNs), Autoencoders, Transformer models, Tabular models, as well as Multimodal DNNs. The evaluation results demonstrate that our data-free backdoor injection approach can efficiently embed backdoors with a nearly 100\% attack success rate, incurring an acceptable performance downgrade on the main task.

A Large Scale Study of the Ethereum Arbitrage Ecosystem

Robert McLaughlin, Christopher Kruegel, and Giovanni Vigna, University of California, Santa Barbara

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The Ethereum blockchain rapidly became the epicenter of a complex financial ecosystem, powered by decentralized exchanges (DEXs). These exchanges form a diverse capital market where anyone can swap one type of token for another. Arbitrage trades are a normal and expected phenomenon in free capital markets, and, indeed, several recent works identify these transactions on decentralized exchanges.

Unfortunately, existing studies leave significant knowledge gaps in our understanding of the system as a whole, which hinders research into the security, stability, and economic impacts of arbitrage. To address this issue, we perform two large-scale measurements over a 28-month period. First, we design a novel arbitrage identification strategy capable of analyzing over 10x more DEX applications than prior work. This uncovers 3.8 million arbitrages, which yield a total of $321 million in profit. Second, we design a novel arbitrage opportunity detection system, which is the first to support modern complex price models at scale. This system identifies 4 billion opportunities and would generate a weekly profit of 395 Ether (approximately $500,000, at the time of writing). We observe two key insights that demonstrate the usefulness of these measurements: (1) an increasing percentage of revenue is paid to the miners, which threatens consensus stability, and (2) arbitrage opportunities occasionally persist for several blocks, which implies that price-oracle manipulation attacks may be less costly than expected.

A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots

Boyang Zhang and Xinlei He, CISPA Helmholtz Center for Information Security; Yun Shen, NetApp; Tianhao Wang, University of Virginia; Yang Zhang, CISPA Helmholtz Center for Information Security

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Building advanced machine learning (ML) models requires expert knowledge and many trials to discover the best architecture and hyperparameter settings. Previous work demonstrates that model information can be leveraged to assist other attacks, such as membership inference, generating adversarial examples. Therefore, such information, e.g., hyperparameters, should be kept confidential. It is well known that an adversary can leverage a target ML model's output to steal the model's information. In this paper, we discover a new side channel for model information stealing attacks, i.e., models' scientific plots which are extensively used to demonstrate model performance and are easily accessible. Our attack is simple and straightforward. We leverage the shadow model training techniques to generate training data for the attack model which is essentially an image classifier. Extensive evaluation on three benchmark datasets shows that our proposed attack can effectively infer the architecture/hyperparameters of image classifiers based on convolutional neural network (CNN) given the scientific plot generated from it. We also reveal that the attack's success is mainly caused by the shape of the scientific plots, and further demonstrate that the attacks are robust in various scenarios. Given the simplicity and effectiveness of the attack method, our study indicates scientific plots indeed constitute a valid side channel for model information stealing attacks. To mitigate the attacks, we propose several defense mechanisms that can reduce the original attacks' accuracy while maintaining the plot utility. However, such defenses can still be bypassed by adaptive attacks.

Abuse Vectors: A Framework for Conceptualizing IoT-Enabled Interpersonal Abuse

Sophie Stephenson and Majed Almansoori, University of Wisconsin--Madison; Pardis Emami-Naeini, Duke University; Danny Yuxing Huang, New York University; Rahul Chatterjee, University of Wisconsin--Madison

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Tech-enabled interpersonal abuse (IPA) is a pervasive problem. Abusers, often intimate partners, use tools such as spyware to surveil and harass victim-survivors. Unfortunately, anecdotal evidence suggests that smart, Internet-connected devices such as home thermostats, cameras, and Bluetooth item finders may similarly be used against victim-survivors of IPA. To tackle abuse involving smart devices, it is vital that we understand the ecosystem of smart devices that enable IPA. Thus, in this work, we conduct a large-scale qualitative analysis of the smart devices used in IPA. We systematically crawl Google Search results to uncover web pages discussing how abusers use smart devices to enact IPA. By analyzing these web pages, we identify 32 devices used for IPA and detail the varied strategies abusers use for spying and harassment via these devices. Then, we design a simple, yet powerful framework—abuse vectors—which conceptualizes IoT-enabled IPA as four overarching patterns: Covert Spying, Unauthorized Access, Repurposing, and Intended Use. Using this lens, we pinpoint the necessary solutions required to address each vector of IoT abuse and encourage the security community to take action.

ACon^2: Adaptive Conformal Consensus for Provable Blockchain Oracles

Sangdon Park, Georgia Institute of Technology; Osbert Bastani, University of Pennsylvania; Taesoo Kim, Georgia Institute of Technology

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Blockchains with smart contracts are distributed ledger systems that achieve block-state consistency among distributed nodes by only allowing deterministic operations of smart contracts. However, the power of smart contracts is enabled by interacting with stochastic off-chain data, which in turn opens the possibility to undermine the block-state consistency. To address this issue, an oracle smart contract is used to provide a single consistent source of external data; but, simultaneously, this introduces a single point of failure, which is called the oracle problem. To address the oracle problem, we propose an adaptive conformal consensus (ACon2) algorithm that derives a consensus set of data from multiple oracle contracts via the recent advance in online uncertainty quantification learning. Interesting, the consensus set provides a desired correctness guarantee under distribution shift and Byzantine adversaries. We demonstrate the efficacy of the proposed algorithm on two price datasets and an Ethereum case study. In particular, the Solidity implementation of the proposed algorithm shows the potential practicality of the proposed algorithm, implying that online machine learning algorithms are applicable to address security issues in blockchains.

Adversarial Training for Raw-Binary Malware Classifiers

Keane Lucas, Samruddhi Pai, Weiran Lin, and Lujo Bauer, Carnegie Mellon University; Michael K. Reiter, Duke University; Mahmood Sharif, Tel Aviv University

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Machine learning (ML) models have shown promise in classifying raw executable files (binaries) as malicious or benign with high accuracy. This has led to the increasing influence of ML-based classification methods in academic and real-world malware detection, a critical tool in cybersecurity. However, previous work provoked caution by creating variants of malicious binaries, referred to as adversarial examples, that are transformed in a functionality-preserving way to evade detection. In this work, we investigate the effectiveness of using adversarial training methods to create malware classification models that are more robust to some state-of-the-art attacks. To train our most robust models, we significantly increase the efficiency and scale of creating adversarial examples to make adversarial training practical, which has not been done before in raw-binary malware detectors. We then analyze the effects of varying the length of adversarial training, as well as analyze the effects of training with various types of attacks. We find that data augmentation does not deter state-of-the-art attacks, but that using a generic gradient-guided method, used in other discrete domains, does improve robustness. We also show that in most cases, models can be made more robust to malware-domain attacks by adversarially training them with lower-effort versions of the same attack. In the best case, we reduce one state-of-the-art attack's success rate from 90% to 5%. We also find that training with some types of attacks can increase robustness to other types of attacks. Finally, we discuss insights gained from our results, and how they can be used to more effectively train robust malware detectors.

Aegis: Mitigating Targeted Bit-flip Attacks against Deep Neural Networks

Jialai Wang, Tsinghua University; Ziyuan Zhang, Beijing University of Posts and Telecommunications; Meiqi Wang, Tsinghua University; Han Qiu, Tsinghua University and Zhongguancun Laboratory; Tianwei Zhang, Nanyang Technological University; Qi Li, Tsinghua University and Zhongguancun Laboratory; Zongpeng Li, Tsinghua University and Hangzhou Dianzi University; Tao Wei, Ant Group; Chao Zhang, Tsinghua University and Zhongguancun Laboratory

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Bit-flip attacks (BFAs) have attracted substantial attention recently, in which an adversary could tamper with a small number of model parameter bits to break the integrity of DNNs. To mitigate such threats, a batch of defense methods are proposed, focusing on the untargeted scenarios. Unfortunately, they either require extra trustworthy applications or make models more vulnerable to targeted BFAs. Countermeasures against targeted BFAs, stealthier and more purposeful by nature, are far from well established.

In this work, we propose Aegis, a novel defense method to mitigate targeted BFAs. The core observation is that existing targeted attacks focus on flipping critical bits in certain important layers. Thus, we design a dynamic-exit mechanism to attach extra internal classifiers (ICs) to hidden layers. This mechanism enables input samples to early-exit from different layers, which effectively upsets the adversary's attack plans. Moreover, the dynamic-exit mechanism randomly selects ICs for predictions during each inference to significantly increase the attack cost for the adaptive attacks where all defense mechanisms are transparent to the adversary. We further propose a robustness training strategy to adapt ICs to the attack scenarios through simulating BFAs during the IC training phase, to increase model robustness. Extensive evaluations over four well-known datasets and two popular DNN structures reveal that Aegis could effectively mitigate different state-of-the-art targeted attacks, reducing attack success rate by 5-10x, significantly outperforming existing defense methods. We open source the code of Aegis.

AIRS: Explanation for Deep Reinforcement Learning based Security Applications

Jiahao Yu, Northwestern University; Wenbo Guo, Purdue University; Qi Qin, ShanghaiTech University; Gang Wang, University of Illinois at Urbana-Champaign; Ting Wang, The Pennsylvania State University; Xinyu Xing, Northwestern University

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Recently, we have witnessed the success of deep reinforcement learning (DRL) in many security applications, ranging from malware mutation to selfish blockchain mining. Like all other machine learning methods, the lack of explainability has been limiting its broad adoption as users have difficulty establishing trust in DRL models' decisions. Over the past years, different methods have been proposed to explain DRL models but unfortunately, they are often not suitable for security applications, in which explanation fidelity, efficiency, and the capability of model debugging are largely lacking.

In this work, we propose AIRS, a general framework to explain deep reinforcement learning-based security applications. Unlike previous works that pinpoint important features to the agent's current action, our explanation is at the step level. It models the relationship between the final reward and the key steps that a DRL agent takes, and thus outputs the steps that are most critical towards the final reward the agent has gathered. Using four representative security-critical applications, we evaluate AIRS from the perspectives of explainability, fidelity, stability, and efficiency. We show that AIRS could outperform alternative explainable DRL methods. We also showcase AIRS's utility, demonstrating that our explanation could facilitate the DRL model's failure offset, help users establish trust in a model decision, and even assist the identification of inappropriate reward designs.

An Input-Agnostic Hierarchical Deep Learning Framework for Traffic Fingerprinting

Jian Qu, Xiaobo Ma, and Jianfeng Li, Xi’an Jiaotong University; Xiapu Luo, The Hong Kong Polytechnic University; Lei Xue, Sun Yat-sen University; Junjie Zhang, Wright State University; Zhenhua Li, Tsinghua University; Li Feng, Southwest Jiaotong University; Xiaohong Guan, Xi'an Jiaotong University

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Deep learning has proven to be promising for traffic fingerprinting that explores features of packet timing and sizes. Although well-known for automatic feature extraction, it is faced with a gap between the heterogeneousness of the traffic (i.e., raw packet timing and sizes) and the homogeneousness of the required input (i.e., input-specific). To address this gap, we design an input-agnostic hierarchical deep learning framework for traffic fingerprinting that can hierarchically abstract comprehensive heterogeneous traffic features into homogeneous vectors seamlessly digestible by existing neural networks for further classification. The extensive evaluation demonstrates that our framework, with just one paradigm, not only supports heterogeneous traffic input but also achieves better or comparable performance compared to state-of-the-art methods black across a wide range of traffic fingerprinting tasks.

Araña: Discovering and Characterizing Password Guessing Attacks in Practice

Mazharul Islam, University of Wisconsin–Madison; Marina Sanusi Bohuk, Cornell Tech; Paul Chung, University of Wisconsin–Madison; Thomas Ristenpart, Cornell Tech; Rahul Chatterjee, University of Wisconsin–Madison

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Remote password guessing attacks remain one of the largest sources of account compromise. Understanding and characterizing attacker strategies is critical to improving security but doing so has been challenging thus far due to the sensitivity of login services and the lack of ground truth labels for benign and malicious login requests. We perform an in-depth measurement study of guessing attacks targeting two large universities. Using a rich dataset of more than 34 million login requests to the two universities as well as thousands of compromise reports, we were able to develop a new analysis pipeline to identify 29 attack clusters—many of which involved compromises not previously known to security engineers. Our analysis provides the richest investigation to date of password guessing attacks as seen from login services. We believe our tooling will be useful in future efforts to develop real-time detection of attack campaigns, and our characterization of attack campaigns can help more broadly guide mitigation design.

ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks

Phillip Rieger, Marco Chilese, Reham Mohamed, Markus Miettinen, Hossein Fereidooni, and Ahmad-Reza Sadeghi, Technical University of Darmstadt

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IoT application domains, device diversity and connectivity are rapidly growing. IoT devices control various functions in smart homes and buildings, smart cities, and smart factories, making these devices an attractive target for attackers. On the other hand, the large variability of different application scenarios and inherent heterogeneity of devices make it very challenging to reliably detect abnormal IoT device behaviors and distinguish these from benign behaviors. Existing approaches for detecting attacks are mostly limited to attacks directly compromising individual IoT devices, or, require predefined detection policies. They cannot detect attacks that utilize the control plane of the IoT system to trigger actions in an unintended/malicious context, e.g., opening a smart lock while the smart home residents are absent.

In this paper, we tackle this problem and propose ARGUS, the first self-learning intrusion detection system for detecting contextual attacks on IoT environments, in which the attacker maliciously invokes IoT device actions to reach its goals. ARGUS monitors the contextual setting based on the state and actions of IoT devices in the environment. An unsupervised Deep Neural Network (DNN) is used for modeling the typical contextual device behavior and detecting actions taking place in abnormal contextual settings. This unsupervised approach ensures that ARGUS is not restricted to detecting previously known attacks but is also able to detect new attacks. We evaluated ARGUS on heterogeneous real-world smart-home settings and achieve at least an F1-Score of 99.64% for each setup, with a false positive rate (FPR) of at most 0.03%.

ARI: Attestation of Real-time Mission Execution Integrity

Jinwen Wang, Yujie Wang, and Ao Li, Washington University in St. Louis; Yang Xiao, University of Kentucky; Ruide Zhang, Wenjing Lou, and Y. Thomas Hou, Virginia Polytechnic Institute and State University; Ning Zhang, Washington University in St. Louis

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With the proliferation of autonomous safety-critical cyber-physical systems (CPS) in our daily life, their security is becoming ever more important. Remote attestation is a powerful mechanism to enable remote verification of system integrity. While recent developments have made it possible to efficiently attest IoT operations, autonomous systems that are built on top of real-time cyber-physical control loops and execute missions independently present new unique challenges.

In this paper, we formulate a new security property, Real-time Mission Execution Integrity (RMEI) to provide proof of correct and timely execution of the missions. While it is an attractive property, measuring it can incur prohibitive overhead for the real-time autonomous system. To tackle this challenge, we propose policy-based attestation of compartments to enable a trade-off between the level of details in measurement and runtime overhead. To further minimize the impact on real-time responsiveness, multiple techniques were developed to improve the performance, including customized software instrumentation and timing recovery through re-execution. We implemented a prototype of ARI and evaluated its performance on five CPS platforms. A user study involving 21 developers with different skill sets was conducted to understand the usability of our solution.

ARMore: Pushing Love Back Into Binaries

Luca Di Bartolomeo, Hossein Moghaddas, and Mathias Payer, EPFL

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Static rewriting enables late-state code changes (e.g., to add mitigations, to remove unnecessary code, or to instrument for code coverage) at low overhead in security-critical environments. Most research on static rewriting has so far focused on the x86 architecture. However, the prevalence and proliferation of ARM-based devices along with a large amount of personal data (e.g., health and sensor data) that they process calls for efficient introspection and analysis capabilities on the ARM platform. Addressing the unique challenges on aarch64, we introduce ARMore, the first efficient, robust, and heuristic-free static binary rewriter for arbitrary aarch64 binaries that produces reassembleable assembly. The key improvements introduced by ARMore make the recovery of indirect control flow an option rather than a necessity. Instead of crashing, the cost of an uncovered target only causes the small overhead of an additional branch. ARMore can rewrite binaries from different languages and compilers (even arbitrary hand-written assembly), both on PIC and non-PIC code, with or without symbols, including exception handling for C++ and Go binaries, and also including binaries with mixed data and text. ARMore is sound as it does not rely on any assumptions about the input binary. ARMore is also efficient: it does not employ any expensive dynamic translation techniques, incurring negligible overhead (<1% in our evaluated benchmarks). Our AFL++ coverage instrumentation pass enables fuzzing of closed-source aarch64 binaries at three times the speed compared to the state-of-the-art (AFL-QEMU), and we found 58 unique crashes in closed-source software. ARMore is the only static rewriter whose rewritten binaries correctly pass all SQLite3 and coreutils test cases and autopkgtest of 97.5% Debian packages.

Attacks are Forwarded: Breaking the Isolation of MicroVM-based Containers Through Operation Forwarding

Jietao Xiao and Nanzi Yang, State Key Lab of ISN, School of Cyber Engineering, Xidian University, China; Wenbo Shen, Zhejiang University, China; Jinku Li and Xin Guo, State Key Lab of ISN, School of Cyber Engineering, Xidian University, China; Zhiqiang Dong and Fei Xie, Tencent Security Yunding Lab, China; Jianfeng Ma, State Key Lab of ISN, School of Cyber Engineering, Xidian University, China

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People proposed to use virtualization techniques to reinforce the isolation between containers. In the design, each container runs inside a lightweight virtual machine (called microVM). MicroVM-based containers benefit from both the security of microVM and the high efficiency of the container, and thus are widely used on the public cloud.

However, in this paper, we demonstrate a new attack surface that can be exploited to break the isolation of the microVM-based container, called operation forwarding attacks. Our key observation is that certain operations of the microVM-based container are forwarded to host system calls and host kernel functions. The attacker can leverage the operation forwarding to exploit the host kernel’s vulnerabilities and exhaust host resources. To fully understand the security risk of operation forwarding attacks, we divide the components of the microVM-based container into three layers according to their functionalities and present corresponding attacking strategies to exploit the operation forwarding of each layer. Moreover, we design eight attacks against Kata Containers and Firecracker-based containers and conduct experiments on the local environment, AWS, and Alibaba Cloud. Our results show that the attacker can trigger potential privilege escalation, downgrade 93.4% IO performance and 75.0% CPU performance of the victim container, and even crash the host. We further give security suggestions for mitigating these attacks.

AURC: Detecting Errors in Program Code and Documentation

Peiwei Hu, Ruigang Liang, and Ying Cao, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China, and School of Cyber Security, University of Chinese Academy of Sciences, China; Kai Chen, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China, School of Cyber Security, University of Chinese Academy of Sciences, China, and Beijing Academy of Artificial Intelligence, China; Runze Zhang, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China, and School of Cyber Security, University of Chinese Academy of Sciences, China

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Error detection in program code and documentation is a critical problem in computer security. Previous studies have shown promising vulnerability discovery performance by extensive code or document-guided analysis. However, the state-of-the-arts have the following significant limitations: (i) They assume the documents are correct and treat the code that violates documents as bugs, thus cannot find documents’ defects and code’s bugs if APIs have defective documents or no documents. (ii) They utilize majority voting to judge the inconsistent code snippets and treat the deviants as bugs, thus cannot cope with situations where correct usage is minor or all use cases are wrong.

In this paper, we present AURC, a static framework for detecting code bugs of incorrect return checks and document defects. We observe that three objects participate in the API invocation, the document, the caller (code that invokes API), and the callee (the source code of API). Mutual corroboration of these three objects eliminates the reliance on the above assumptions. AURC contains a context-sensitive backward analysis to process callees, a pre-trained model-based document classifier, and a container that collects conditions of if statements from callers. After cross-checking the results from callees, callers, and documents, AURC delivers them to the correctness inference module to infer the defective one. We evaluated AURC on ten popular codebases. AURC discovered 529 new bugs that can lead to security issues like heap buffer overflow and sensitive information leakage, and 224 new document defects. Maintainers acknowledge our findings and have accepted 222 code patches and 76 document patches.

Authenticated private information retrieval

Simone Colombo, EPFL; Kirill Nikitin, Cornell Tech; Henry Corrigan-Gibbs, MIT; David J. Wu, UT Austin; Bryan Ford, EPFL

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This paper introduces protocols for authenticated private information retrieval. These schemes enable a client to fetch a record from a remote database server such that (a) the server does not learn which record the client reads, and (b) the client either obtains the "authentic" record or detects server misbehavior and safely aborts. Both properties are crucial for many applications. Standard private-information-retrieval schemes either do not ensure this form of output authenticity, or they require multiple database replicas with an honest majority. In contrast, we offer multi-server schemes that protect security as long as at least one server is honest. Moreover, if the client can obtain a short digest of the database out of band, then our schemes require only a single server. Performing an authenticated private PGP-public-key lookup on an OpenPGP key server's database of 3.5 million keys (3 GiB), using two non-colluding servers, takes under 1.2 core-seconds of computation, essentially matching the time taken by unauthenticated private information retrieval. Our authenticated single-server schemes are 30-100× more costly than state-of-the-art unauthenticated single-server schemes, though they achieve incomparably stronger integrity properties.

AutoFR: Automated Filter Rule Generation for Adblocking

Hieu Le, Salma Elmalaki, and Athina Markopoulou, University of California, Irvine; Zubair Shafiq, University of California, Davis

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Adblocking relies on filter lists, which are manually curated and maintained by a community of filter list authors. Filter list curation is a laborious process that does not scale well to a large number of sites or over time. In this paper, we introduce AutoFR, a reinforcement learning framework to fully automate the process of filter rule creation and evaluation for sites of interest. We design an algorithm based on multi-arm bandits to generate filter rules that block ads while controlling the trade-off between blocking ads and avoiding visual breakage. We test AutoFR on thousands of sites and we show that it is efficient: it takes only a few minutes to generate filter rules for a site of interest. AutoFR is effective: it generates filter rules that can block 86% of the ads, as compared to 87% by EasyList, while achieving comparable visual breakage. Furthermore, AutoFR generates filter rules that generalize well to new sites. We envision that AutoFR can assist the adblocking community in filter rule generation at scale.

autofz: Automated Fuzzer Composition at Runtime

Yu-Fu Fu, Jaehyuk Lee, and Taesoo Kim, Georgia Institute of Technology

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Fuzzing has gained in popularity for software vulnerability detection by virtue of the tremendous effort to develop a diverse set of fuzzers. Thanks to various fuzzing techniques, most of the fuzzers have been able to demonstrate great performance on their selected targets. However, paradoxically, this diversity in fuzzers also made it difficult to select fuzzers that are best suitable for complex real-world programs, which we call selection burden. Communities attempted to address this problem by creating a set of standard benchmarks to compare and contrast the performance of fuzzers for a wide range of applications, but the result was always a suboptimal decision—the best-performing fuzzer on average does not guarantee the best outcome for the target of a user's interest.

To overcome this problem, we propose an automated, yet non-intrusive meta-fuzzer, called autofz, to maximize the benefits of existing state-of-the-art fuzzers via dynamic composition. To an end user, this means that, instead of spending time on selecting which fuzzer to adopt (similar in concept to hyperparameter tuning in ML), one can simply put all of the available fuzzers to autofz (similar in concept to AutoML), and achieve the best, optimal result. The key idea is to monitor the runtime progress of the fuzzers, called trends (similar in concept to gradient descent), and make a fine-grained adjustment of resource allocation (e.g., CPU time) of each fuzzer. This is a stark contrast to existing approaches that statically combine a set of fuzzers, or via exhaustive pre-training per target program - autofz deduces a suitable set of fuzzers of the active workload in a fine-grained manner at runtime. Our evaluation shows that, given the same amount of computation resources, autofz outperforms any best-performing individual fuzzers in 11 out of 12 available benchmarks and beats the best, collaborative fuzzing approaches in 19 out of 20 benchmarks without any prior knowledge in terms of coverage. Moreover, on average, autofz found 152% more bugs than individual fuzzers on UNIFUZZ and FTS, and 415% more bugs than collaborative fuzzing on UNIFUZZ.

Automated Cookie Notice Analysis and Enforcement

Rishabh Khandelwal and Asmit Nayak, University of Wisconsin—Madison; Hamza Harkous, Google, Inc.; Kassem Fawaz, University of Wisconsin—Madison

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Online websites use cookie notices to elicit consent from the users, as required by recent privacy regulations like the GDPR and the CCPA. Prior work has shown that these notices are designed in a way to manipulate users into making website-friendly choices which put users' privacy at risk. In this work, we present CookieEnforcer, a new system for automatically discovering cookie notices and extracting a set of instructions that result in disabling all non-essential cookies. In order to achieve this, we first build an automatic cookie notice detector that utilizes the rendering pattern of the HTML elements to identify the cookie notices. Next, we analyze the cookie notices and predict the set of actions required to disable all unnecessary cookies. This is done by modeling the problem as a sequence-to-sequence task, where the input is a machine-readable cookie notice and the output is the set of clicks to make. We demonstrate the efficacy of CookieEnforcer via an end-to-end accuracy evaluation, showing that it can generate the required steps in 91% of the cases. Via a user study, we also show that CookieEnforcer can significantly reduce the user effort. Finally, we characterize the behavior of CookieEnforcer on the top 100k websites from the Tranco list, showcasing its stability and scalability.

Automated Exploitable Heap Layout Generation for Heap Overflows Through Manipulation Distance-Guided Fuzzing

Bin Zhang, Jiongyi Chen, Runhao Li, Chao Feng, Ruilin Li, and Chaojing Tang, National University of Defense Technology

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Generating exploitable heap layouts is a fundamental step to produce working exploits for heap overflows. For this purpose, the heap primitives identified from the target program, serving as functional units to manipulate the heap layout, are strategically leveraged to construct exploitable states. To flexibly use primitives, prior efforts only focus on particular program types or programs with dispatcher-loop structures. Beyond that, automatically generating exploitable heap layouts is hard for general-purpose programs due to the difficulties in explicitly and flexibly using primitives.

This paper presents Scatter, enabling the generation of exploitable heap layouts for heap overflows in general-purpose programs in a primitive-free manner. At the center of Scatter is a fuzzer that is guided by a new manipulation distance which measures the distance to the corruption of a victim object in the heap layout space. To make the fuzzing-based approach practical, Scatter leverages a set of techniques to improve the efficiency and handle the side effects introduced by the heap manager's sophisticated behaviors in the real-world environment. Our evaluation demonstrates that Scatter can successfully generate a total of 126 exploitable heap layouts for 18 out of 27 heap overflows in 10 general-purpose programs.

BalanceProofs: Maintainable Vector Commitments with Fast Aggregation

Weijie Wang, Annie Ulichney, and Charalampos Papamanthou, Yale University

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We present BalanceProofs, the first vector commitment that is maintainable (i.e., supporting sublinear updates) while also enjoying fast proof aggregation and verification. The basic version of BalanceProofs has O(√nlogn) update time and O(√n) query time and its constant-size aggregated proofs can be produced and verified in milliseconds. In particular, BalanceProofs improves the aggregation time and aggregation verification time of the only known maintainable and aggregatable vector commitment scheme, Hyperproofs (USENIX SECURITY 2022), by up to 1000× and up to 100× respectively. Fast verification of aggregated proofs is particularly useful for applications such as stateless cryptocurrencies (and was a major bottleneck for Hyperproofs), where an aggregated proof of balances is produced once but must be verified multiple times and by a large number of nodes. As a limitation, the updating time in BalanceProofs compared to Hyperproofs is roughly 6× slower, but always stays in the range from 10 to 18 milliseconds. We finally study useful tradeoffs in BalanceProofs between (aggregate) proof size, update time and (aggregate) proof computation and verification, by introducing a bucketing technique, and present an extensive evaluation as well as a comparison to Hyperproofs.

Bilingual Problems: Studying the Security Risks Incurred by Native Extensions in Scripting Languages

Cristian-Alexandru Staicu, CISPA Helmholtz Center for Information Security; Sazzadur Rahaman, University of Arizona; Ágnes Kiss and Michael Backes, CISPA Helmholtz Center for Information Security

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Scripting languages are continuously gaining popularity due to their ease of use and the flourishing software ecosystems surrounding them. These languages offer crash and memory safety by design. Thus, developers do not need to understand and prevent low-level security issues like the ones plaguing the C code. However, scripting languages often allow native extensions, a way for custom C/C++ code to be invoked directly from the high-level language. While this feature promises several benefits, such as increased performance or the reuse of legacy code, it can also break the language’s guarantees, e.g., crash safety.

In this work, we first provide a comparative analysis of the security risks of native extension APIs in three popular scripting languages. Additionally, we discuss a novel methodology for studying the misuse of the native extension API. We then perform an in-depth study of npm, an ecosystem that is most exposed to threats introduced by native extensions. We show that vulnerabilities in extensions can be exploited in their embedding library by producing reads of uninitialized memory, hard crashes, or memory leaks in 33 npm packages simply by invoking their API with well-crafted inputs. Moreover, we identify six open-source web applications in which a weak adversary can deploy such exploits remotely. Finally, we were assigned seven security advisories for the work presented in this paper, most labeled as high severity.

Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information

Heng Li, Huazhong University of Science and Technology; Zhang Cheng, NSFOCUS Technologies Group Co., Ltd. and Huazhong University of Science and Technology; Bang Wu, Liheng Yuan, Cuiying Gao, and Wei Yuan, Huazhong University of Science and Technology; Xiapu Luo, The Hong Kong Polytechnic University

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The function call graph (FCG) based Android malware detection methods have recently attracted increasing attention due to their promising performance. However, these methods are susceptible to adversarial examples (AEs). In this paper, we design a novel black-box AE attack towards the FCG based malware detection system, called BagAmmo. To mislead its target system, BagAmmo purposefully perturbs the FCG feature of malware through inserting "never-executed" function calls into malware code. The main challenges are two-fold. First, the malware functionality should not be changed by adversarial perturbation. Second, the information of the target system (e.g., the graph feature granularity and the output probabilities) is absent.

To preserve malware functionality, BagAmmo employs the try-catch trap to insert function calls to perturb the FCG of malware. Without the knowledge about feature granularity and output probabilities, BagAmmo adopts the architecture of generative adversarial network (GAN), and leverages a multi-population co-evolution algorithm (i.e., Apoem) to generate the desired perturbation. Every population in Apoem represents a possible feature granularity, and the real feature granularity can be achieved when Apoem converges.

Through extensive experiments on over 44k Android apps and 32 target models, we evaluate the effectiveness, efficiency and resilience of BagAmmo. BagAmmo achieves an average attack success rate of over 99.9% on MaMaDroid, APIGraph and GCN, and still performs well in the scenario of concept drift and data imbalance. Moreover, BagAmmo outperforms the state-of-the-art attack SRL in attack success rate.

Bleem: Packet Sequence Oriented Fuzzing for Protocol Implementations

Zhengxiong Luo, Junze Yu, Feilong Zuo, Jianzhong Liu, and Yu Jiang, Tsinghua University; Ting Chen, University of Electronic Science and Technology of China; Abhik Roychoudhury, National University of Singapore; Jiaguang Sun, Tsinghua University

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Protocol implementations are essential components in network infrastructures. Flaws hidden in the implementations can easily render devices vulnerable to adversaries. Therefore, guaranteeing their correctness is important. However, commonly used vulnerability detection techniques, such as fuzz testing, face increasing challenges in testing these implementations due to ineffective feedback mechanisms and insufficient protocol state-space exploration techniques.

This paper presents Bleem, a packet-sequence-oriented black-box fuzzer for vulnerability detection of protocol implementations. Instead of focusing on individual packet generation, Bleem generates packets on a sequence level. It provides an effective feedback mechanism by analyzing the system output sequence noninvasively, supports guided fuzzing by resorting to state-space tracking that encompasses all parties timely, and utilizes interactive traffic information to generate protocol-logic-aware packet sequences. We evaluate Bleem on 15 widely-used implementations of well-known protocols (e.g., TLS and QUIC). Results show that, compared to the state-of-the-art protocol fuzzers such as Peach, Bleem achieves substantially higher branch coverage (up to 174.93% improvement) within 24 hours. Furthermore, Bleem exposed 15 security-critical vulnerabilities in prominent protocol implementations, with 10 CVEs assigned.

BoKASAN: Binary-only Kernel Address Sanitizer for Effective Kernel Fuzzing

Mingi Cho, Dohyeon An, Hoyong Jin, and Taekyoung Kwon, Yonsei University

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Kernel Address Sanitizer (KASAN), an invaluable tool for finding use-after-free and out-of-bounds bugs in the Linux kernel, needs the kernel source for compile-time instrumentation. To apply KASAN to closed-source systems, we should develop a binary-only KASAN, which is challenging. A technique that uses binary rewriting and processor support to run KASAN for binary modules needs a KASAN-applied kernel, thereby still the kernel source. Dynamic instrumentation offers an alternative way to it but greatly increases the performance overhead, rendering the kernel fuzzing impractical.

To address these problems, we present the first practical, binary-only KASAN named BoKASAN, which conducts address sanitization through dynamic instrumentation for the entire kernel binaries efficiently. Our key idea is selective sanitization, which identifies target processes to sanitize and hooks the page fault mechanism for significantly reducing the performance overhead of dynamic instrumentation. Our key insight is that the kernel bugs are most relevant to the processes created by a fuzzer. Thus, BoKASAN deliberately sanitizes the target memory regions related to these processes and leaves the remains unsanitized for effective kernel fuzzing.

Our evaluation results show that BoKASAN is practical on closed-source systems, achieving the compiler-level performance of KASAN even on binary-only kernels and modules. Compared to KASAN on the Linux kernel, BoKASAN detected slightly more bugs in the Janus dataset and slightly fewer bugs in the Syzkaller/SyzVegas dataset; and BoKASAN found the same number of unique bugs in the 5-day fuzzing and executed the similar number of basic blocks. For binary modules on the Windows kernel and the Linux kernel, resp., BoKASAN was effective in finding bugs. An ablation result shows that selective sanitization affected these outcomes.

Bug Hunters’ Perspectives on the Challenges and Benefits of the Bug Bounty Ecosystem

Omer Akgul, University of Maryland; Taha Eghtesad, Pennsylvania State University; Amit Elazari, University of California, Berkeley; Omprakash Gnawali, University of Houston; Jens Grossklags, Technical University of Munich; Michelle L. Mazurek, University of Maryland; Daniel Votipka, Tufts University; Aron Laszka, Pennsylvania State University

Distinguished Paper Award Winner

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Although researchers have characterized the bug-bounty ecosystem from the point of view of platforms and programs, minimal effort has been made to understand the perspectives of the main workers: bug hunters. To improve bug bounties, it is important to understand hunters’ motivating factors, challenges, and overall benefits. We address this research gap with three studies: identifying key factors through a free listing survey (n=56), rating each factor’s importance with a larger-scale factor-rating survey (n=159), and conducting semi-structured interviews to uncover details (n=24). Of 54 factors that bug hunters listed, we find that rewards and learning opportunities are the most important benefits. Further, we find scope to be the top differentiator between programs. Surprisingly, we find earning reputation to be one of the least important motivators for hunters. Of the challenges we identify, communication problems, such as unresponsiveness and disputes, are the most substantial. We present recommendations to make the bug-bounty ecosystem accommodating to more bug hunters and ultimately increase participation in an underutilized market.

BunnyHop: Exploiting the Instruction Prefetcher

Zhiyuan Zhang, Mingtian Tao, and Sioli O'Connell, The University of Adelaide; Chitchanok Chuengsatiansup, The University of Melbourne; Daniel Genkin, Georgia Tech; Yuval Yarom, The University of Adelaide

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The instruction prefetcher is a microarchitectural component whose task is to bring program code into the instruction cache. To predict which code is likely to be executed, the instruction prefetcher relies on the branch predictor.

In this paper we investigate the instruction prefetcher in modern Intel processors. We first propose BunnyHop, a technique that uses the instruction prefetcher to encode branch prediction information as a cache state. We show how to use BunnyHop to perform low-noise attacks on the branch predictor. Specifically, we show how to implement attacks similar to Flush+Reload and Prime+Probe on the branch predictor instead of on the data caches. We then show that BunnyHop allows using the instruction prefetcher as a confused deputy to force cache eviction within a victim. We use this to demonstrate an attack on an implementation of AES protected with both cache coloring and data prefetch.

CAPatch: Physical Adversarial Patch against Image Captioning Systems

Shibo Zhang, USSLAB, Zhejiang University; Yushi Cheng, BNRist, Tsinghua University; Wenjun Zhu, Xiaoyu Ji, and Wenyuan Xu, USSLAB, Zhejiang University

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The fast-growing surveillance systems will make image captioning, i.e., automatically generating text descriptions of images, an essential technique to process the huge volumes of videos efficiently, and correct captioning is essential to ensure the text authenticity. While prior work has demonstrated the feasibility of fooling computer vision models with adversarial patches, it is unclear whether the vulnerability can lead to incorrect captioning, which involves natural language processing after image feature extraction. In this paper, we design CAPatch, a physical adversarial patch that can result in mistakes in the final captions, i.e., either create a completely different sentence or a sentence with keywords missing, against multi-modal image captioning systems. To make CAPatch effective and practical in the physical world, we propose a detection assurance and attention enhancement method to increase the impact of CAPatch and a robustness improvement method to address the patch distortions caused by image printing and capturing. Evaluations on three commonly-used image captioning systems (Show-and-Tell, Self-critical Sequence Training: Att2in, and Bottom-up Top-down) demonstrate the effectiveness of CAPatch in both the digital and physical worlds, whereby volunteers wear printed patches in various scenarios, clothes, lighting conditions. With a size of 5% of the image, physically-printed CAPatch can achieve continuous attacks with an attack success rate higher than 73.1% over a video recorder.

Capstone: A Capability-based Foundation for Trustless Secure Memory Access

Jason Zhijingcheng Yu, National University of Singapore; Conrad Watt, University of Cambridge; Aditya Badole, Trevor E. Carlson, and Prateek Saxena, National University of Singapore

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Capability-based memory isolation is a promising new architectural primitive. Software can access low-level memory only via capability handles rather than raw pointers, which provides a natural interface to enforce security restrictions. Existing architectural capability designs such as CHERI provide spatial safety, but fail to extend to other memory models that security-sensitive software designs may desire. In this paper, we propose Capstone, a more expressive architectural capability design that supports multiple existing memory isolation models in a trustless setup, i.e., without relying on trusted software components. We show how Capstone is well-suited for environments where privilege boundaries are fluid (dynamically extensible), memory sharing/delegation are desired both temporally and spatially, and where such needs are to be balanced with availability concerns. Capstone can also be implemented efficiently. We present an implementation sketch and through evaluation show that its overhead is below 50% in common use cases. We also prototype a functional emulator for Capstone and use it to demonstrate the runnable implementations of six real-world memory models without trusted software components: three types of enclave-based TEEs, a thread scheduler, a memory allocator, and Rust-style memory safety—all within the interface of Capstone.

CarpetFuzz: Automatic Program Option Constraint Extraction from Documentation for Fuzzing

Dawei Wang, Ying Li, and Zhiyu Zhang, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China; Kai Chen, SKLOIS, Institute of Information Engineering, Chinese Academy of Sciences, China; School of Cyber Security, University of Chinese Academy of Sciences, China; Beijing Academy of Artificial Intelligence, China

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The large-scale code in software supports the rich and diverse functionalities, and at the same time contains potential vulnerabilities. Fuzzing, as one of the most popular vulnerability detection methods, continues evolving in both industry and academy, aiming to find more vulnerabilities by covering more code. However, we find that even with the state-of-the-art fuzzers, there is still some unexplored code that can only be triggered using a specific combination of program options. Simply mutating the options may generate many invalid combinations due to the lack of consideration of constraints (or called relationships) among options. In this paper, we leverage natural language processing (NLP) to automatically extract option descriptions from program documents and analyze the relationship (e.g., conflicts, dependencies) among the options before filtering out invalid combinations and only leaving the valid ones for fuzzing. We implemented a tool called CarpetFuzz and evaluated its performance. The results show that CarpetFuzz accurately extracts the relationships from documents with 96.10% precision and 88.85% recall. Based on these relationships, CarpetFuzz reduced the 67.91% option combinations to be tested. It helps AFL find 45.97% more paths that other fuzzers cannot discover. After analyzing 20 popular open-source programs, CarpetFuzz discovered 57 vulnerabilities, including 43 undisclosed ones. We also successfully obtained CVE IDs for 30 vulnerabilities.

Catch You and I Can: Revealing Source Voiceprint Against Voice Conversion

Jiangyi Deng, Yanjiao Chen, Yinan Zhong, and Qianhao Miao, Zhejiang University; Xueluan Gong, Wuhan University; Wenyuan Xu, Zhejiang University

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Voice conversion (VC) techniques can be abused by malicious parties to transform their audios to sound like a target speaker, making it hard for a human being or a speaker verification/identification system to trace the source speaker. In this paper, we make the first attempt to restore the source voiceprint from audios synthesized by voice conversion methods with high credit. However, unveiling the features of the source speaker from a converted audio is challenging since the voice conversion operation intends to disentangle the original features and infuse the features of the target speaker. To fulfill our goal, we develop Revelio, a representation learning model, which learns to effectively extract the voiceprint of the source speaker from converted audio samples. We equip Revelio with a carefully-designed differential rectification algorithm to eliminate the influence of the target speaker by removing the representation component that is parallel to the voiceprint of the target speaker. We have conducted extensive experiments to evaluate the capability of Revelio in restoring voiceprint from audios converted by VQVC, VQVC+, AGAIN, and BNE. The experiments verify that Revelio is able to rebuild voiceprints that can be traced to the source speaker by speaker verification and identification systems. Revelio also exhibits robust performance under inter-gender conversion, unseen languages, and telephony networks.

Cipherfix: Mitigating Ciphertext Side-Channel Attacks in Software

Jan Wichelmann, Anna Pätschke, Luca Wilke, and Thomas Eisenbarth, University of Lübeck

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Trusted execution environments (TEEs) provide an environment for running workloads in the cloud without having to trust cloud service providers, by offering additional hardware-assisted security guarantees. However, main memory encryption as a key mechanism to protect against system-level attackers trying to read the TEE's content and physical, off-chip attackers, is insufficient. The recent Cipherleaks attacks infer secret data from TEE-protected implementations by analyzing ciphertext patterns exhibited due to deterministic memory encryption. The underlying vulnerability, dubbed the ciphertext side-channel, is neither protected by state-of-the-art countermeasures like constant-time code nor by hardware fixes.

Thus, in this paper, we present a software-based, drop-in solution that can harden existing binaries such that they can be safely executed under TEEs vulnerable to ciphertext side-channels, without requiring recompilation. We combine taint tracking with both static and dynamic binary instrumentation to find sensitive memory locations, and mitigate the leakage by masking secret data before it gets written to memory. This way, although the memory encryption remains deterministic, we destroy any secret-dependent patterns in encrypted memory. We show that our proof-of-concept implementation protects various constant-time implementations against ciphertext side-channels with reasonable overhead.

Controlled Data Races in Enclaves: Attacks and Detection

Sanchuan Chen, Fordham University; Zhiqiang Lin, The Ohio State University; Yinqian Zhang, Southern University of Science and Technology

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This paper introduces controlled data race attacks, a new class of attacks against programs guarded by trusted execution environments such as Intel SGX. Controlled data race attacks are analog to controlled channel attacks, where the adversary controls the underlying operating system and manipulates the scheduling of enclave threads and handling of interrupts and exceptions. Controlled data race attacks are of particular interest for two reasons: First, traditionally non-deterministic data race bugs can be triggered deterministically and exploited for security violation in the context of SGX enclaves. Second, an intended single-threaded enclave can be concurrently invoked by the adversary, which triggers unique interleaving patterns that would not occur in traditional settings. To detect the controlled data race vulnerabilities in real-world enclave binaries (including the code linked with the SGX libraries), we present a lockset-based binary analysis detection algorithm. We have implemented our algorithm in a tool named SGXRacer, and evaluated it with four SGX SDKs and eight open-source SGX projects, identifying 1,780 data races originated from 476 shared variables.

CSHER: A System for Compact Storage with HE-Retrieval

Adi Akavia and Neta Oren, University of Haifa; Boaz Sapir and Margarita Vald, Intuit Israel Inc.

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Homomorphic encryption (HE) is a promising technology for protecting data in use, with considerable progress in recent years towards attaining practical runtime performance. However, the high storage overhead associated with HE remains an obstacle to its large-scale adoption. In this work we propose a new storage solution in the two-server model resolving the high storage overhead associated with HE, while preserving rigorous data confidentiality. We empirically evaluated our solution in a proof-of-concept system running on AWS EC2 instances with AWS S3 storage, demonstrating storage size with zero overhead over storing AES ciphertexts, and 10µs amortized end-to-end runtime. In addition, we performed experiments on multiple clouds, i.e., where each server resides on a different cloud, exhibiting similar results. As a central tool we introduce the first perfect secret sharing scheme with fast homomorphic reconstruction over the reals; this may be of independent interest.

DDRace: Finding Concurrency UAF Vulnerabilities in Linux Drivers with Directed Fuzzing

Ming Yuan and Bodong Zhao, Tsinghua University; Penghui Li, The Chinese University of Hong Kong; Jiashuo Liang and Xinhui Han, Peking University; Xiapu Luo, The Hong Kong Polytechnic University; Chao Zhang, Tsinghua University and Zhongguancun Lab

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Concurrency use-after-free (UAF) vulnerabilities account for a large portion of UAF vulnerabilities in Linux drivers. Many solutions have been proposed to find either concurrency bugs or UAF vulnerabilities, but few of them can be directly applied to efficiently find concurrency UAF vulnerabilities. In this paper, we propose the first concurrency directed greybox fuzzing solution DDRace to discover concurrency UAF vulnerabilities efficiently in Linux drivers. First, we identify candidate use-after-free locations as target sites and extract the relevant concurrency elements to reduce the exploration space of directed fuzzing. Second, we design a novel vulnerability related distance metric and an interleaving priority scheme to guide the fuzzer to better explore UAF vulnerabilities and thread interleavings. Lastly, to make test cases reproducible, we design an adaptive kernel state migration scheme to assist continuous fuzzing. We have implemented a prototype of DDRace, and evaluated it on upstream Linux drivers. Results show that DDRace is effective at discovering concurrency use-after-free vulnerabilities. It finds 4 unknown vulnerabilities and 8 known ones, which is more effective than other state-of-the-art solutions.

Defining "Broken": User Experiences and Remediation Tactics When Ad-Blocking or Tracking-Protection Tools Break a Website’s User Experience

Alexandra Nisenoff, University of Chicago and Carnegie Mellon University; Arthur Borem, Madison Pickering, Grant Nakanishi, Maya Thumpasery, and Blase Ur, University of Chicago

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To counteract the ads and third-party tracking ubiquitous on the web, users turn to blocking tools—ad-blocking and tracking-protection browser extensions and built-in features. Unfortunately, blocking tools can cause non-ad, non-tracking elements of a website to degrade or fail, a phenomenon termed breakage. Examples include missing images, non-functional buttons, and pages failing to load. While the literature frequently discusses breakage, prior work has not systematically mapped and disambiguated the spectrum of user experiences subsumed under breakage, nor sought to understand how users experience, prioritize, and attempt to fix breakage. We fill these gaps. First, through qualitative analysis of 18,932 extension-store reviews and GitHub issue reports for ten popular blocking tools, we developed novel taxonomies of 38 specific types of breakage and 15 associated mitigation strategies. To understand subjective experiences of breakage, we then conducted a 95-participant survey. Nearly all participants had experienced various types of breakage, and they employed an array of strategies of variable effectiveness in response to specific types of breakage in specific contexts. Unfortunately, participants rarely notified anyone who could fix the root causes. We discuss how our taxonomies and results can improve the comprehensiveness and prioritization of ongoing attempts to automatically detect and fix breakage.

Design of Access Control Mechanisms in Systems-on-Chip with Formal Integrity Guarantees

Dino Mehmedagić, Mohammad Rahmani Fadiheh, Johannes Müller, Anna Lena Duque Antón, Dominik Stoffel, and Wolfgang Kunz, Rheinland-Pfälzische Technische Universität (RPTU) Kaiserslautern-Landau, Germany

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Many SoCs employ system-level hardware access control mechanisms to ensure that security-critical operations cannot be tampered with by less trusted components of the circuit. While there are many design and verification techniques for developing an access control system, continuous discoveries of new vulnerabilities in such systems suggest a need for an exhaustive verification methodology to find and eliminate such weaknesses. This paper proposes UPEC-OI, a formal verification methodology that exhaustively covers integrity vulnerabilities of an SoC-level access control system. The approach is based on iteratively checking a 2-safety interval property whose formulation does not require any explicit specification of possible attack scenarios. The counterexamples returned by UPEC-OI can provide designers of access control hardware with valuable information on possible attack channels, allowing them to perform pinpoint fixes. We present a verification-driven development methodology which formally guarantees the developed SoC’s access control mechanism to be secure with respect to integrity. We evaluate the proposed approach in a case study on OpenTitan’s Earl Grey SoC where we add an SoC-level access control mechanism alongside malicious IPs to model the threat. UPEC-OI was found vital to guarantee the integrity of the mechanism and was proven to be tractable for SoCs of realistic size.

Detecting and Handling IoT Interaction Threats in Multi-Platform Multi-Control-Channel Smart Homes

Haotian Chi, Shanxi University and Temple University; Qiang Zeng, George Mason University; Xiaojiang Du, Stevens Institute of Technology

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A smart home involves a variety of entities, such as IoT devices, automation applications, humans, voice assistants, and companion apps. These entities interact in the same physical environment, which can yield undesirable and even hazardous results, called IoT interaction threats. Existing work on interaction threats is limited to considering automation apps, ignoring other IoT control channels, such as voice commands, companion apps, and physical operations. Second, it becomes increasingly common that a smart home utilizes multiple IoT platforms, each of which has a partial view of device states and may issue conflicting commands. Third, compared to detecting interaction threats, their handling is much less studied. Prior work uses generic handling policies, which are unlikely to fit all homes. We present IoTMediator, which provides accurate threat detection and threat-tailored handling in multi-platform multi-control-channel homes. Our evaluation in two real-world homes demonstrates that IoTMediator significantly outperforms prior state-of-the-art work.

Detecting Multi-Step IAM Attacks in AWS Environments via Model Checking

Ilia Shevrin, Citi; Oded Margalit, Ben-Gurion University

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Cloud services enjoy a surging popularity among IT professionals, owing to their rapid provision of virtual infrastructure on demand. Hand-in-hand with the growing usage, there is also a growing concern about potential security vulnerabilities arising from misconfigurations, exposing resources or allowing malicious actors to escalate privileges. Model checking is a known method for verifying that a finite-state Boolean model of a system satisfies certain properties, where the model and the properties are described in formal logic. In case it doesn’t, a finite trace leading to a violating state can be generated.

In this paper, we present an approach to construct a finite-state Boolean model from the Identity and Access Management (IAM) component of Amazon Web Services (AWS), and a property from an attack target, e.g., read a classified S3 bucket object. We run a model checker that detects whether some initial setup allows an attacker to escalate privileges and reach the target in one or more steps by applying IAM manipulating actions. We show that our approach can discover existing misconfigurations in real AWS environments, and that it can detect multi-step attacks in setups containing tens of AWS accounts with hundreds of resources in under a minute.

Did the Shark Eat the Watchdog in the NTP Pool? Deceiving the NTP Pool’s Monitoring System

Jonghoon Kwon, ETH Zürich; Jeonggyu Song and Junbeom Hur, Korea University; Adrian Perrig, ETH Zürich

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The NTP pool has become a critical infrastructure for modern Internet services and applications. With voluntarily joined thousands of timeservers, it supplies millions of distributed (heterogeneous) systems with time. While numerous efforts have been made to enhance NTP's accuracy, reliability, and security, unfortunately, the NTP pool attracts relatively little attention. In this paper, we provide a comprehensive analysis of NTP pool security, in particular the NTP pool monitoring system, which oversees the correctness and responsiveness of the participating servers. We first investigate strategic attacks that deceive the pool's health-check system to remove legitimate timeservers from the pool. Then, through empirical analysis using monitoring servers and timeservers injected into the pool, we demonstrate the feasibility of our approaches, show their effectiveness, and debate the implications. Finally, we discuss designing a new pool monitoring system to mitigate these attacks.

DiffSmooth: Certifiably Robust Learning via Diffusion Models and Local Smoothing

Jiawei Zhang, UIUC; Zhongzhu Chen, University of Michigan, Ann Arbor; Huan Zhang, Carnegie Mellon University; Chaowei Xiao, Arizona State University; Bo Li, UIUC

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Diffusion models have been leveraged to perform adversarial purification and thus provide both empirical and certified robustness for a standard model. On the other hand, different robustly trained smoothed models have been studied to improve the certified robustness. Thus, it raises a natural question: Can diffusion model be used to achieve improved certified robustness on those robustly trained smoothed models? In this work, we first theoretically show that recovered instances by diffusion models are in the bounded neighborhood of the original instance with high probability; and the "one-shot" denoising diffusion probabilistic models (DDPM) can approximate the mean of the generated distribution of a continuous-time diffusion model, which approximates the original instance under mild conditions. Inspired by our analysis, we propose a certifiably robust pipeline DiffSmooth, which first performs adversarial purification via diffusion models and then maps the purified instances to a common region via a simple yet effective local smoothing strategy. We conduct extensive experiments on different datasets and show that DiffSmooth achieves SOTA-certified robustness compared with eight baselines. For instance, DiffSmooth improves the SOTA-certified accuracy from 36.0% to 53.0% under ℓ2 radius 1.5 on ImageNet.

Diving into Robocall Content with SnorCall

Sathvik Prasad, Trevor Dunlap, Alexander Ross, and Bradley Reaves, North Carolina State University

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Unsolicited bulk telephone calls — termed "robocalls" — nearly outnumber legitimate calls, overwhelming telephone users. While the vast majority of these calls are illegal, they are also ephemeral. Although telephone service providers, regulators, and researchers have ready access to call metadata, they do not have tools to investigate call content at the vast scale required. This paper presents SnorCall, a framework that scalably and efficiently extracts content from robocalls. SnorCall leverages the Snorkel framework that allows a domain expert to write simple labeling functions to classify text with high accuracy. We apply SnorCall to a corpus of transcripts covering 232,723 robocalls collected over a 23-month period. Among many other findings, SnorCall enables us to obtain first estimates on how prevalent different scam and legitimate robocall topics are, determine which organizations are referenced in these calls, estimate the average amounts solicited in scam calls, identify shared infrastructure between campaigns, and monitor the rise and fall of election-related political calls. As a result, we demonstrate how regulators, carriers, anti-robocall product vendors, and researchers can use SnorCall to obtain powerful and accurate analyses of robocall content and trends that can lead to better defenses.

Don’t be Dense: Efficient Keyword PIR for Sparse Databases

Sarvar Patel and Joon Young Seo, Google; Kevin Yeo, Google and Columbia University

Distinguished Paper Award Winner

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In this paper, we introduce SparsePIR, a single-server keyword private information retrieval (PIR) construction that enables querying over sparse databases. At its core, SparsePIR is based on a novel encoding algorithm that encodes sparse database entries as linear combinations while being compatible with important PIR optimizations including recursion. SparsePIR achieves response overhead that is half of state-of-the art keyword PIR schemes without requiring long-term client storage of linear-sized mappings. We also introduce two variants, SparsePIRg and SparsePIRc, that further reduces the size of the serving database at the cost of increased encoding time and small additional client storage, respectively. Our frameworks enable performing keyword PIR with, essentially, the same costs as standard PIR. Finally, we also show that SparsePIR may be used to build batch keyword PIR with halved response overhead without any client mappings.

Duoram: A Bandwidth-Efficient Distributed ORAM for 2- and 3-Party Computation

Adithya Vadapalli, University of Waterloo; Ryan Henry, University of Calgary; Ian Goldberg, University of Waterloo

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We design, analyze, and implement Duoram, a fast and bandwidth-efficient distributed ORAM protocol suitable for secure 2- and 3-party computation settings. Following Doerner and shelat's Floram construction (CCS 2017), Duoram leverages (2,2)-distributed point functions (DPFs) to represent PIR and PIR-writing queries compactly—but with a host of innovations that yield massive asymptotic reductions in communication cost and notable speedups in practice, even for modestly sized instances. Specifically, Duoram introduces a novel method for evaluating dot products of certain secret-shared vectors using communication that is only logarithmic in the vector length. As a result, for memories with n addressable locations, Duoram can perform a sequence of m arbitrarily interleaved reads and writes using just O(mlgn) words of communication, compared with Floram's O(mn) words. Moreover, most of this work can occur during a data-independent preprocessing phase, leaving just O(m) words of online communication cost for the sequence—i.e., a constant online communication cost per memory access.

Educators’ Perspectives of Using (or Not Using) Online Exam Proctoring

David G. Balash, Elena Korkes, Miles Grant, and Adam J. Aviv, The George Washington University; Rahel A. Fainchtein and Micah Sherr, Georgetown University

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The onset of the COVID-19 pandemic changed the landscape of education and led to increased usage of remote proctoring tools that are designed to monitor students when they take assessments outside the classroom. While prior work has explored students' privacy and security concerns regarding online proctoring tools, the perspective of educators is under explored. Notably, educators are the decision makers in the classrooms and choose which remote proctoring services and the level of observations they deem appropriate. To explore how educators balance the security and privacy of their students with the requirements of remote exams, we sent survey requests to over 3,400 instructors at a large private university that taught online classes during the 2020/21 academic year. We had n=125 responses: 21% of the educators surveyed used online exam proctoring services during the remote learning period, and of those, 35% plan to continue using the tools even when there is a full return to in-person learning. Educators who use exam proctoring services are often comfortable with their monitoring capabilities. However, educators are concerned about students sharing certain types of information with exam proctoring companies, particularly when proctoring services collect identifiable information to validate students' identities. Our results suggest that many educators developed alternative assessments that did not require online proctoring and that those who did use online proctoring services often considered the tradeoffs between the potential risks to student privacy and the utility or necessity of exam proctoring services.

ELASM: Error-Latency-Aware Scale Management for Fully Homomorphic Encryption

Yongwoo Lee, Seonyoung Cheon, and Dongkwan Kim, Yonsei University; Dongyoon Lee, Stony Brook University; Hanjun Kim, Yonsei University

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Thanks to its fixed-point arithmetic and SIMD-like vectorization, among fully homomorphic encryption (FHE) schemes that allow computation on encrypted data, RNS-CKKS is widely used for privacy-preserving machine learning services. Prior works have partly automated a daunting scale management task required for RNS-CKKS fixed-point arithmetic, yet none takes an output error into consideration, preventing users from exploring a better error-latency trade-off.

This work proposes a new error- and latency-aware scale management (ELASM) scheme for the RNS-CKKS FHE scheme. By actively controlling the scale of a ciphertext, one can effectively make the impact of noise on an error smaller because an error is a scaled noise introduced by an RNS-CKKS operation. ELASM explores different scale management plans that repurpose an upscale operation as an error reduction operation, estimates the output error and latency of each plan, and iteratively finds the best plan that minimizes the error-latency cost function. In addition, this work proposes a new scale-to-noise ratio (SNR) parameter and introduces fine-grained noise-aware waterlines (a minimum scale requirement) for different RNS-CKKS operations, opening a new opportunity to further improve an error-latency trade-off.

This work implements the proposed ideas in the ELASM compiler along with a new FHE language and type system that enforces the RNS-CKKS constraints including SNR-based noise-aware waterlines. For ten machine and deep learning benchmarks, ELASM finds the better error and latency trade-offs (lower Pareto curves) than the state-of-the-art solutions such as EVA and Hecate.

Eos: Efficient Private Delegation of zkSNARK Provers

Alessandro Chiesa, UC Berkeley and EPFL; Ryan Lehmkuhl, MIT; Pratyush Mishra, Aleo and University of Pennsylvania; Yinuo Zhang, UC Berkeley

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Succinct zero knowledge proofs (i.e. zkSNARKs) are powerful cryptographic tools that enable a prover to convince a verifier that a given statement is true without revealing any additional information. Their attractive privacy properties have led to much academic and industrial interest.

Unfortunately, existing systems for generating zkSNARKs are expensive, which limits the applications in which these proofs can be used. One approach is to take advantage of powerful cloud servers to generate the proof. However, existing techniques for this (e.g., DIZK) sacrifice privacy by revealing secret information to the cloud machines. This is problematic for many applications of zkSNARKs, such as decentralized private currency and computation systems.

In this work we design and implement privacy-preserving delegation protocols for zkSNARKs with universal setup. Our protocols enable a prover to outsource proof generation to a set of workers, so that if at least one worker does not collude with other workers, no private information is revealed to any worker. Our protocols achieve security against malicious workers without relying on heavyweight cryptographic tools.

We implement and evaluate our delegation protocols for a state-of-the-art zkSNARK in a variety of computational and bandwidth settings, and demonstrate that our protocols are concretely efficient. When compared to local proving, using our protocols to delegate proof generation from a recent smartphone (a) reduces end-to-end latency by up to 26×, (b) lowers the delegator's active computation time by up to 1447×, and (c) enables proving up to 256× larger instances.

Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks

Hamid Mozaffari, Virat Shejwalkar, and Amir Houmansadr, University of Massachusetts Amherst

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Federated learning (FL) allows untrusted clients to collaboratively train a common machine learning model, called global model, without sharing their private/proprietary training data. However, FL is susceptible to poisoning by malicious clients who aim to hamper the accuracy of the global model by contributing malicious updates during FL's training process.

We argue that the key factor to the success of poisoning attacks against existing FL systems is the large space of model updates available to the clients to choose from. To address this, we propose Federated Rank Learning (FRL). FRL reduces the space of client updates from model parameter updates (a continuous space of float numbers) in standard FL to the space of parameter rankings (a discrete space of integer values). To be able to train the global model using parameter ranks (instead of parameter weights), FRL leverage ideas from recent supermasks training mechanisms. Specifically, FRL clients rank the parameters of a randomly initialized neural network (provided by the server) based on their local training data, and the FRL server uses a voting mechanism to aggregate the parameter rankings submitted by the clients.

Intuitively, our voting-based aggregation mechanism prevents poisoning clients from making significant adversarial modifications to the global model, as each client will have a single vote! We demonstrate the robustness of FRL to poisoning through analytical proofs and experimentation, and we show its high communication efficiency.

Exorcising "Wraith": Protecting LiDAR-based Object Detector in Automated Driving System from Appearing Attacks

Qifan Xiao, Xudong Pan, Yifan Lu, Mi Zhang, Jiarun Dai, and Min Yang, Fudan University

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Automated driving systems rely on 3D object detectors to recognize possible obstacles from LiDAR point clouds. However, recent works show the adversary can forge non-existent cars in the prediction results with a few fake points (i.e., appearing attack). By removing statistical outliers, existing defenses are however designed for specific attacks or biased by predefined heuristic rules. Towards more comprehensive mitigation, we first systematically inspect the mechanism of previous appearing attacks: Their common weaknesses are observed in crafting fake obstacles which (i) have obvious differences in the local parts compared with real obstacles and (ii) violate the physical relation between depth and point density.

In this paper, we propose a novel plug-and-play defensive module which works by side of a trained LiDAR-based object detector to eliminate forged obstacles where a major proportion of local parts have low objectness, i.e., to what degree it belongs to a real object. At the core of our module is a local objectness predictor, which explicitly incorporates the depth information to model the relation between depth and point density, and predicts each local part of an obstacle with an objectness score. Extensive experiments show, our proposed defense eliminates at least 70% cars forged by three known appearing attacks in most cases, while, for the best previous defense, less than 30% forged cars are eliminated. Meanwhile, under the same circumstance, our defense incurs less overhead for AP/precision on cars compared with existing defenses. Furthermore, We validate the effectiveness of our proposed defense on simulation-based closed-loop control driving tests in the open-source system of Baidu's Apollo.

Extending a Hand to Attackers: Browser Privilege Escalation Attacks via Extensions

Young Min Kim and Byoungyoung Lee, Seoul National University

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Web browsers are attractive targets of attacks, whereby attackers can steal security- and privacy-sensitive data, such as online banking and social network credentials, from users. Thus, browsers adopt the principle of least privilege (PoLP) to minimize damage if compromised, namely, the multiprocess architecture and site isolation. We focus on browser extensions, which are third-party programs that extend the features of modern browsers (Chrome, Firefox, and Safari). The browser also applies PoLP to the extension architecture; that is, two primary extension components are separated, where one component is granted higher privilege, and the other is granted lower privilege.

In this paper, we first analyze the security aspect of extensions. The analysis reveals that the current extension architecture imposes strict security requirements on extension developers, which are difficult to satisfy. In particular, 59 vulnerabilities are found in 40 extensions caused by violated requirements, allowing the attacker to perform privilege escalation attacks, including UXSS (universal cross-site scripting) and stealing passwords or cryptocurrencies in the extensions. Alarmingly, extensions are used by more than half and a third of Chrome and Firefox users, respectively. Furthermore, many extensions in which vulnerabilities are found are extremely popular and have more than 10 million users.

To address the security limitations of the current extension architecture, we present FistBump, a new extension architecture to strengthen PoLP enforcement. FistBump employs strong process isolation between the webpage and content script; thus, the aforementioned security requirements are satisfied by design, thereby eliminating all the identified vulnerabilities. Moreover, FistBump’s design maintains the backward compatibility of the extensions; therefore, the extensions can run with FistBump without modification.

Eye-Shield: Real-Time Protection of Mobile Device Screen Information from Shoulder Surfing

Brian Jay Tang and Kang G. Shin, University of Michigan

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People use mobile devices ubiquitously for computing, communication, storage, web browsing, and more. As a result, the information accessed and stored within mobile devices, such as financial and health information, text messages, and emails, can often be sensitive. Despite this, people frequently use their mobile devices in public areas, becoming susceptible to a simple yet effective attack — shoulder surfing. Shoulder surfing occurs when a person near a mobile user peeks at the user's mobile device, potentially acquiring passcodes, PINs, browsing behavior, or other personal information. We propose, Eye-Shield, a solution to prevent shoulder surfers from accessing/stealing sensitive on-screen information. Eye-Shield is designed to protect all types of on-screen information in real time, without any serious impediment to users' interactions with their mobile devices. Eye-Shield generates images that appear readable at close distances, but appear blurry or pixelated at farther distances and wider angles. It is capable of protecting on-screen information from shoulder surfers, operating in real time, and being minimally intrusive to the intended users. Eye-Shield protects images and text from shoulder surfers by reducing recognition rates to 24.24% and 15.91%. Our implementations of Eye-Shield achieved high frame rates for 1440 × 3088 screen resolutions (24 FPS for Android and 43 FPS for iOS). Eye-Shield also incurs acceptable memory usage, CPU utilization, and energy overhead. Finally, our MTurk and in-person user studies indicate that Eye-Shield protects on-screen information without a large usability cost for privacy-conscious users.

FABRID: Flexible Attestation-Based Routing for Inter-Domain Networks

Cyrill Krähenbühl, Marc Wyss, and David Basin, ETH Zürich; Vincent Lenders, armasuisse; Adrian Perrig, ETH Zürich; Martin Strohmeier, armasuisse

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In its current state, the Internet does not provide end users with transparency and control regarding on-path forwarding devices. In particular, the lack of network device information reduces the trustworthiness of the forwarding path and prevents end-user applications requiring specific router capabilities from reaching their full potential. Moreover, the inability to influence the traffic's forwarding path results in applications communicating over undesired routes, while alternative paths with more desirable properties remain unusable.

In this work, we present FABRID, a system that enables applications to forward traffic flexibly, potentially on multiple paths selected to comply with user-defined preferences, where information about forwarding devices is exposed and transparently attested by autonomous systems (ASes). The granularity of this information is chosen by each AS individually, protecting them from leaking sensitive network details, while the secrecy and authenticity of preferences embedded within the users' packets are protected through efficient cryptographic operations. We show the viability of FABRID by deploying it on a global SCION network test bed, and we demonstrate high throughput on commodity hardware.

Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems

Sahar Abdelnabi and Mario Fritz, CISPA Helmholtz Center for Information Security

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Mis- and disinformation are a substantial global threat to our security and safety. To cope with the scale of online misinformation, researchers have been working on automating fact-checking by retrieving and verifying against relevant evidence. However, despite many advances, a comprehensive evaluation of the possible attack vectors against such systems is still lacking. Particularly, the automated fact-verification process might be vulnerable to the exact disinformation campaigns it is trying to combat. In this work, we assume an adversary that automatically tampers with the online evidence in order to disrupt the fact-checking model via camouflaging the relevant evidence or planting a misleading one. We first propose an exploratory taxonomy that spans these two targets and the different threat model dimensions. Guided by this, we design and propose several potential attack methods. We show that it is possible to subtly modify claim-salient snippets in the evidence and generate diverse and claim-aligned evidence. Thus, we highly degrade the fact-checking performance under many different permutations of the taxonomy’s dimensions. The attacks are also robust against post-hoc modifications of the claim. Our analysis further hints at potential limitations in models’ inference when faced with contradicting evidence. We emphasize that these attacks can have harmful implications on the inspectable and human-in-the-loop usage scenarios of such models, and we conclude by discussing challenges and directions for future defenses.

Fine-grained Poisoning Attack to Local Differential Privacy Protocols for Mean and Variance Estimation

Xiaoguang Li, Xidian University and Purdue University; Ninghui Li and Wenhai Sun, Purdue University; Neil Zhenqiang Gong, Duke University; Hui Li, Xidian University

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Although local differential privacy (LDP) protects individual users' data from inference by an untrusted data curator, recent studies show that an attacker can launch a data poisoning attack from the user side to inject carefully-crafted bogus data into the LDP protocols in order to maximally skew the final estimate by the data curator.

In this work, we further advance this knowledge by proposing a new fine-grained attack, which allows the attacker to fine-tune and simultaneously manipulate mean and variance estimations that are popular analytical tasks for many real-world applications. To accomplish this goal, the attack leverages the characteristics of LDP to inject fake data into the output domain of the local LDP instance. We call our attack the output poisoning attack (OPA). We observe a security-privacy consistency where a small privacy loss enhances the security of LDP, which contradicts the known security-privacy trade-off from prior work. We further study the consistency and reveal a more holistic view of the threat landscape of data poisoning attacks on LDP. We comprehensively evaluate our attack against a baseline attack that intuitively provides false input to LDP. The experimental results show that OPA outperforms the baseline on three real-world datasets. We also propose a novel defense method that can recover the result accuracy from polluted data collection and offer insight into the secure LDP design.

Formal Analysis and Patching of BLE-SC Pairing

Min Shi, Jing Chen, Kun He, Haoran Zhao, Meng Jia, and Ruiying Du, Wuhan University

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Bluetooth Low Energy (BLE) is the mainstream Bluetooth standard and BLE Secure Connections (BLC-SC) pairing is a protocol that authenticates two Bluetooth devices and derives a shared secret key between them. Although BLE-SC pairing employs well-studied cryptographic primitives to guarantee its security, a recent study revealed a logic flaw in the protocol.

In this paper, we develop the first comprehensive formal model of the BLE-SC pairing protocol. Our model is compliant with the latest Bluetooth specification version 5.3 and covers all association models in the specification to discover attacks caused by the interplay between different association models. We also partly loosen the perfect cryptography assumption in traditional symbolic analysis approaches by designing a low-entropy key oracle to detect attacks caused by the poorly derived keys. Our analysis confirms two existing attacks and discloses a new attack. We propose a countermeasure to fix the flaws found in the BLE-SC pairing protocol and discuss the backward compatibility. Moreover, we extend our model to verify the countermeasure, and the results demonstrate its effectiveness in our extended model.

Formal Analysis of Session-Handling in Secure Messaging: Lifting Security from Sessions to Conversations

Cas Cremers, CISPA Helmholtz Center for Information Security; Charlie Jacomme, Inria Paris; Aurora Naska, CISPA Helmholtz Center for Information Security

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The building blocks for secure messaging apps, such as Signal’s X3DH and Double Ratchet (DR) protocols, have received a lot of attention from the research community. They have notably been proved to meet strong security properties even in the case of compromise such as Forward Secrecy (FS) and Post-Compromise Security (PCS). However, there is a lack of formal study of these properties at the application level. Whereas the research works have studied such properties in the context of a single ratcheting chain, a conversation between two persons in a messaging application can in fact be the result of merging multiple ratcheting chains.

In this work, we initiate the formal analysis of secure messaging taking the session-handling layer into account, and apply our approach to Sesame, Signal’s session management. We first experimentally show practical scenarios in which PCS can be violated in Signal by a clone attacker, despite its use of the Double Ratchet. We identify how this is enabled by Signal’s session-handling layer. We then design a formal model of the session-handling layer of Signal that is tractable for automated verification with the Tamarin prover, and use this model to rediscover the PCS violation and propose two provably secure mechanisms to offer stronger guarantees.

Formal Analysis of SPDM: Security Protocol and Data Model version 1.2

Cas Cremers, Alexander Dax, and Aurora Naska, CISPA Helmholtz Center for Information Security

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DMTF is a standards organization by major industry players in IT infrastructure including AMD, Alibaba, Broadcom, Cisco, Dell, Google, Huawei, IBM, Intel, Lenovo, and NVIDIA, which aims to enable interoperability, e.g., including cloud, virtualization, network, servers and storage. It is currently standardizing a security protocol called SPDM, which aims to secure communication over the wire and to enable device attestation, notably also explicitly catering for communicating hardware components.

The SPDM protocol inherits requirements and design ideas from IETF’s TLS 1.3. However, its state machines and transcript handling are substantially different and more complex. While architecture, specification, and open-source libraries of the current versions of SPDM are publicly available, these include no significant security analysis of any kind.

In this work we develop the first formal models of the three modes of the SPDM protocol version 1.2.1, and formally analyze their main security properties.

Forming Faster Firmware Fuzzers

Lukas Seidel, Qwiet AI; Dominik Maier, TU Berlin; Marius Muench, VU Amsterdam and University of Birmingham

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A recent trend for assessing the security of an embedded system’s firmware is rehosting, the art of running the firmware in a virtualized environment, rather than on the original hardware platform. One significant use case for firmware rehosting is fuzzing to dynamically uncover security vulnerabilities.

However, state-of-the-art implementations suffer from high emulator-induced overhead, leading to less-than-optimal execution speeds. Instead of emulation, we propose near-native rehosting: running embedded firmware as a Linux userspace process on a high-performance system that shares the instruction set family with the targeted device. We implement this approach with SAFIREFUZZ, a throughput-optimized rehosting and fuzzing framework for ARM Cortex-M firmware. SAFIREFUZZ takes monolithic binary-only firmware images and uses high-level emulation (HLE) and dynamic binary rewriting to run them on far more powerful hardware with low overhead. By replicating experiments of HALucinator, the state-of-the-art HLE-based rehosting system for binary firmware, we show that SAFIREFUZZ can provide a 690x throughput increase on average during 24-hour fuzzing campaigns while covering up to 30% more basic blocks.

FreeEagle: Detecting Complex Neural Trojans in Data-Free Cases

Chong Fu, Xuhong Zhang, and Shouling Ji, Zhejiang University; Ting Wang, Pennsylvania State University; Peng Lin, Chinese Aeronautical Establishment; Yanghe Feng, National University of Defense Technology; Jianwei Yin, Zhejiang University

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Trojan attack on deep neural networks, also known as backdoor attack, is a typical threat to artificial intelligence. A trojaned neural network behaves normally with clean inputs. However, if the input contains a particular trigger, the trojaned model will have attacker-chosen abnormal behavior. Although many backdoor detection methods exist, most of them assume that the defender has access to a set of clean validation samples or samples with the trigger, which may not hold in some crucial real-world cases, e.g., the case where the defender is the maintainer of model-sharing platforms. Thus, in this paper, we propose FreeEagle, the first data-free backdoor detection method that can effectively detect complex backdoor attacks on deep neural networks, without relying on the access to any clean samples or samples with the trigger. The evaluation results on diverse datasets and model architectures show that FreeEagle is effective against various complex backdoor attacks, even outperforming some state-of-the-art non-data-free backdoor detection methods.

GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation

Sina Sajadmanesh, Idiap Research Institute and EPFL; Ali Shahin Shamsabadi, Alan Turing Institute; Aurélien Bellet, Inria; Daniel Gatica-Perez, Idiap Research Institute and EPFL

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In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation function to statistically obfuscate the presence of a single edge (edge-level privacy) or a single node and all its adjacent edges (node-level privacy). Tailored to the specifics of private learning, GAP's new architecture is composed of three separate modules: (i) the encoder module, where we learn private node embeddings without relying on the edge information; (ii) the aggregation module, where we compute noisy aggregated node embeddings based on the graph structure; and (iii) the classification module, where we train a neural network on the private aggregations for node classification without further querying the graph edges. GAP's major advantage over previous approaches is that it can benefit from multi-hop neighborhood aggregations, and guarantees both edge-level and node-level DP not only for training, but also at inference with no additional costs beyond the training's privacy budget. We analyze GAP's formal privacy guarantees using Rényi DP and conduct empirical experiments over three real-world graph datasets. We demonstrate that GAP offers significantly better accuracy-privacy trade-offs than state-of-the-art DP-GNN approaches and naive MLP-based baselines. Our code is publicly available at

Going through the motions: AR/VR keylogging from user head motions

Carter Slocum, Yicheng Zhang, Nael Abu-Ghazaleh, and Jiasi Chen, University of California, Riverside

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Augmented Reality/Virtual Reality (AR/VR) are the next step in the evolution of ubiquitous computing after personal computers to mobile devices. Applications of AR/VR continue to grow, including education and virtual workspaces, increasing opportunities for users to enter private text, such as passwords or sensitive corporate information. In this work, we show that there is a serious security risk of typed text in the foreground being inferred by a background application, without requiring any special permissions. The key insight is that a user’s head moves in subtle ways as she types on a virtual keyboard, and these motion signals are sufficient for inferring the text that a user types. We develop a system, TyPose, that extracts these signals and automatically infers words or characters that a victim is typing. Once the sensor signals are collected, TyPose uses machine learning to segment the motion signals in time to determine word/character boundaries, and also perform inference on the words/characters themselves. Our experimental evaluation on commercial AR/VR headsets demonstrate the feasibility of this attack, both in situations where multiple users’ data is used for training (82% top-5 word classification accuracy) or when the attack is personalized to a particular victim (92% top-5 word classification accuracy). We also show that first-line defenses of reducing the sampling rate or precision of head tracking are ineffective, suggesting that more sophisticated mitigations are needed.

HECO: Fully Homomorphic Encryption Compiler

Alexander Viand, Patrick Jattke, Miro Haller, and Anwar Hithnawi, ETH Zurich

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In recent years, Fully Homomorphic Encryption ( FHE) has undergone several breakthroughs and advancements leading to a leap in performance. Today, performance is no longer a major barrier to adoption. Instead, it is the complexity of developing an efficient FHE application that currently limits deploying FHE in practice and at scale. Several FHE compilers have emerged recently to ease FHE development. However, none of these answer how to automatically transform imperative programs to secure and efficient FHE implementations. This is a fundamental issue that needs to be addressed before we can realistically expect broader use of FHE. Automating these transformations is challenging because the restrictive set of operations in FHE and their non-intuitive performance characteristics require programs to be drastically transformed to achieve efficiency. Moreover, existing tools are monolithic and focus on individual optimizations. Therefore, they fail to fully address the needs of end-to-end FHE development. In this paper, we present HECO, a new end-to-end design for FHE compilers that takes high-level imperative programs and emits efficient and secure FHE implementations. In our design, we take a broader view of FHE development, extending the scope of optimizations beyond the cryptographic challenges existing tools focus on.

Hey Kimya, Is My Smart Speaker Spying on Me? Taking Control of Sensor Privacy Through Isolation and Amnesia

Piet De Vaere and Adrian Perrig, ETH Zürich

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Although smart speakers and other voice assistants are becoming increasingly ubiquitous, their always-standby nature continues to prompt significant privacy concerns. To address these, we propose Kimya, a hardening framework that allows device vendors to provide strong data-privacy guarantees. Concretely, Kimya guarantees that microphone data can only be used for local processing, and is immediately discarded unless a user-auditable notification is generated. Kimya thus makes devices accountable for their data-retention behavior. Moreover, Kimya is not limited to voice assistants, but is applicable to all devices with always-standby, event-triggered sensors. We implement Kimya for ARM Cortex-M, and apply it to a wake-word detection engine. Our evaluation shows that Kimya introduces low overhead, can be used in constrained environments, and does not require hardware modifications.

Hidden Reality: Caution, Your Hand Gesture Inputs in the Immersive Virtual World are Visible to All!

Sindhu Reddy Kalathur Gopal and Diksha Shukla, University of Wyoming; James David Wheelock, University of Colorado Boulder; Nitesh Saxena, Texas A&M University, College Station

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Text entry is an inevitable task while using Virtual Reality (VR) devices in a wide range of applications such as remote learning, gaming, and virtual meeting. VR users enter passwords/pins to log in to their user accounts in various applications and type regular text to compose emails or browse the internet. The typing activity on VR devices is believed to be resistant to direct observation attacks as the virtual screen in an immersive environment is not directly visible to others present in physical proximity. This paper presents a video-based side-channel attack, Hidden Reality (HR), that shows – although the virtual screen in VR devices is not in direct sight of adversaries, the indirect observations might get exploited to steal the user’s private information.

The Hidden Reality (HR) attack utilizes video clips of the user’s hand gestures while they type on the virtual screen to decipher the typed text in various key entry scenarios on VR devices including typed pins and passwords. Experimental analysis performed on a large corpus of 368 video clips show that the Hidden Reality model can successfully decipher an average of over 75% of the text inputs. The high success rate of our attack model led us to conduct a user study to understand the user’s behavior and perception of security in virtual reality. The analysis showed that over 95% of users were not aware of any security threats on VR devices and believed the immersive environments to be secure from digital attacks. Our attack model challenges users’ false sense of security in immersive environments and emphasizes the need for more stringent security solutions in VR space.

Hiding in Plain Sight: An Empirical Study of Web Application Abuse in Malware

Mingxuan Yao, Georgia Institute of Technology; Jonathan Fuller, United States Military Academy; Ranjita Pai Kasturi, Saumya Agarwal, Amit Kumar Sikder, and Brendan Saltaformaggio, Georgia Institute of Technology

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Web applications provide a wide array of utilities that are abused by malware as a replacement for traditional attacker-controlled servers. Thwarting these Web App-Engaged (WAE) malware requires rapid collaboration between incident responders and web app providers. Unfortunately, our research found that delays in this collaboration allow WAE malware to thrive. We developed Marsea, an automated malware analysis pipeline that studies WAE malware and enables rapid remediation. Given 10K malware samples, Marsea revealed 893 WAE malware in 97 families abusing 29 web apps. Our research uncovered a 226% increase in the number of WAE malware since 2020 and that malware authors are beginning to reduce their reliance on attacker-controlled servers. In fact, we found a 13.7% decrease in WAE malware relying on attacker-controlled servers. To date, we have used Marsea to collaborate with the web app providers to take down 50% of the malicious web app content.

High Recovery with Fewer Injections: Practical Binary Volumetric Injection Attacks against Dynamic Searchable Encryption

Xianglong Zhang and Wei Wang, Huazhong University of Science and Technology; Peng Xu, Huazhong University of Science and Technology and Hubei Key Laboratory of Distributed System Security; Laurence T. Yang, Huazhong University of Science and Technology and St. Francis Xavier University; Kaitai Liang, Delft University of Technology

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Searchable symmetric encryption enables private queries over an encrypted database, but it can also result in information leakages. Adversaries can exploit these leakages to launch injection attacks (Zhang et al., USENIX Security'16) to recover the underlying keywords from queries. The performance of the existing injection attacks is strongly dependent on the amount of leaked information or injection. In this work, we propose two new injection attacks, namely BVA and BVMA, by leveraging a binary volumetric approach. We enable adversaries to inject fewer files than the existing volumetric attacks by using the known keywords and reveal the queries by observing the volume of the query results. Our attacks can thwart well-studied defenses (e.g., threshold countermeasure, padding) without exploiting the distribution of target queries and client databases. We evaluate the proposed attacks empirically in real-world datasets with practical queries. The results show that our attacks can obtain a high recovery rate (> 80%) in the best-case scenario and a roughly 60% recovery even under a large-scale dataset with a small number of injections (< 20 files).

HOLMES: Efficient Distribution Testing for Secure Collaborative Learning

Ian Chang and Katerina Sotiraki, UC Berkeley; Weikeng Chen, UC Berkeley & DZK Labs; Murat Kantarcioglu, University of Texas at Dallas & UC Berkeley; Raluca Popa, UC Berkeley

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Using secure multiparty computation (MPC), organizations which own sensitive data (e.g., in healthcare, finance or law enforcement) can train machine learning models over their joint dataset without revealing their data to each other. At the same time, secure computation restricts operations on the joint dataset, which impedes computation to assess its quality. Without such an assessment, deploying a jointly trained model is potentially illegal. Regulations, such as the European Union's General Data Protection Regulation (GDPR), require organizations to be legally responsible for the errors, bias, or discrimination caused by their machine learning models. Hence, testing data quality emerges as an indispensable step in secure collaborative learning. However, performing distribution testing is prohibitively expensive using current techniques, as shown in our experiments.

We present HOLMES, a protocol for performing distribution testing efficiently. In our experiments, compared with three non-trivial baselines, HOLMES achieves a speedup of more than 10× for classical distribution tests and up to 104× for multidimensional tests. The core of HOLMES is a hybrid protocol that integrates MPC with zero-knowledge proofs and a new ZK-friendly and naturally oblivious sketching algorithm for multidimensional tests, both with significantly lower computational complexity and concrete execution costs.

​​How Library IT Staff Navigate Privacy and Security Challenges and Responsibilities

Alan F. Luo, Noel Warford, and Samuel Dooley, University of Maryland; Rachel Greenstadt, New York University; Michelle L. Mazurek, University of Maryland; Nora McDonald, George Mason University

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Libraries provide critical IT services to patrons who lack access to computational and internet resources. We conducted 12 semi-structured interviews with library IT staff to learn about their privacy and security protocols and policies, the challenges they face implementing them, and how this relates to their patrons. We frame our findings using Sen's capabilities approach and find that library IT staff are primarily concerned with protecting their patrons' privacy from threats outside their walls—police, government authorities, and third parties. Despite their dedication to patron privacy, library IT staff frequently have to grapple with complex tradeoffs between providing easy, fluid, full-featured access to Internet technologies or third-party resources, protecting library infrastructure, and ensuring patron privacy.

How the Great Firewall of China Detects and Blocks Fully Encrypted Traffic

Mingshi Wu, GFW Report; Jackson Sippe, University of Colorado Boulder; Danesh Sivakumar and Jack Burg, University of Maryland; Peter Anderson, Independent researcher; Xiaokang Wang, V2Ray Project; Kevin Bock, University of Maryland; Amir Houmansadr, University of Massachusetts Amherst; Dave Levin, University of Maryland; Eric Wustrow, University of Colorado Boulder

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One of the cornerstones in censorship circumvention is fully encrypted protocols, which encrypt every byte of the payload in an attempt to “look like nothing”. In early November 2021, the Great Firewall of China (GFW) deployed a new censorship technique that passively detects—and subsequently blocks—fully encrypted traffic in real time. The GFW’s new censorship capability affects a large set of popular censorship circumvention protocols, including but not limited to Shadowsocks, VMess, and Obfs4. Although China had long actively probed such protocols, this was the first report of purely passive detection, leading the anti-censorship community to ask how detection was possible.

In this paper, we measure and characterize the GFW’s new system for censoring fully encrypted traffic. We find that, instead of directly defining what fully encrypted traffic is, the censor applies crude but efficient heuristics to exempt traffic that is unlikely to be fully encrypted traffic; it then blocks the remaining non-exempted traffic. These heuristics are based on the fingerprints of common protocols, the fraction of set bits, and the number, fraction, and position of printable ASCII characters. Our Internet scans reveal what traffic and which IP addresses the GFW inspects. We simulate the inferred GFW’s detection algorithm on live traffic at a university network tap to evaluate its comprehensiveness and false positives. We show evidence that the rules we inferred have good coverage of what the GFW actually uses. We estimate that, if applied broadly, it could potentially block about 0.6% of normal Internet traffic as collateral damage.

Our understanding of the GFW’s new censorship mechanism helps us derive several practical circumvention strategies. We responsibly disclosed our findings and suggestions to the developers of different anti-censorship tools, helping millions of users successfully evade this new form of blocking.

How to Cover up Anomalous Accesses to Electronic Health Records

Xiaojun Xu, Qingying Hao, Zhuolin Yang, and Bo Li, University of Illinois at Urbana-Champaign; David Liebovitz, Northwestern University; Gang Wang and Carl A. Gunter, University of Illinois at Urbana-Champaign

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Illegitimate access detection systems in hospital logs perform post hoc detection instead of runtime access restriction to allow widespread access in emergencies. We study the effectiveness of adversarial machine learning strategies against such detection systems on a large-scale dataset consisting of a year of access logs at a major hospital. We study a range of graph-based anomaly detection systems, including heuristic-based and Graph Neural Network (GNN)-based models. We find that evasion attacks, in which covering accesses (that is, accesses made to disguise a target access) are injected during evaluation period of the target access, can successfully fool the detection system. We also show that such evasion attacks can transfer among different detection algorithms. On the other hand, we find that poisoning attacks, in which adversaries inject covering accesses during the training phase of the model, do not effectively mislead the trained detection system unless the attacker is given unrealistic capabilities such as injecting over 10,000 accesses or imposing a high weight on the covering accesses in the training algorithm. To examine the generalizability of the results, we also apply our attack against a state-of-the-art detection model on the LANL network lateral movement dataset, and observe similar conclusions.

ICSPatch: Automated Vulnerability Localization and Non-Intrusive Hotpatching in Industrial Control Systems using Data Dependence Graphs

Prashant Hari Narayan Rajput, NYU Tandon School of Engineering; Constantine Doumanidis and Michail Maniatakos, New York University Abu Dhabi

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The paradigm shift of enabling extensive intercommunication between the Operational Technology (OT) and Information Technology (IT) devices allows vulnerabilities typical to the IT world to propagate to the OT side. Therefore, the security layer offered in the past by air gapping is removed, making security patching for OT devices a hard requirement. Conventional patching involves a device reboot to load the patched code in the main memory, which does not apply to OT devices controlling critical processes due to downtime, necessitating in-memory vulnerability patching. Furthermore, these control binaries are often compiled by in-house proprietary compilers, further hindering the patching process and placing reliance on OT vendors for rapid vulnerability discovery and patch development. The current state-of-the-art hotpatching approaches only focus on firmware and/or RTOS. Therefore, in this work, we develop ICSPatch, a framework to automate control logic vulnerability localization using Data Dependence Graphs (DDGs). With the help of DDGs, ICSPatch pinpoints the vulnerability in the control application. As an independent second step, ICSPatch can non-intrusively hotpatch vulnerabilities in the control application directly in the main memory of Programmable Logic Controllers while maintaining reliable continuous operation. To evaluate our framework, we test ICSPatch on a synthetic dataset of 24 vulnerable control application binaries from diverse critical infrastructure sectors. Results show that ICSPatch could successfully localize all vulnerabilities and generate patches accordingly. Furthermore, the patch added negligible latency increase in the execution cycle while maintaining correctness and protection against the vulnerability.

Improving Real-world Password Guessing Attacks via Bi-directional Transformers

Ming Xu and Jitao Yu, Fudan University; Xinyi Zhang, Facebook; Chuanwang Wang, Shenghao Zhang, Haoqi Wu, and Weili Han, Fudan University

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Password guessing attacks, prevalent issues in the real world, can be conceptualized as efforts to approximate the probability distribution of text tokens. Techniques in the natural language processing (NLP) field naturally lend themselves to password guessing. Among them, bi-directional transformers stand out with their ability to utilize bi-directional contexts to capture the nuances in texts.

To further improve password guessing attacks, we propose a bi-directional-transformer-based guessing framework, referred to as PassBERT, which applies the pre-training / fine-tuning paradigm to password guessing attacks. We first prepare a pre-trained password model, which contains the knowledge of the general password distribution. Then, we design three attack-specific fine-tuning approaches to tailor the pre-trained password model to the following real-world attack scenarios: (1) conditional password guessing, which recovers the complete password given a partial password; (2) targeted password guessing, which compromises the password(s) of a specific user using their personal information; (3) adaptive rule-based password guessing, which selects adaptive mangling rules for a word (i.e., base password) to generate rule-transformed password candidates. The experimental results show that our fine-tuned models can outperform the state-of-the-art models by 14.53%, 21.82% and 4.86% in the three attacks, respectively, demonstrating the effectiveness of bi-directional transformers on downstream guessing attacks. Finally, we propose a hybrid password strength meter to mitigate the risks from the three attacks.

InfinityGauntlet: Expose Smartphone Fingerprint Authentication to Brute-force Attack

Yu Chen and Yang Yu, Xuanwu Lab, Tencent; Lidong Zhai, Institute of Information Engineering, Chinese Academy of Sciences

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Billions of smartphone fingerprint authentications (SFA) occur daily for unlocking, privacy and payment. Existing threats to SFA include presentation attacks (PA) and some case-by-case vulnerabilities. The former need to know the victim's fingerprint information (e.g., latent fingerprints) and can be mitigated by liveness detection and security policies. The latter require additional conditions (e.g., third-party screen protector, root permission) and are only exploitable for individual smartphone models.

In this paper, we conduct the first investigation on the general zero-knowledge attack towards SFA where no knowledge about the victim is needed. We propose a novelty fingerprint brute-force attack on off-the-shelf smartphones, named InfinityGauntlet. Firstly, we discover design vulnerabilities in SFA systems across various manufacturers, operating systems, and fingerprint types to achieve unlimited authentication attempts. Then, we use SPI MITM to bypass liveness detection and make automatic attempts. Finally, we customize a synthetic fingerprint generator to get a valid brute-force fingerprint dictionary.

We design and implement low-cost equipment to launch InfinityGauntlet. A proof-of-concept case study demonstrates that InfinityGauntlet can brute-force attack successfully in less than an hour without any knowledge of the victim. Additionally, empirical analysis on representative smartphones shows the scalability of our work.

Intender: Fuzzing Intent-Based Networking with Intent-State Transition Guidance

Jiwon Kim, Purdue University; Benjamin E. Ujcich, Georgetown University; Dave (Jing) Tian, Purdue University

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Intent-based networking (IBN) abstracts network configuration complexity from network operators by focusing on what operators want the network to do rather than how such configuration should be implemented. While such abstraction eases network management challenges, little attention to date has focused on IBN’s new security concerns that adversely impact an entire network’s correct operation. To motivate the prevalence of such security concerns, we systematize IBN’s security challenges by studying existing bug reports from a representative IBN implementation within the ONOS network operating system. We find that 61% of IBN-related bugs are semantic bugs that are challenging, if not impossible, to detect efficiently by state-of-the-art vulnerability discovery tools.

To tackle existing limitations, we present Intender, the first semantically-aware fuzzing framework for IBN. Intender leverages network topology information and intent-operation dependencies (IOD) to efficiently generate testing inputs. Intender introduces a new feedback mechanism, intent-state transition guidance (ISTG), which traces the history of transitions in intent states. We evaluate Intender using ONOS and find 12 bugs, 11 of which were CVE-assigned security-critical vulnerabilities affecting network-wide control plane integrity and availability. Compared to state-of-the-art fuzzing tools AFL, Jazzer, Zest, and PAZZ, Intender generates up to 78.7× more valid fuzzing input, achieves up to 2.2× better coverage, and detects up to 82.6× more unique errors. Intender with IOD reduces 73.02% of redundant operations and spends 10.74% more time on valid operations. Intender with ISTG leads to 1.8× more intent-state transitions compared to code-coverage guidance.

It's all in your head(set): Side-channel attacks on AR/VR systems

Yicheng Zhang, Carter Slocum, Jiasi Chen, and Nael Abu-Ghazaleh, University of California, Riverside

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With the increasing adoption of Augmented Reality/Virtual Reality (AR/VR) systems, security and privacy concerns attract attention from both academia and industry. This paper demonstrates that AR/VR systems are vulnerable to side-channel attacks launched from software; a malicious application without any special permissions can infer private information about user interactions, other concurrent applications, or even the surrounding world. We develop a number of side-channel attacks targeting different types of private information. Specifically, we demonstrate three attacks on the victim's interactions, successfully recovering hand gestures, voice commands made by victims, and keystrokes on a virtual keyboard, with accuracy exceeding 90%. We also demonstrate an application fingerprinting attack where the spy is able to identify an application being launched by the victim. The final attack demonstrates that the adversary can perceive a bystander in the real-world environment and estimate the bystander's distance with Mean Absolute Error (MAE) of 10.3 cm. We believe the threats presented by our attacks are pressing; they expand our understanding of the threat model faced by these emerging systems and inform the development of new AR/VR systems that are resistant to these threats.

IvySyn: Automated Vulnerability Discovery in Deep Learning Frameworks

Neophytos Christou, Di Jin, and Vaggelis Atlidakis, Brown University; Baishakhi Ray, Columbia University; Vasileios P. Kemerlis, Brown University

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We present IvySyn, the first fully-automated framework for discovering memory error vulnerabilities in Deep Learning (DL) frameworks. IvySyn leverages the statically-typed nature of native APIs in order to automatically perform type-aware mutation-based fuzzing on low-level kernel code. Given a set of offending inputs that trigger memory safety (and runtime) errors in low-level, native DL (C/C++) code, IvySyn automatically synthesizes code snippets in high-level languages (e.g., in Python), which propagate error-triggering input via high(er)-level APIs. Such code snippets essentially act as "Proof of Vulnerability", as they demonstrate the existence of bugs in native code that an attacker can target through various high-level APIs. Our evaluation shows that IvySyn significantly outperforms past approaches, both in terms of efficiency and effectiveness, in finding vulnerabilities in popular DL frameworks. Specifically, we used IvySyn to test Tensor-Flow and PyTorch. Although still an early prototype, IvySyn has already helped the TensorFlow and PyTorch framework developers to identify and fix 61 previously-unknown security vulnerabilities, and assign 39 unique CVEs.

Jinn: Hijacking Safe Programs with Trojans

Komail Dharsee and John Criswell, University of Rochester

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Untrusted hardware supply chains enable malicious, powerful, and permanent alterations to processors known as hardware trojans. Such hardware trojans can undermine any software-enforced security policies deployed on top of the hardware. Existing defenses target a select set of hardware components, specifically those that implement hardware-enforced security mechanisms such as cryptographic cores, user/kernel privilege isolation, and memory protections.

We observe that computing systems exercise general purpose processor logic to implement software-enforced security policies. This makes general purpose logic security critical since tampering with it could violate software-based security policies. Leveraging this insight, we develop a novel class of hardware trojans, which we dub Jinn trojans, that corrupt general-purpose hardware to enable flexible and powerful high level attacks. Jinn trojans deactivate compiler-based security-enforcement mechanisms, making type-safe software vulnerable to memory-safety attacks. We prototyped design-time Jinn trojans in the gem5 simulator and used them to attack programs written in Rust, inducing memory-safety vulnerabilities to launch control-flow hijacking attacks. We find that Jinn trojans can effectively compromise software-enforced security policies by compromising a single bit of architectural state with as little as 8 bits of persistent trojan-internal state. Thus, we show that Jinn trojans are effective even when planted in general purpose hardware, disjoint from any hardware-enforced security components. We show that protecting hardware-enforced security logic is insufficient to keep a system secure from hardware trojans.

KextFuzz: Fuzzing macOS Kernel EXTensions on Apple Silicon via Exploiting Mitigations

Tingting Yin, Tsinghua University and Ant Group; Zicong Gao, State Key Laboratory of Mathematical Engineering and Advanced Computing; Zhenghang Xiao, Hunan University; Zheyu Ma, Tsinghua University; Min Zheng, Ant Group; Chao Zhang, Tsinghua University and Zhongguancun Laboratory

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macOS drivers, i.e., Kernel EXTensions (kext), are attractive attack targets for adversaries. However, automatically discovering vulnerabilities in kexts is extremely challenging because kexts are mostly closed-source, and the latest macOS running on customized Apple Silicon has limited tool-chain support. Most existing static analysis and dynamic testing solutions cannot be applied to the latest macOS. In this paper, we present the first smart fuzzing solution KextFuzz to detect bugs in the latest macOS kexts running on Apple Silicon. Unlike existing driver fuzzing solutions, KextFuzz does not require source code, execution traces, hypervisors, or hardware features (e.g., coverage tracing) and thus is universal and practical. We note that macOS has deployed many mitigations, including pointer authentication, code signature, and userspace kernel layer wrappers, to thwart potential attacks. These mitigations can provide extra knowledge and resources for us to enable kernel fuzzing. KextFuzz exploits these mitigation schemes to instrument the binary for coverage tracking, test privileged kext code that is guarded and infrequently accessed, and infer the type and semantic information of the kext interfaces. KextFuzz has found 48 unique kernel bugs in the macOS kexts. Some of them could cause severe consequences like non-recoverable denial-of-service or damages.

Know Your Cybercriminal: Evaluating Attacker Preferences by Measuring Profile Sales on an Active, Leading Criminal Market for User Impersonation at Scale

Michele Campobasso and Luca Allodi, Eindhoven University of Technology

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In this paper we exploit market features proper of a leading Russian cybercrime market for user impersonation at scale to evaluate attacker preferences when purchasing stolen user profiles, and the overall economic activity of the market. We run our data collection over a period of $161$ days and collect data on a sample of $1'193$ sold user profiles out of $11'357$ advertised products in that period and their characteristics. We estimate a market trade volume of up to approximately $700$ profiles per day, corresponding to estimated daily sales of up to $4'000$ USD and an overall market revenue within the observation period between $540k$ and $715k$ USD. We find profile provision to be rather stable over time and mainly focused on European profiles, whereas actual profile acquisition varies significantly depending on other profile characteristics. Attackers' interests focus disproportionally on profiles of certain types, including those originating in North America and featuring Crypto resources. We model and evaluate the relative importance of different profile characteristics in the final decision of an attacker to purchase a profile, and discuss implications for defenses and risk evaluation.

Lalaine: Measuring and Characterizing Non-Compliance of Apple Privacy Labels

Yue Xiao, Zhengyi Li, and Yue Qin, Indiana University Bloomington; Xiaolong Bai, Orion Security Lab, Alibaba Group; Jiale Guan, Xiaojing Liao, and Luyi Xing, Indiana University Bloomington

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As a key supplement to privacy policies that are known to be lengthy and difficult to read, Apple has launched app privacy labels, which purportedly help users more easily understand an app's privacy practices. However, false and misleading privacy labels can dupe privacy-conscious consumers into downloading data-intensive apps, ultimately eroding the credibility and integrity of the labels. Although Apple releases requirements and guidelines for app developers to create privacy labels, little is known about whether and to what extent the privacy labels in the wild are correct and compliant, reflecting the actual data practices of iOS apps.

This paper presents the first systematic study, based on our new methodology named Lalaine, to evaluate data-flow to privacy-label flow-to-label consistency. Lalaine fully analyzed the privacy labels and binaries of 5,102 iOS apps, shedding lights on the prevalence and seriousness of privacy-label non-compliance. We provide detailed case studies and analyze root causes for privacy label non-compliance that complements prior understandings. This has led to new insights for improving privacy-label design and compliance requirements, so app developers, platform stakeholders, and policy-makers can better achieve their privacy and accountability goals. Lalaine is thoroughly evaluated for its high effectiveness and efficiency. We are responsibly reporting the results to stakeholders.

Lessons Lost: Incident Response in the Age of Cyber Insurance and Breach Attorneys

Daniel W. Woods, University of Edinburgh; Rainer Böhme, University of Innsbruck; Josephine Wolff, Tufts University; Daniel Schwarcz, University of Minnesota

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Incident Response (IR) allows victim firms to detect, contain, and recover from security incidents. It should also help the wider community avoid similar attacks in the future. In pursuit of these goals, technical practitioners are increasingly influenced by stakeholders like cyber insurers and lawyers. This paper explores these impacts via a multi-stage, mixed methods research design that involved 69 expert interviews, data on commercial relationships, and an online validation workshop. The first stage of our study established 11 stylized facts that describe how cyber insurance sends work to a small numbers of IR firms, drives down the fee paid, and appoints lawyers to direct technical investigators. The second stage showed that lawyers when directing incident response often: introduce legalistic contractual and communication steps that slow-down incident response; advise IR practitioners not to write down remediation steps or to produce formal reports; and restrict access to any documents produced.

Log: It’s Big, It’s Heavy, It’s Filled with Personal Data! Measuring the Logging of Sensitive Information in the Android Ecosystem

Allan Lyons, University of Calgary; Julien Gamba, IMDEA Networks Institute and Universidad Carlos III de Madrid; Austin Shawaga, University of Calgary; Joel Reardon, University of Calgary and AppCensus, Inc.; Juan Tapiador, Universidad Carlos III de Madrid; Serge Egelman, ICSI and UC Berkeley and AppCensus, Inc.; Narseo Vallina-Rodriguez, IMDEA Networks Institute and AppCensus, Inc.

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Android offers a shared system that multiplexes all logged data from all system components, including both the operating system and the console output of apps that run on it. A security mechanism ensures that user-space apps can only read the log entries that they create, though many "privileged" apps are exempt from this restriction. This includes preloaded system apps provided by Google, the phone manufacturer, the cellular carrier, as well as those sharing the same signature. Consequently, Google advises developers to not log sensitive information to the system log.

In this work, we examined the logging of sensitive data in the Android ecosystem. Using a field study, we show that most devices log some amount of user-identifying information. We show that the logging of "activity" names can inadvertently reveal information about users through their app usage. We also tested whether different smartphones log personal identifiers by default, examined preinstalled apps that access the system logs, and analyzed the privacy policies of manufacturers that report collecting system logs.

Long Live The Honey Badger: Robust Asynchronous DPSS and its Applications

Thomas Yurek, University of Illinois at Urbana-Champaign, NTT Research, and IC3; Zhuolun Xiang, Aptos; Yu Xia, MIT CSAIL and NTT Research; Andrew Miller, University of Illinois at Urbana-Champaign and IC3

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Secret sharing is an essential tool for many distributed applications, including distributed key generation and multiparty computation. For many practical applications, we would like to tolerate network churn, meaning participants can dynamically enter and leave the pool of protocol participants as they please. Such protocols, called Dynamic-committee Proactive Secret Sharing (DPSS) have recently been studied; however, existing DPSS protocols do not gracefully handle faults: the presence of even one unexpectedly slow node can often slow down the whole protocol by a factor of O(n).

In this work, we explore optimally fault-tolerant asynchronous DPSS that is not slowed down by crash faults and even handles byzantine faults while maintaining the same performance. We first introduce the first high-threshold DPSS, which offers favorable characteristics relative to prior non-synchronous works in the presence of faults while simultaneously supporting higher privacy thresholds. We then batch-amortize this scheme along with a parallel non-high-threshold scheme which achieves optimal bandwidth characteristics. We implement our schemes and demonstrate that they can compete with prior work in best-case performance while outperforming it in non-optimal settings.

Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants

Gustavo Sandoval, Hammond Pearce, Teo Nys, Ramesh Karri, Siddharth Garg, and Brendan Dolan-Gavitt, New York University

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Large Language Models (LLMs) such as OpenAI Codex are increasingly being used as AI-based coding assistants. Understanding the impact of these tools on developers’ code is paramount, especially as recent work showed that LLMs may suggest cybersecurity vulnerabilities. We conduct a security-driven user study (N=58) to assess code written by student programmers when assisted by LLMs. Given the potential severity of low-level bugs as well as their relative frequency in real-world projects, we tasked participants with implementing a singly-linked ‘shopping list’ structure in C. Our results indicate that the security impact in this setting (low-level C with pointer and array manipulations) is small: AI-assisted users produce critical security bugs at a rate no greater than 10% more than the control, indicating the use of LLMs does not introduce new security risks.

Machine-checking Multi-Round Proofs of Shuffle: Terelius-Wikstrom and Bayer-Groth

Thomas Haines, Australian National University; Rajeev Gore, Polish Academy of Science; Mukesh Tiwari, University of Cambridge

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Shuffles are used in electronic voting in much the same way physical ballot boxes are used in paper systems: (encrypted) ballots are input into the shuffle and (encrypted) ballots are output in a random order, thereby breaking the link between voter identities and ballots. To guarantee that no ballots are added, omitted or altered, zero-knowledge proofs, called proofs of shuffle, are used to provide publicly verifiable transcripts that prove that the outputs are a re-encrypted permutation of the inputs. The most prominent proofs of shuffle, in practice, are those due to Terelius and Wikstrom~(TW), and Bayer and Groth (BG). TW is simpler whereas BG is more efficient, both in terms of bandwidth and computation. Security for the simpler (TW) proof of shuffle has already been machine-checked but several prominent vendors insist on using the more complicated BG proof of shuffle. Here, we machine-check the security of the Bayer-Groth proof of shuffle via the Coq proof-assistant. We then extract the verifier (software) required to check the transcripts produced by Bayer-Groth implementations and use it to check transcripts from the Swiss Post evoting system under development for national elections in Switzerland.

Measuring Up to (Reasonable) Consumer Expectations: Providing an Empirical Basis for Holding IoT Manufacturers Legally Responsible

Lorenz Kustosch and Carlos Gañán, TU Delft; Mattis van 't Schip, Radboud University; Michel van Eeten and Simon Parkin, TU Delft

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With continued cases of security and privacy incidents with consumer Internet-of-Things (IoT) devices comes the need to identify which actors are in the best place to respond. Previous literature studied expectations of consumers regarding how security and privacy should be implemented and who should take on preventive efforts. But how do such normative consumer expectations differ from what is actually realistic, or reasonable to expect how security and privacy-related events will be handled? Using a vignette survey with 862 participants, we studied consumer expectations on how IoT manufacturers and users would and should respond when confronted with a potentially infected or privacy-invading IoT device. We find that expectations differ considerably between what is realistic and what is appropriate. Furthermore, security and privacy lead to different expectations around users’ and manufacturers’ actions, with a general diffusion of expectations on how to handle privacy-related events. We offer recommendations to IoT manufacturers and regulators on how to support users in addressing security and privacy issues.

Meta-Sift: How to Sift Out a Clean Subset in the Presence of Data Poisoning?

Yi Zeng, Virginia Tech and SONY AI; Minzhou Pan, Himanshu Jahagirdar, and Ming Jin, Virginia Tech; Lingjuan Lyu, SONY AI; Ruoxi Jia, Virginia Tech

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External data sources are increasingly being used to train machine learning (ML) models as the data demand increases. However, the integration of external data into training poses data poisoning risks, where malicious providers manipulate their data to compromise the utility or integrity of the model. Most data poisoning defenses assume access to a set of clean data (referred to as the base set), which could be obtained through trusted sources. But it also becomes common that entire data sources for an ML task are untrusted (e.g., Internet data). In this case, one needs to identify a subset within a contaminated dataset as the base set to support these defenses.

This paper starts by examining the performance of defenses when poisoned samples are mistakenly mixed into the base set. We analyze five representative defenses that use base sets and find that their performance deteriorates dramatically with less than 1% poisoned points in the base set. These findings suggest that sifting out a base set with \emph{high precision} is key to these defenses' performance. Motivated by these observations, we study how precise existing automated tools and human inspection are at identifying clean data in the presence of data poisoning. Unfortunately, neither effort achieves the precision needed that enables effective defenses. Worse yet, many of the outcomes of these methods are worse than random selection.

In addition to uncovering the challenge, we take a step further and propose a practical countermeasure, Meta-Sift. Our method is based on the insight that existing poisoning attacks shift data distributions, resulting in high prediction loss when training on the clean portion of a poisoned dataset and testing on the corrupted portion. Leveraging the insight, we formulate a bilevel optimization to identify clean data and further introduce a suite of techniques to improve the efficiency and precision of the identification. Our evaluation shows that Meta-Sift can sift a clean base set with 100\% precision under a wide range of poisoning threats. The selected base set is large enough to give rise to successful defense when plugged into the existing defense techniques.

MINER: A Hybrid Data-Driven Approach for REST API Fuzzing

Chenyang Lyu, Jiacheng Xu, Shouling Ji, Xuhong Zhang, and Qinying Wang, Zhejiang University; Binbin Zhao, Georgia Institute of Technology; Gaoning Pan, Zhejiang University; Wei Cao and Peng Chen, Ant Group; Raheem Beyah, Georgia Institute of Technology

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In recent years, REST API fuzzing has emerged to explore errors on a cloud service. Its performance highly depends on the sequence construction and request generation. However, existing REST API fuzzers have trouble generating long sequences with well-constructed requests to trigger hard-to-reach states in a cloud service, which limits their performance of finding deep errors and security bugs. Further, they cannot find the specific errors caused by using undefined parameters during request generation. Therefore, in this paper, we propose a novel hybrid data-driven solution, named MINER, with three new designs working together to address the above limitations. First, MINER collects the valid sequences whose requests pass the cloud service's checking as the templates, and assigns more executions to long sequence templates. Second, to improve the generation quality of requests in a sequence template, MINER creatively leverages the state-of-the-art neural network model to predict key request parameters and provide them with appropriate parameter values. Third, MINER implements a new data-driven security rule checker to capture the new kind of errors caused by undefined parameters. We evaluate MINER against the state-of-the-art fuzzer RESTler on GitLab, Bugzilla, and WordPress via 11 REST APIs. The results demonstrate that the average pass rate of MINER is 23.42% higher than RESTler. MINER finds 97.54% more unique errors than RESTler on average and 142.86% more reproducible errors after manual analysis. We have reported all the newly found errors, and 7 of them have been confirmed as logic bugs by the corresponding vendors.

Minimalist: Semi-automated Debloating of PHP Web Applications through Static Analysis

Rasoul Jahanshahi, Boston University; Babak Amin Azad and Nick Nikiforakis, Stony Brook University; Manuel Egele, Boston University

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As web applications grow more complicated and rely on third-party libraries to deliver new features to their users, they become bloated with unnecessary code. This unnecessary code increases a web application’s attack surface, which can be exploited to steal user data and compromise the underlying web server. One approach to deal with bloated code is the process of selectively removing features that users do not require – debloating.

In this paper, we identify the current challenges with debloating web applications and propose a semi-automated static debloating scheme. We implement a prototype of our proposed method, called Minimalist that generates a call-graph for a given PHP web application. Minimalist performs a reachability analysis for the features users require and removes unreachable functions in the analyzed web application. Compared to prior work, Minimalist debloats web applications without relying on heavy runtime instrumentation. Furthermore, the call-graph generated by Minimalist can be reused (in combination with web server logs) to debloat different installations of the same web application. Due to the inherent complexity and highly dynamic nature of the PHP language, Minimalist cannot guarantee the soundness of its call-graph analysis. However, Minimalist follows a best-effort approach to model the majority of PHP features used by popular web applications, such as WordPress, phpMyAdmin, and others.

We evaluated Minimalist on 12 versions of four popular PHP web applications with 45 recent security vulnerabilities. We show that Minimalist reduces the size of web applications in our dataset on average by 18% and removes 38% of known vulnerabilities. Our results demonstrate that the principled debloating of web applications can lead to significant security gains without relying on instrumentation mechanisms that degrade the performance of the server.

MobileAtlas: Geographically Decoupled Measurements in Cellular Networks for Security and Privacy Research

Gabriel K. Gegenhuber, University of Vienna; Wilfried Mayer, SBA Research; Edgar Weippl, University of Vienna; Adrian Dabrowski, CISPA Helmholtz Center for Information Security

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Cellular networks are not merely data access networks to the Internet. Their distinct services and ability to form large complex compounds for roaming purposes make them an attractive research target in their own right. Their promise of providing a consistent service with comparable privacy and security across roaming partners falls apart at close inspection.

Thus, there is a need for controlled testbeds and measurement tools for cellular access networks doing justice to the technology's unique structure and global scope. Particularly, such measurements suffer from a combinatorial explosion of operators, mobile plans, and services. To cope with these challenges, we built a framework that geographically decouples the SIM from the cellular modem by selectively connecting both remotely. This allows testing any subscriber with any operator at any modem location within minutes without moving parts. The resulting GSM/UMTS/LTE measurement and testbed platform offers a controlled experimentation environment, which is scalable and cost-effective. The platform is extensible and fully open-sourced, allowing other researchers to contribute locations, SIM cards, and measurement scripts.

Using the above framework, our international experiments in commercial networks revealed exploitable inconsistencies in traffic metering, leading to multiple phreaking opportunities, i.e., fare-dodging. We also expose problematic IPv6 firewall configurations, hidden SIM card communication to the home network, and fingerprint dial progress tones to track victims across different roaming networks and countries with voice calls.

MorFuzz: Fuzzing Processor via Runtime Instruction Morphing enhanced Synchronizable Co-simulation

Jinyan Xu and Yiyuan Liu, Zhejiang University; Sirui He, City University of Hong Kong; Haoran Lin and Yajin Zhou, Zhejiang University; Cong Wang, City University of Hong Kong

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Modern processors are too complex to be bug free. Recently, a few hardware fuzzing techniques have shown promising results in verifying processor designs. However, due to the complexity of processors, they suffer from complex input grammar, deceptive mutation guidance, and model implementation differences. Therefore, how to effectively and efficiently verify processors is still an open problem.

This paper proposes MorFuzz, a novel processor fuzzer that can efficiently discover software triggerable hardware bugs. The core idea behind MorFuzz is to use runtime information to generate instruction streams with valid formats and meaningful semantics. MorFuzz designs a new input structure to provide multi-level runtime mutation primitives and proposes the instruction morphing technique to mutate instruction dynamically. Besides, we also extend the co-simulation framework to various microarchitectures and develop the state synchronization technique to eliminate implementation differences. We evaluate MorFuzz on three popular open-source RISC-V processors: CVA6, Rocket, BOOM, and discover 17 new bugs (with 13 CVEs assigned). Our evaluation shows MorFuzz achieves 4.4× and 1.6× more state coverage than the state-of-the-art fuzzer, DifuzzRTL, and the famous constrained instruction generator, riscv-dv.

MTSan: A Feasible and Practical Memory Sanitizer for Fuzzing COTS Binaries

Xingman Chen, Tsinghua University; Yinghao Shi, Institute of Information Engineering, Chinese Academy of Sciences; Zheyu Jiang and Yuan Li, Tsinghua University; Ruoyu Wang, Arizona State University; Haixin Duan, Tsinghua University and Zhongguancun Laboratory; Haoyu Wang, Huazhong University of Science and Technology; Chao Zhang, Tsinghua University and Zhongguancun Laboratory

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Fuzzing has been widely adopted for finding vulnerabilities in programs, especially when source code is not available. But the effectiveness and efficiency of binary fuzzing are curtailed by the lack of memory sanitizers for binaries. This lack of binary sanitizers is due to the information loss in compiling programs and challenges in binary instrumentation.

In this paper, we present a feasible and practical hardware-assisted memory sanitizer, MTSan, for binary fuzzing. MTSan can detect both spatial and temporal memory safety violations at runtime. It adopts a novel progressive object recovery scheme to recover objects in binaries and uses a customized binary rewriting solution to instrument binaries with the memory-tagging-based memory safety sanitizing policy. Further, MTSan uses a hardware feature, ARM Memory Tagging Extension (MTE) to significantly reduce its runtime overhead. We implemented a prototype of MTSan on AArch64 and systematically evaluated its effectiveness and performance. Our evaluation results show that MTSan could detect more memory safety violations than existing binary sanitizers whiling introducing much lower runtime and memory overhead.

Multi-Factor Key Derivation Function (MFKDF) for Fast, Flexible, Secure, & Practical Key Management

Vivek Nair and Dawn Song, University of California, Berkeley

Distinguished Artifact Award Winner

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We present the first general construction of a Multi-Factor Key Derivation Function (MFKDF). Our function expands upon password-based key derivation functions (PBKDFs) with support for using other popular authentication factors like TOTP, HOTP, and hardware tokens in the key derivation process. In doing so, it provides an exponential security improvement over PBKDFs with less than 12 ms of additional computational overhead in a typical web browser. We further present a threshold MFKDF construction, allowing for client-side key recovery and reconstitution if a factor is lost. Finally, by "stacking" derived keys, we provide a means of cryptographically enforcing arbitrarily specific key derivation policies. The result is a paradigm shift toward direct cryptographic protection of user data using all available authentication factors, with no noticeable change to the user experience. We demonstrate the ability of our solution to not only significantly improve the security of existing systems implementing PBKDFs, but also to enable new applications where PBKDFs would not be considered a feasible approach.

Multiview: Finding Blind Spots in Access-Deny Issues Diagnosis

Bingyu Shen, Tianyi Shan, and Yuanyuan Zhou, University of California, San Diego

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Access-deny issues are hard to fix because it implies both availability and security requirements. On one hand, system administrators (sysadmins) need to make a change quickly to enable legitimate access. On the other hand, sysadmins need to make sure the change does not allow excessive access. Fulfilling the second requirement on security is especially challenging because it highly requires the sysadmins’ knowledge of the system environments and security context. Blind spots in knowledge and system settings may hinder sysadmins from finding the solutions that align with the security context. Insecure fixes can over-grant permissions, which may only get noticed after the security vulnerability gets exploited.

This paper aims to help sysadmins reduce blind spots in diagnosis by providing multiple directions to resolve access-deny issues. We propose a system, called Multiview, that automatically mutates the configurations to explore possible directions to fix the access-deny issue and lets the configuration changes on each direction grant as few permissions as possible. Multiview provides a detailed diagnosis report, including access-control configurations that are related to the denial, possible configuration changes on different directions to allow the request, as well as the impact on the access-control state of the entire system.

We conducted a user study to evaluate Multiview with 20 participants on five real-world access-deny issues. Multiview can reduce the percentage of insecure fixes from 44.0% to 2.0% and reduce the diagnosis time by 62.0% on average. We also evaluated Multiview on 112 real-world failure cases from eight different systems and server applications, and it can successfully diagnose 89 of them. Multiview accurately identifies the failure-causing configurations and provides possible directions to each access-deny issue within one minute.

NAUTILUS: Automated RESTful API Vulnerability Detection

Gelei Deng, Nanyang Technological University; Zhiyi Zhang, CodeSafe Team, Qi An Xin Group Corp.; Yuekang Li, Yi Liu, Tianwei Zhang, and Yang Liu, Nanyang Technological University; Guo Yu, China Industrial Control Systems Cyber Emergency Response Team; Dongjin Wang, Institute of Scientific and Technical Information, China Academy of Railway Sciences

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RESTful APIs have become arguably the most prevalent endpoint for accessing web services. Blackbox vulnerability scanners are a popular choice for detecting vulnerabilities in web services automatically. Unfortunately, they suffer from a number of limitations in RESTful API testing. Particularly, existing tools cannot effectively obtain the relations between API operations, and they lack the awareness of the correct sequence of API operations during testing. These drawbacks hinder the tools from requesting the API operations properly to detect potential vulnerabilities.

To address this challenge, we propose NAUTILUS, which includes a novel specification annotation strategy to uncover RESTful API vulnerabilities. The annotations encode the proper operation relations and parameter generation strategies for the RESTful service, which assist NAUTILUS to generate meaningful operation sequences and thus uncover vulnerabilities that require the execution of multiple API operations in the correct sequence. We experimentally compare NAUTILUS with four state-of-art vulnerability scanners and RESTful API testing tools on six RESTful services. Evaluation results demonstrate that NAUTILUS can successfully detect an average of 141% more vulnerabilities, and cover 104% more API operations. We also apply NAUTILUS to nine real-world RESTful services, and detected 23 unique 0-day vulnerabilities with 12 CVE numbers, including one remote code execution vulnerability in Atlassian Confluence, and three high-risk vulnerabilities in Microsoft Azure, which can affect millions of users.

Near-Ultrasound Inaudible Trojan (Nuit): Exploiting Your Speaker to Attack Your Microphone

Qi Xia and Qian Chen, University of Texas at San Antonio; Shouhuai Xu, University of Colorado Colorado Springs

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Voice Control Systems (VCSs) offer a convenient interface for issuing voice commands to smart devices. However, VCS security has yet to be adequately understood and addressed as evidenced by the presence of two classes of attacks: (i) inaudible attacks, which can be waged when the attacker and the victim are in proximity to each other; and (ii) audible attacks, which can be waged remotely by embedding attack signals into audios. In this paper, we introduce a new class of attacks, dubbed near-ultrasound inaudible trojan (Nuit). Nuit attacks achieve the best of the two classes of attacks mentioned above: they are inaudible and can be waged remotely. Moreover, Nuit attacks can achieve end-to-end unnoticeability, which is important but has not been paid due attention in the literature. Another feature of Nuit attacks is that they exploit victim speakers to attack victim microphones and their associated VCSs, meaning the attacker does not need to use any special speaker. We demonstrate the feasibility of Nuit attacks and propose an effective defense against them.

Network Responses to Russia's Invasion of Ukraine in 2022: A Cautionary Tale for Internet Freedom

Reethika Ramesh, Ram Sundara Raman, and Apurva Virkud, University of Michigan; Alexandra Dirksen, TU Braunschweig; Armin Huremagic, University of Michigan; David Fifield, unaffiliated; Dirk Rodenburg and Rod Hynes, Psiphon; Doug Madory, Kentik; Roya Ensafi, University of Michigan

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Russia's invasion of Ukraine in February 2022 was followed by sanctions and restrictions: by Russia against its citizens, by Russia against the world, and by foreign actors against Russia. Reports suggested a torrent of increased censorship, geoblocking, and network events affecting Internet freedom.

This paper is an investigation into the network changes that occurred in the weeks following this escalation of hostilities. It is the result of a rapid mobilization of researchers and activists, examining the problem from multiple perspectives. We develop GeoInspector, and conduct measurements to identify different types of geoblocking, and synthesize data from nine independent data sources to understand and describe various network changes. Immediately after the invasion, more than 45% of Russian government domains tested blocked access from countries other than Russia and Kazakhstan; conversely, 444 foreign websites, including news and educational domains, geoblocked Russian users. We find significant increases in Russian censorship, especially of news and social media. We find evidence of the use of BGP withdrawals to implement restrictions, and we quantify the use of a new domestic certificate authority. Finally, we analyze data from circumvention tools, and investigate their usage and blocking. We hope that our findings showing the rapidly shifting landscape of Internet splintering serves as a cautionary tale, and encourages research and efforts to protect Internet freedom.

No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning

Thorsten Eisenhofer, Ruhr University Bochum; Erwin Quiring, Ruhr University Bochum and International Computer Science Institute (ICSI) Berkeley; Jonas Möller, Technische Universität Berlin; Doreen Riepel, Ruhr University Bochum; Thorsten Holz, CISPA Helmholtz Center for Information Security; Konrad Rieck, Technische Universität Berlin

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The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments are increasingly used during the reviewing process. These systems use statistical topic models to characterize the content of submissions and automate the assignment to reviewers. In this paper, we show that this automation can be manipulated using adversarial learning. We propose an attack that adapts a given paper so that it misleads the assignment and selects its own reviewers. Our attack is based on a novel optimization strategy that alternates between the feature space and problem space to realize unobtrusive changes to the paper. To evaluate the feasibility of our attack, we simulate the paper-reviewer assignment of an actual security conference (IEEE S&P) with 165 reviewers on the program committee. Our results show that we can successfully select and remove reviewers without access to the assignment system. Moreover, we demonstrate that the manipulated papers remain plausible and are often indistinguishable from benign submissions.

No Single Silver Bullet: Measuring the Accuracy of Password Strength Meters

Ding Wang, Xuan Shan, and Qiying Dong, Nankai University; Yaosheng Shen, Peking University; Chunfu Jia, Nankai University

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To help users create stronger passwords, nearly every respectable web service adopts a password strength meter (PSM) to provide real-time strength feedback upon user registration and password change. Recent research has found that PSMs that provide accurate feedback can indeed effectively nudge users toward choosing stronger passwords. Thus, it is imperative to systematically evaluate existing PSMs to facilitate the selection of accurate ones. In this paper, we highlight that there is no single silver bullet metric for measuring the accuracy of PSMs: For each given guessing scenario and strategy, a specific metric is necessary. We investigate the intrinsic characteristics of online and offline guessing scenarios, and for the first time, propose a systematic evaluation framework that is composed of four different dimensioned criteria to rate PSM accuracy under these two password guessing scenarios (as well as various guessing strategies).

More specifically, for online guessing, the strength misjudgments of passwords with different popularity would have varied effects on PSM accuracy, and we suggest the weighted Spearman metric and consider two typical attackers: The general attacker who is unaware of the target password distribution, and the knowledgeable attacker aware of it. For offline guessing, since the cracked passwords are generally weaker than the uncracked ones, and they correspond to two disparate distributions, we adopt the Kullback-Leibler divergence metric and investigate the four most typical guessing strategies: brute-force, dictionary-based, probability-based, and a combination of above three strategies. In particular, we propose the Precision metric to measure PSM accuracy when non-binned strength feedback (e.g., probability) is transformed into easy-to-understand bins/scores (e.g., [weak, medium, strong]). We further introduce a reconciled Precision metric to characterize the impacts of strength misjudgments in different directions (e.g., weak→strong and strong→weak) on PSM accuracy. The effectiveness and practicality of our evaluation framework are demonstrated by rating 12 leading PSMs, leveraging 14 real-world password datasets. Finally, we provide three recommendations to help improve the accuracy of PSMs.

NRDelegationAttack: Complexity DDoS attack on DNS Recursive Resolvers

Yehuda Afek and Anat Bremler-Barr, Tel-Aviv University; Shani Stajnrod, Reichman University

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Malicious actors carrying out distributed denial-of-service (DDoS) attacks are interested in requests that consume a large amount of resources and provide them with ammunition. We present a severe complexity attack on DNS resolvers, where a single malicious query to a DNS resolver can significantly increase its CPU load. Even a few such concurrent queries can result in resource exhaustion and lead to a denial of its service to legitimate clients. This attack is unlike most recent DDoS attacks on DNS servers, which use communication amplification attacks where a single query generates a large number of message exchanges between DNS servers.

The attack described here involves a malicious client whose request to a target resolver is sent to a collaborating malicious authoritative server; this server, in turn, generates a carefully crafted referral response back to the (victim) resolver. The chain reaction of requests continues, leading to the delegation of queries. These ultimately direct the resolver to a server that does not respond to DNS queries. The exchange generates a long sequence of cache and memory accesses that dramatically increase the CPU load on the target resolver. Hence the name non-responsive delegation attack, or NRDelegationAttack.

We demonstrate that three major resolver implementations, BIND9, Unbound, and Knot, are affected by the NRDelegationAttack, and carry out a detailed analysis of the amplification factor on a BIND9 based resolver. As a result of this work, three common vulnerabilities and exposures (CVEs) regarding NRDelegationAttack were issued by these resolver implementations. We also carried out minimal testing on 16 open resolvers, confirming that the attack affects them as well.

Downgrading DNSSEC: How to Exploit Crypto Agility for Hijacking Signed Zones

Elias Heftrig, ATHENE and Fraunhofer SIT; Haya Shulman, ATHENE, Fraunhofer SIT, and Goethe-Universität Frankfurt; Michael Waidner, ATHENE, Fraunhofer SIT, and Technische Universität Darmstadt

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Cryptographic algorithm agility is an important property for DNSSEC: it allows easy deployment of new algorithms if the existing ones are no longer secure. Significant operational and research efforts are dedicated to pushing the deployment of new algorithms in DNSSEC forward. Recent research shows that DNSSEC is gradually achieving algorithm agility: most DNSSEC supporting resolvers can validate a number of different algorithms and domains are increasingly signed with cryptographically strong ciphers.

In this work we show for the first time that the cryptographic agility in DNSSEC, although critical for making DNS secure with strong cryptography, also introduces a severe vulnerability. We find that under certain conditions, when new, unsupported algorithms are listed in signed DNS responses, the resolvers do not validate DNSSEC. As a result, domains that deploy new ciphers, risk exposing the validating resolvers to cache poisoning attacks. We use this to develop DNSSECdowngrade attacks and experimentally and ethically evaluate our attacks against popular DNS resolver implementations, public DNS providers, and DNS resolvers used by web clients.

We validate the success of DNSSEC-downgrade attacks by poisoning the resolvers: we inject fake records, in signed domains, into the caches of validating resolvers. Our evaluations showed that during 2021 major DNS providers, such as Google Public DNS and Cloudflare, as well as 35% of DNS resolvers used by the web clients were vulnerable to our attacks. After coordinated disclosure with the affected operators, that number reduced to 5.03% in 2022.

We trace the factors that led to this situation and provide recommendations.

One Size Does not Fit All: Quantifying the Risk of Malicious App Encounters for Different Android User Profiles

Savino Dambra, Leyla Bilge, and Platon Kotzias, Norton Research Group; Yun Shen, NetApp; Juan Caballero, IMDEA Software Institute

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Previous work has investigated the particularities of security practices within specific user communities defined based on country of origin, age, prior tech abuse, and economic status. Their results highlight that current security solutions that adopt a one-size-fits-all-users approach ignore the differences and needs of particular user communities. However, those works focus on a single community or cluster users into hard-to-interpret sub-populations. In this work, we perform a large-scale quantitative analysis of the risk of encountering malware and other potentially unwanted applications (PUA) across user communities. At the core of our study is a dataset of app installation logs collected from 12M Android mobile devices. Leveraging user-installed apps, we define intuitive profiles based on users’ interests (e.g., gamers and investors), and fit a subset of 5.4M devices to those profiles. Our analysis is structured in three parts. First, we perform risk analysis on the whole population to measure how the risk of malicious app encounters is affected by different factors. Next, we create different profiles to investigate whether risk differences across users may be due to their interests. Finally, we compare a per-profile approach for classifying clean and infected devices with the classical approach that considers the whole population. We observe that features such as the diversity of the app signers and the use of alternative markets highly correlate with the risk of malicious app encounters. We also discover that some profiles such as gamers and social-media users are exposed to more than twice the risks experienced by the average users. We also show that the classification outcome has a marked accuracy improvement when using a per-profile approach to train the prediction models. Overall, our results confirm the inadequacy of one-size-fits-all protection solutions.

Oops..! I Glitched It Again! How to Multi-Glitch the Glitching-Protections on ARM TrustZone-M

Xhani Marvin Saß, Richard Mitev, and Ahmad-Reza Sadeghi, Technical University of Darmstadt

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Voltage Fault Injection (VFI), also known as power glitching, has proven to be a severe threat to real-world systems. In VFI attacks, the adversary disturbs the power-supply of the target-device forcing the device to illegitimate behavior. Various countermeasures have been proposed to address different types of fault injection attacks at different abstraction layers, either requiring to modify the underlying hardware or software/firmware at the machine instruction level. Moreover, only recently, individual chip manufacturers have started to respond to this threat by integrating countermeasures in their products. Generally, these countermeasures aim at protecting against single fault injection (SFI) attacks, since Multiple Fault Injection (MFI) is believed to be challenging and sometimes even impractical.

In this paper, we present μ-Glitch, the first Voltage Fault Injection (VFI) platform which is capable of injecting multiple, coordinated voltage faults into a target device, requiring only a single trigger signal. We provide a novel flow for Multiple Voltage Fault Injection (MVFI) attacks to significantly reduce the search complexity for fault parameters, as the search space increases exponentially with each additional fault injection. We evaluate and showcase the effectiveness and practicality of our attack platform on four real-world chips, featuring TrustZone-M:

The first two have interdependent backchecking mechanisms, while the second two have additionally integrated countermeasures against fault injection. Our evaluation revealed that μ-Glitch can successfully inject four consecutive faults within an average time of one day. Finally, we discuss potential countermeasures to mitigate VFI attacks and additionally propose two novel attack scenarios for MVFI.

Panda: Security Analysis of Algorand Smart Contracts

Zhiyuan Sun, The Hong Kong Polytechnic University and Southern University of Science and Technology; Xiapu Luo, The Hong Kong Polytechnic University; Yinqian Zhang, Southern University of Science and Technology

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Algorand has recently grown rapidly as a representative of the new generation of pure-proof-of-stake (PPoS) blockchains. At the same time, Algorand has also attracted more and more users to use it as a trading platform for non-fungible tokens. However, similar to traditional programs, the incorrect way of programming will lead to critical security vulnerabilities in Algorand smart contracts. In this paper, we first analyze the semantics of Algorand smart contracts and find 9 types of generic vulnerabilities. Next, we propose Panda, the first extensible static analysis framework that can automatically detect such vulnerabilities in Algorand smart contracts, and formally define the vulnerability detection rules. We also construct the first benchmark dataset to evaluate Panda. Finally, we used Panda to conduct a vulnerability assessment on all smart contracts on the Algorand blockchain and found 80,515 (10.38%) vulnerable smart signatures and 150,676 (27.73%) vulnerable applications. Of the vulnerable applications, 4,008 (4.04%) are still on the blockchain and have not been deleted. In the disclosure process, the vulnerabilities found by Panda have been acknowledged by many projects, including some critical blockchain infrastructures such as the decentralized exchange and the NFT auction platform.

Pass2Edit: A Multi-Step Generative Model for Guessing Edited Passwords

Ding Wang and Yunkai Zou, Nankai University; Yuan-An Xiao, Peking University; Siqi Ma, The University of New South Wales; Xiaofeng Chen, Xidian University

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While password stuffing attacks (that exploit the direct password reuse behavior) have gained considerable attention, only a few studies have examined password tweaking attacks, where an attacker exploits users' indirect reuse behaviors (with edit operations like insertion, deletion, and substitution). For the first time, we model the password tweaking attack as a multi-class classification problem for characterizing users' password edit/modification processes, and propose a generative model coupled with the multi-step decision-making mechanism, called Pass2Edit, to accurately characterize users' password reuse/modification behaviors.

We demonstrate the effectiveness of Pass2Edit through extensive experiments, which consist of 12 practical attack scenarios and employ 4.8 billion real-world passwords. The experimental results show that Pass2Edit and its variant significantly improve over the prior art. More specifically, when the victim's password at site A (namely pwA) is known, within 100 guesses, the cracking success rate of Pass2Edit in guessing her password at site B (pwBpwA) is 24.2% (for common users) and 11.7% (for security-savvy users), respectively, which is 18.2%-33.0% higher than its foremost counterparts. Our results highlight that password tweaking is a much more damaging threat to password security than expected.

Password Guessing Using Random Forest

Ding Wang and Yunkai Zou, Nankai University; Zijian Zhang, Peking University; Kedong Xiu, Nankai University

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Passwords are the most widely used authentication method, and guessing attacks are the most effective method for password strength evaluation. However, existing password guessing models are generally built on traditional statistics or deep learning, and there has been no research on password guessing that employs classical machine learning.

To fill this gap, this paper provides a brand new technical route for password guessing. More specifically, we re-encode the password characters and make it possible for a series of classical machine learning techniques that tackle multi-class classification problems (such as random forest, boosting algorithms and their variants) to be used for password guessing. Further, we propose RFGuess, a random-forest based framework that characterizes the three most representative password guessing scenarios (i.e., trawling guessing, targeted guessing based on personally identifiable information (PII) and on users' password reuse behaviors).

Besides its theoretical significance, this work is also of practical value. Experiments using 13 large real-world password datasets demonstrate that our random-forest based guessing models are effective: (1) RFGuess for trawling guessing scenarios, whose guessing success rates are comparable to its foremost counterparts; (2) RFGuess-PII for targeted guessing based on PII, which guesses 20%~28% of common users within 100 guesses, outperforming its foremost counterpart by 7%~13%; (3) RFGuess-Reuse for targeted guessing based on users' password reuse/modification behaviors, which performs the best or 2nd best among related models. We believe this work makes a substantial step toward introducing classical machine learning techniques into password guessing.

PELICAN: Exploiting Backdoors of Naturally Trained Deep Learning Models In Binary Code Analysis

Zhuo Zhang, Guanhong Tao, Guangyu Shen, Shengwei An, Qiuling Xu, Yingqi Liu, and Yapeng Ye, Purdue University; Yaoxuan Wu, University of California, Los Angeles; Xiangyu Zhang, Purdue University

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Deep Learning (DL) models are increasingly used in many cyber-security applications and achieve superior performance compared to traditional solutions. In this paper, we study backdoor vulnerabilities in naturally trained models used in binary analysis. These backdoors are not injected by attackers but rather products of defects in datasets and/or training processes. The attacker can exploit these vulnerabilities by injecting some small fixed input pattern (e.g., an instruction) called backdoor trigger to their input (e.g., a binary code snippet for a malware detection DL model) such that misclassification can be induced (e.g., the malware evades the detection). We focus on transformer models used in binary analysis. Given a model, we leverage a trigger inversion technique particularly designed for these models to derive trigger instructions that can induce misclassification. During attack, we utilize a novel trigger injection technique to insert the trigger instruction(s) to the input binary code snippet. The injection makes sure that the code snippets' original program semantics are preserved and the trigger becomes an integral part of such semantics and hence cannot be easily eliminated. We evaluate our prototype PELICAN on 5 binary analysis tasks and 15 models. The results show that PELICAN can effectively induce misclassification on all the evaluated models in both white-box and black-box scenarios. Our case studies demonstrate that PELICAN can exploit the backdoor vulnerabilities of two closed-source commercial tools.

Place Your Locks Well: Understanding and Detecting Lock Misuse Bugs

Yuandao Cai, Peisen Yao, Chengfeng Ye, and Charles Zhang, The Hong Kong University of Science and Technology

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Modern multi-threaded software systems commonly leverage locks to prevent concurrency bugs. Nevertheless, due to the complexity of writing the correct concurrent code, using locks itself is often error-prone. In this work, we investigate a general variety of lock misuses. Our characteristic study of existing CVE IDs reveals that lock misuses can inflict concurrency errors and even severe security issues, such as denial-of-service and memory corruption. To alleviate the threats, we present a practical static analysis framework, namely Lockpick, which consists of two core stages to effectively detect misused locks. More specifically, Lockpick first conducts path-sensitive typestate analysis, tracking lock-state transitions and interactions to identify sequential typestate violations. Guided by the preceding results, Lockpick then performs concurrency-aware detection to pinpoint various lock misuse errors, effectively reasoning about the thread interleavings of interest. The results are encouraging—we have used Lockpick to uncover 203 unique and confirmed lock misuses across a broad spectrum of impactful open-source systems, such as OpenSSL, the Linux kernel, PostgreSQL, MariaDB, FFmpeg, Apache HTTPd, and FreeBSD. Three exciting results are that those confirmed lock misuses are long-latent, hiding for 7.4 years on average; in total, 16 CVE IDs have been assigned for the severe errors uncovered; and Lockpick can flag many real bugs missed by the previous tools with significantly fewer false positives.

Pool-Party: Exploiting Browser Resource Pools for Web Tracking

Peter Snyder, Brave Software; Soroush Karami, University of Illinois at Chicago; Arthur Edelstein, Brave Software; Benjamin Livshits, Imperial College London; Hamed Haddadi, Brave Software and Imperial College of London

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We identify class of covert channels in browsers that are not mitigated by current defenses, which we call “pool-party” attacks. Pool-party attacks allow sites to create covert channels by manipulating limited-but-unpartitioned resource pools. This class of attacks have been known to exist; in this work we show that they are more prevalent, more practical for exploitation, and allow exploitation in more ways, than previously identified. These covert channels have sufficient bandwidth to pass cookies and identifiers across site boundaries under practical and real-world conditions.We identify pool-party attacks in all popular browsers, and show they are practical cross-site tracking techniques (i.e., attacks take 0.6s in Chrome and Edge, and 7s in Firefox and Tor Browser).

In this paper we make the following contributions: first, we describe pool-party covert channel attacks that exploit limits in application-layer resource pools in browsers. Second, we demonstrate that pool-party attacks are practical, and can be used to track users in all popular browsers; we also share open source implementations of the attack. Third, we show that in Gecko based-browsers (including the Tor Browser) pool-party attacks can also be used for cross-profile tracking (e.g., linking user behavior across normal and private browsing sessions). Finally, we discuss possible defenses.

Practical Asynchronous High-threshold Distributed Key Generation and Distributed Polynomial Sampling

Sourav Das, University of Illinois at Urbana-Champaign; Zhuolun Xiang, Aptos; Lefteris Kokoris-Kogias, IST Austria and Mysten Labs; Ling Ren, University of Illinois at Urbana-Champaign

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Distributed Key Generation (DKG) is a technique to bootstrap threshold cryptosystems without a trusted party. DKG is an essential building block to many decentralized protocols such as randomness beacons, threshold signatures, Byzantine consensus, and multiparty computation. While significant progress has been made recently, existing asynchronous DKG constructions are inefficient when the reconstruction threshold is larger than one-third of the total nodes. In this paper, we present a simple and concretely efficient \emph{asynchronous} DKG (ADKG) protocol among n = 3t + 1 nodes that can tolerate up to t malicious nodes and support any reconstruction threshold t. Our protocol has an expected O(κn3) communication cost, where κ is the security parameter, and only assumes the hardness of the Discrete Logarithm. The core ingredient of our ADKG protocol is an asynchronous protocol to secret share a random polynomial of degree t, which has other applications, such as asynchronous proactive secret sharing and asynchronous multiparty computation. We implement our high-threshold ADKG protocol and evaluate it using a network of up to 128 geographically distributed nodes. Our evaluation shows that our high-threshold ADKG protocol reduces the running time by 90% and bandwidth usage by 80% over the state-of-the-art.

Prime Match: A Privacy-Preserving Inventory Matching System

Antigoni Polychroniadou, J.P. Morgan; Gilad Asharov, Bar-Ilan University; Benjamin Diamond, Tucker Balch, Hans Buehler, Richard Hua, Suwen Gu, Greg Gimler, and Manuela Veloso, J.P. Morgan

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Inventory matching is a standard mechanism for trading financial stocks by which buyers and sellers can be paired. In the financial world, banks often undertake the task of finding such matches between their clients. The related stocks can be traded without adversely impacting the market price for either client. If matches between clients are found, the bank can offer the trade at advantageous rates. If no match is found, the parties have to buy or sell the stock in the public market, which introduces additional costs.

A problem with the process as it is presently conducted is that the involved parties must share their order to buy or sell a particular stock, along with the intended quantity (number of shares), to the bank. Clients worry that if this information were to “leak” somehow, then other market participants would become aware of their intentions and thus cause the price to move adversely against them before their transaction finalizes.

We provide a solution that enables clients to match their orders efficiently with reduced market impact while maintaining privacy. In the case where there are no matches, no information is revealed. Our main cryptographic innovation is a two-round secure linear comparison protocol for computing the minimum between two quantities without preprocessing and with malicious security, which can be of independent interest. We report benchmarks of our Prime Match system, which runs in production and is adopted by a large bank in the US - J.P. Morgan. Prime Match is the first secure multiparty computation solution running live in the financial world.

PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation

Yuchen Yang, Bo Hui, and Haolin Yuan, The Johns Hopkins University; Neil Gong, Duke University; Yinzhi Cao, The Johns Hopkins University

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Federated learning (FL) enables multiple clients to collaboratively train a model with the coordination of a central server. Although FL improves data privacy via keeping each client's training data locally, an attacker—e.g., an untrusted server—an still compromise the privacy of clients' local training data via various inference attacks. A de facto approach to preserving FL privacy is Differential Privacy (DP), which adds random noise during training. However, when applied to FL, DP suffers from a key limitation: it sacrifices the model accuracy substantially—which is even more severely than being applied to traditional centralized learning—to achieve a meaningful level of privacy.

In this paper, we study the accuracy degradation cause of FL+DP and then design an approach to improve the accuracy. First, we propose that such accuracy degradation is partially because DP introduces additional heterogeneity among FL clients when adding different random noise with clipping bias during local training. To the best of our knowledge, we are the first to associate DP in FL with client heterogeneity. Second, we design PrivateFL to learn accurate, differentially private models in FL with reduced heterogeneity. The key idea is to jointly learn a differentially private, personalized data transformation for each client during local training. The personalized data transformation shifts client's local data distribution to compensate the heterogeneity introduced by DP, thus improving FL model's accuracy.

In the evaluation, we combine and compare PrivateFL with eight state-of-the-art differentially private FL methods on seven benchmark datasets, including six image and one non-image datasets. Our results show that PrivateFL learns accurate FL models with a small ε, e.g., 93.3% on CIFAR-10 with 100 clients under (ε = 2, δ = 1e – 3)-DP. Moreover, PrivateFL can be combined with prior works to reduce DP-induced heterogeneity and further improve their accuracy.

PROGRAPHER: An Anomaly Detection System based on Provenance Graph Embedding

Fan Yang, The Chinese University of Hong Kong; Jiacen Xu, University of California, Irvine; Chunlin Xiong, Sangfor Technologies Inc.; Zhou Li, University of California, Irvine; Kehuan Zhang, The Chinese University of Hong Kong

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In recent years, the Advanced Persistent Threat (APT), which involves complex and malicious actions over a long period, has become one of the biggest threats against the security of the modern computing environment. As a countermeasure, data provenance is leveraged to capture the complex relations between entities in a computing system/network, and uses such information to detect sophisticated APT attacks. Though showing promise in countering APT attacks, the existing systems still cannot achieve a good balance between efficiency, accuracy, and granularity.

In this work, we design a new anomaly detection system on provenance graphs, termed PROGRAPHER. To address the problem of “dependency explosion” of provenance graphs and achieve high efficiency, PROGRAPHER extracts temporal-ordered snapshots from the ingested logs and performs detection on the snapshots. To capture the rich structural properties of a graph, whole graph embedding and sequence-based learning are applied. Finally, key indicators are extracted from the abnormal snapshots and reported to the analysts, so their workload will be greatly reduced.

We evaluate PROGRAPHER on five real-world datasets. The results show that PROGRAPHER can detect standard attacks and APT attacks with high accuracy and outperform the state-of-the-art detection systems.

ProSpeCT: Provably Secure Speculation for the Constant-Time Policy

Lesly-Ann Daniel, Marton Bognar, and Job Noorman, imec-DistriNet, KU Leuven; Sébastien Bardin, CEA, LIST, Université Paris Saclay; Tamara Rezk, INRIA, Université Côte d’Azur, Sophia Antipolis; Frank Piessens, imec-DistriNet, KU Leuven

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We propose ProSpeCT, a generic formal processor model providing provably secure speculation for the constant-time policy. For constant-time programs under a non-speculative semantics, ProSpeCT guarantees that speculative and out-of-order execution cause no microarchitectural leaks. This guarantee is achieved by tracking secrets in the processor pipeline and ensuring that they do not influence the microarchitectural state during speculative execution. Our formalization covers a broad class of speculation mechanisms, generalizing prior work. As a result, our security proof covers all known Spectre attacks, including load value injection (LVI) attacks.

In addition to the formal model, we provide a prototype hardware implementation of ProSpeCT on a RISC-V processor and show evidence of its low impact on hardware cost, performance, and required software changes. In particular, the experimental evaluation confirms our expectation that for a compliant constant-time binary, enabling ProSpeCT incurs no performance overhead.

PROVIDENCE: a Flexible Round-by-Round Risk-Limiting Audit

Oliver Broadrick and Poorvi Vora, The George Washington University; Filip Zagórski, University of Wroclaw and Votifica

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A Risk-Limiting Audit (RLA) is a statistical election tabulation audit with a rigorous error guarantee. We present ballot polling RLA PROVIDENCE, an audit with the efficiency of MINERVA and flexibility of BRAVO, and prove that it is risk-limiting in the presence of an adversary who can choose subsequent round sizes given knowledge of previous samples. We describe a measure of audit workload as a function of the number of rounds, precincts touched, and ballots drawn and quantify the problem of obtaining a misleading audit sample when rounds are too small, demonstrating the importance of the resulting constraint on audit planning. We describe an approach to planning audit round schedules using these measures and present simulation results demonstrating the superiority of PROVIDENCE.

We describe the use of PROVIDENCE by the Rhode Island Board of Elections in a tabulation audit of the 2021 election. Our implementation of PROVIDENCE in the open source R2B2 library has been integrated as an option in Arlo, the most commonly used RLA software.

Proxy Hunting: Understanding and Characterizing Proxy-based Upgradeable Smart Contracts in Blockchains

William E Bodell III, Sajad Meisami, and Yue Duan, Illinois Institute of Technology

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Upgradeable smart contracts (USCs) have become a key trend in smart contract development, bringing flexibility to otherwise immutable code. However, they also introduce security concerns. On the one hand, they require extensive security knowledge to implement in a secure fashion. On the other hand, they provide new strategic weapons for malicious activities. Thus, it is crucial to fully understand them, especially their security implications in the real-world. To this end, we conduct a large-scale study to systematically reveal the status quo of USCs in the wild. To achieve our goal, we develop a complete USC taxonomy to comprehensively characterize the unique behaviors of USCs and further develop USCHUNT, an automated USC analysis framework for supporting our study. Our study aims to answer three sets of essential research questions regarding USC importance, design patterns, and security issues. Our results show that USCs are of great importance to today’s blockchain as they hold billions of USD worth of digital assets. Moreover, our study summarizes eleven unique design patterns of USCs, and discovers a total of 2,546 real-world USC-related security and safety issues in six major categories.

Remote Code Execution from SSTI in the Sandbox: Automatically Detecting and Exploiting Template Escape Bugs

Yudi Zhao, Yuan Zhang, and Min Yang, Fudan University

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Template engines are widely used in web applications to ease the development of user interfaces. The powerful capabilities provided by the template engines can be abused by attackers through server-side template injection (SSTI), enabling severe attacks on the server side, including remote code execution (RCE). Hence, modern template engines have provided a sandbox mode to prevent SSTI attacks from RCE.

In this paper, we study an overlooked sandbox bypass vulnerability in template engines, called template escape, that could elevate SSTI attacks to RCE. By escaping the template rendering process, template escape bugs can be used to inject executable code on the server side. Template escape bugs are subtle to detect and exploit, due to their dependencies on the template syntax and the template rendering logic. Consequently, little knowledge is known about their prevalence and severity in the real world. To this end, we conduct the first in-depth study on template escape bugs and present TEFuzz, an automatic tool to detect and exploit such bugs. By incorporating several new techniques, TEFuzz does not need to learn the template syntax and can generate PoCs and exploits for the discovered bugs. We apply TEFuzz to seven popular PHP template engines. In all, TEFuzz discovers 135 new template escape bugs and synthesizes RCE exploits for 55 bugs. Our study shows that template escape bugs are prevalent and pose severe threats.

Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation

Yifan Yan, Xudong Pan, Mi Zhang, and Min Yang, Fudan University

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Copyright protection for deep neural networks (DNNs) is an urgent need for AI corporations. To trace illegally distributed model copies, DNN watermarking is an emerging technique for embedding and verifying secret identity messages in the prediction behaviors or the model internals. Sacrificing less functionality and involving more knowledge about the target DNN, the latter branch called white-box DNN watermarking is believed to be accurate, credible and secure against most known watermark removal attacks, with emerging research efforts in both the academy and the industry.

In this paper, we present the first systematic study on how the mainstream white-box DNN watermarks are commonly vulnerable to neural structural obfuscation with dummy neurons, a group of neurons which can be added to a target model but leave the model behavior invariant. Devising a comprehensive framework to automatically generate and inject dummy neurons with high stealthiness, our novel attack intensively modifies the architecture of the target model to inhibit the success of watermark verification. With extensive evaluation, our work for the first time shows that nine published watermarking schemes require amendments to their verification procedures.

Rosetta: Enabling Robust TLS Encrypted Traffic Classification in Diverse Network Environments with TCP-Aware Traffic Augmentation

Renjie Xie and Jiahao Cao, Tsinghua University; Enhuan Dong and Mingwei Xu, Tsinghua University and Quan Cheng Laboratory; Kun Sun, George Mason University; Qi Li and Licheng Shen, Tsinghua University; Menghao Zhang, Tsinghua University and Kuaishou Technology

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As the majority of Internet traffic is encrypted by the Transport Layer Security (TLS) protocol, recent advances leverage Deep Learning (DL) models to conduct encrypted traffic classification by automatically extracting complicated and informative features from the packet length sequences of TLS flows. Though existing DL models have reported to achieve excellent classification results on encrypted traffic, we conduct a comprehensive study to show that they all have significant performance degradation in real diverse network environments. After systematically studying the reasons, we discover the packet length sequences of flows may change dramatically due to various TCP mechanisms for reliable transmission in varying network environments. Thereafter, we propose Rosetta to enable robust TLS encrypted traffic classification for existing DL models. It leverages TCP-aware traffic augmentation mechanisms and self-supervised learning to understand implict TCP semantics, and hence extracts robust features of TLS flows. Extensive experiments show that Rosetta can significantly improve the classification performance of existing DL models on TLS traffic in diverse network environments.

SandDriller: A Fully-Automated Approach for Testing Language-Based JavaScript Sandboxes

Abdullah AlHamdan and Cristian-Alexandru Staicu, CISPA Helmholtz Center for Information Security

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Language-based isolation offers a cheap way to restrict the privileges of untrusted code. Previous work proposes a plethora of such techniques for isolating JavaScript code on the client-side, enabling the creation of web mashups. While these solutions are mostly out of fashion among practitioners, there is a growing trend to use analogous techniques for JavaScript code running outside of the browser, e.g., for protecting against supply chain attacks on the server-side. Irrespective of the use case, bugs in the implementation of language-based isolation can have devastating consequences. Hence, we propose SandDriller, the first dynamic analysis-based approach for detecting sandbox escape vulnerabilities. Our core insight is to design testing oracles based on two main objectives of language-based sandboxes: Prevent writes outside the sandbox and restrict access to privileged operations. Using instrumentation, we interpose oracle checks on all the references exchanged between the host and the guest code to detect foreign references that allow the guest code to escape the sandbox. If at run time, a foreign reference is detected by an oracle, SandDriller proceeds to synthesize an exploit for it. We apply our approach to six sandbox systems and find eight unique zero-day sandbox breakout vulnerabilities and two crashes. We believe that SandDriller can be integrated in the development process of sandboxes to detect security vulnerabilities in the pre-release phase.

Secure Floating-Point Training

Deevashwer Rathee, University of California, Berkeley; Anwesh Bhattacharya, Divya Gupta, and Rahul Sharma, Microsoft Research; Dawn Song, University of California, Berkeley

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Secure 2-party computation (2PC) of floating-point arithmetic is improving in performance and recent work runs deep learning algorithms with it, while being as numerically precise as commonly used machine learning (ML) frameworks like PyTorch. We find that the existing 2PC libraries for floating-point support generic computations and lack specialized support for ML training. Hence, their latency and communication costs for compound operations (e.g., dot products) are high. We provide novel specialized 2PC protocols for compound operations and prove their precision using numerical analysis. Our implementation BEACON outperforms state-of-the-art libraries for 2PC of floating-point by over $6\times$.

SHELTER: Extending Arm CCA with Isolation in User Space

Yiming Zhang, Southern University of Science and Technology and The Hong Kong Polytechnic University; Yuxin Hu, Southern University of Science and Technology; Zhenyu Ning, Hunan University and Southern University of Science and Technology; Fengwei Zhang, Southern University of Science and Technology; Xiapu Luo, The Hong Kong Polytechnic University; Haoyang Huang, Southern University of Science and Technology; Shoumeng Yan and Zhengyu He, Ant Group

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The increasing adoption of confidential computing is providing individual users with a more seamless interaction with numerous mobile and server devices. TrustZone is a promising security technology for the use of partitioning sensitive private data into a trusted execution environment (TEE). Unfortunately, third-party developers have limited accessibility to TrustZone. This is because TEE vendors need to validate such security applications to preserve their security rigorously. Moreover, TrustZone-based systems suffer from vulnerabilities affecting Trusted App and trusted OS, possibly causing the entire system to be compromised.

Advanced virtualization-based TEE introduced in the recently new concept of Confidential Compute Architecture (CCA) creates a new physical address space called Realm world for confidential computing to protect the data confidentiality and integrity. The current version of CCA primarily targets the VM level in the Realm world and does not provide user-level isolated environments. To fill up this gap, we present SHELTER, which is a complement to CCA’s primary Realm VM-style architecture. SHELTER allows third-party developers to deploy their applications with isolation in userspace. SHELTER is designed by cooperating with Arm CCA hardware primitive available in Armv9.2 to provide hardware-based isolation while removing the need for software workloads to trust their data to a Host OS, hypervisor, or privileged software (e.g., trusted OS, Secure/Realm hypervisor). We have implemented and evaluated SHELTER, and the results demonstrated that SHELTER guarantees the security of applications with a modest performance overhead (<15%) on real-world workloads.

Sherlock on Specs: Building LTE Conformance Tests through Automated Reasoning

Yi Chen and Di Tang, Indiana University Bloomington; Yepeng Yao, {CAS-KLONAT, BKLONSPT}, Institute of Information Engineering, CAS, and School of Cyber Security, University of Chinese Academy of Sciences; Mingming Zha and Xiaofeng Wang, Indiana University Bloomington; Xiaozhong Liu, Worcester Polytechnic Institute; Haixu Tang, Indiana University Bloomington; Baoxu Liu, {CAS-KLONAT, BKLONSPT}, Institute of Information Engineering, CAS, and School of Cyber Security, University of Chinese Academy of Sciences

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Conformance tests are critical for finding security weaknesses in carrier network systems. However, building a conformance test procedure from specifications is challenging, as indicated by the slow progress made by the 3GPP, particularly in developing security-related tests, even with a large amount of resources already committed. A unique challenge in building the procedure is that a testing system often cannot directly invoke the condition event in a security requirement or directly observe the occurrence of the operation expected to be triggered by the event. Addressing this issue requires an event chain to be found, which once initiated leads to a chain reaction so the testing system can either indirectly triggers the target event or indirectly observe the occurrence of the expected event. To find a solution to this problem and make progress towards a fully automated conformance test generation, we developed a new approach called Contester , which utilizes natural language processing and machine learning to build an event dependency graph from a 3GPP specification, and further perform automated reasoning on the graph to discover the event chains for a given security requirement. Such event chains are further converted by Contester into a conformance testing procedure, which is then executed by a testing system to evaluate the compliance of user equipment (UE) with the security requirement. Our evaluation shows that given 22 security requirements from the LTE NAS specifications, Contester successfully generated over a hundred test procedures in just 25 minutes. After running these procedures on 22 popular UEs including iPhone 13, Pixel 5a and IoT devices, our approach uncovered 197 security requirement violations, with 190 never reported before, rendering these devices to serious security risks such as MITM, fake base station and reply attacks.

Silent Bugs Matter: A Study of Compiler-Introduced Security Bugs

Jianhao Xu, Nanjing University; Kangjie Lu, University of Minnesota; Zhengjie Du, Zhu Ding, and Linke Li, Nanjing University; Qiushi Wu, University of Minnesota; Mathias Payer, EPFL; Bing Mao, Nanjing University

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Compilers assure that any produced optimized code is semantically equivalent to the original code. However, even "correct" compilers may introduce security bugs as security properties go beyond translation correctness. Security bugs introduced by such correct compiler behaviors can be disputable; compiler developers expect users to strictly follow language specifications and understand all assumptions, while compiler users may incorrectly assume that their code is secure. Such bugs are hard to find and prevent, especially when it is unclear whether they should be fixed on the compiler or user side. Nevertheless, these bugs are real and can be severe, thus should be studied carefully.

We perform a comprehensive study on compiler-introduced security bugs (CISB) and their root causes. We collect a large set of CISB in the wild by manually analyzing 4,827 potential bug reports of the most popular compilers (GCC and Clang), distilling them into a taxonomy of CISB. We further conduct a user study to understand how compiler users view compiler behaviors. Our study shows that compiler-introduced security bugs are common and may have serious security impacts. It is unrealistic to expect compiler users to understand and comply with compiler assumptions. For example, the "no-undefined-behavior" assumption has become a nightmare for users and a major cause of CISB.

Squirrel: A Scalable Secure Two-Party Computation Framework for Training Gradient Boosting Decision Tree

Wen-jie Lu and Zhicong Huang, Alibaba Group; Qizhi Zhang, Ant Group; Yuchen Wang, Alibaba Group; Cheng Hong, Ant Group

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Gradient Boosting Decision Tree (GBDT) and its variants are widely used in industry, due to their high efficiency as well as strong interpretability. Secure multi-party computation allows multiple data owners to compute a function jointly while keeping their input private. In this work, we present Squirrel, a secure two-party GBDT training framework on a vertically split dataset, where two data owners each hold different features of the same data samples. Squirrel is private against semi-honest adversaries, and no sensitive intermediate information is revealed during the training process. Squirrel is also scalable to datasets with millions of samples even under a Wide Area Network (WAN).

Squirrel achieves its high performance via several novel co-designs of the GBDT algorithms and advanced cryptography. Especially, 1) we propose a new mechanism to hide the sample distribution on each node using oblivious transfer. 2) We propose a highly optimized method for secure gradient aggregation using two lattice-based homomorphic encryption schemes. Our empirical results show that our method can be three orders of magnitude faster than the existing approaches. 3) We propose a novel protocol to evaluate the sigmoid function on secretly shared values, showing 19×-200×-fold improvements over two existing methods. Combining all these improvements, Squirrel costs less than 6 seconds per tree on a dataset with 50 thousands samples which outperforms Pivot (VLDB 2020) by more than 28×. We also show that Squirrel can scale up to datasets with more than one million samples, e.g., about 90 seconds per tree over a WAN.

Subverting Website Fingerprinting Defenses with Robust Traffic Representation

Meng Shen, School of Cyberspace Science and Technology, Beijing Institute of Technology; Kexin Ji and Zhenbo Gao, School of Computer Science, Beijing Institute of Technology; Qi Li, Institute for Network Sciences and Cyberspace, Tsinghua University; Liehuang Zhu, School of Cyberspace Science and Technology, Beijing Institute of Technology; Ke Xu, Department of Computer Science and Technology, Tsinghua University

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Anonymity networks, e.g., Tor, are vulnerable to various website fingerprinting (WF) attacks, which allows attackers to perceive user privacy on these networks. However, the defenses developed recently can effectively interfere with WF attacks, e.g., by simply injecting dummy packets. In this paper, we propose a novel WF attack called Robust Fingerprinting (RF), which enables an attacker to fingerprint the Tor traffic under various defenses. Specifically, we develop a robust traffic representation method that generates Traffic Aggregation Matrix (TAM) to fully capture key informative features leaked from Tor traces. By utilizing TAM, an attacker can train a CNN-based classifier that learns common high-level traffic features uncovered by different defenses. We conduct extensive experiments with public real-world datasets to compare RF with state-of-the-art (SOTA) WF attacks. The closed- and open-world evaluation results demonstrate that RF significantly outperforms the SOTA attacks. In particular, RF can effectively fingerprint Tor traffic under the SOTA defenses with an average accuracy improvement of 8.9% over the best existing attack (i.e., Tik-Tok).

Temporal CDN-Convex Lens: A CDN-Assisted Practical Pulsing DDoS Attack

Run Guo, Tsinghua University; Jianjun Chen, Tsinghua University and Zhongguancun Laboratory; Yihang Wang and Keran Mu, Tsinghua University; Baojun Liu, Tsinghua University and Zhongguancun Laboratory; Xiang Li, Tsinghua University; Chao Zhang, Tsinghua University and Zhongguancun Laboratory; Haixin Duan, Tsinghua University and Zhongguancun Laboratory and QI-ANXIN Technology Research Institute; Jianping Wu, Tsinghua University and Zhongguancun Laboratory

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As one cornerstone of Internet infrastructure, Content Delivery Networks (CDNs) work as a globally distributed proxy platform between clients and websites, providing the functionalities of speeding up content delivery, offloading web traffic, and DDoS protection. In this paper, however, we reveal that inherent nature of CDN forwarding network can be exploited to compromise service availability.

We present a new class of pulsing denial of service attacks, named CDN-Convex attack. We explore the possibility of exploiting the CDN infrastructure as a converging lens, and concentrating low-rate attacking requests into short, high-bandwidth pulse waves, resulting in a pulsing DoS attack to saturate the targeted TCP services periodically. Through real-world experiments on five leading CDN vendors, we demonstrate that CDN-Convex is practical and flexible. We show that attackers can use it to achieve peak bandwidths over 1000 times greater than their upload bandwidth, seriously degrading the performance and availability of target services. Following the responsible disclosure policy, we have reported our attack details to all affected CDN vendors and proposed possible mitigation solutions.

The Blockchain Imitation Game

Kaihua Qin, Imperial College London, RDI; Stefanos Chaliasos, Imperial College London; Liyi Zhou, Imperial College London, RDI; Benjamin Livshits, Imperial College London; Dawn Song, UC Berkeley, RDI; Arthur Gervais, University College London, RDI

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The use of blockchains for automated and adversarial trading has become commonplace. However, due to the transparent nature of blockchains, an adversary is able to observe any pending, not-yet-mined transactions, along with their execution logic. This transparency further enables a new type of adversary, which copies and front-runs profitable pending transactions in real-time, yielding significant financial gains.

Shedding light on such ''copy-paste'' malpractice, this paper introduces the Blockchain Imitation Game and proposes a generalized imitation attack methodology called Ape. Leveraging dynamic program analysis techniques, Ape supports the automatic synthesis of adversarial smart contracts. Over a timeframe of one year (1st of August, 2021 to 31st of July, 2022), Ape could have yielded 148.96M USD in profit on Ethereum, and 42.70M USD on BNB Smart Chain (BSC).

Not only as a malicious attack, we further show the potential of transaction and contract imitation as a defensive strategy. Within one year, we find that Ape could have successfully imitated 13 and 22 known DeFi attacks on Ethereum and BSC, respectively. Our findings suggest that blockchain validators can imitate attacks in real-time to prevent intrusions in DeFi.

The Case for Learned Provenance Graph Storage Systems

Hailun Ding, Juan Zhai, Dong Deng, and Shiqing Ma, Rutgers University

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Cyberattacks are becoming more frequent and sophisticated, and investigating them becomes more challenging. Provenance graphs are the primary data source to support forensics analysis. Because of system complexity and long attack duration, provenance graphs can be huge, and efficiently storing them remains a challenging problem. Existing works typically use relational or graph databases to store provenance graphs. These solutions suffer from high storage overhead and low query efficiency. Recently, researchers leveraged Deep Neural Networks (DNNs) in storage system design and achieved promising results. We observe that DNNs can embed given inputs as context-aware numerical vector representations, which are compact and support parallel query operations. In this paper, we propose to learn a DNN as the storage system for provenance graphs to achieve storage and query efficiency. We also present novel designs that leverage domain knowledge to reduce provenance data redundancy and build fast-query processing with indexes. We built a prototype LEONARD and evaluated it on 12 datasets. Compared with the relational database Quickstep and the graph database Neo4j, LEONARD reduced the space overhead by up to 25.90x and boosted up to 99.6% query executions.

The Gates of Time: Improving Cache Attacks with Transient Execution

Daniel Katzman, Tel Aviv University; William Kosasih, The University of Adelaide; Chitchanok Chuengsatiansup, The University of Melbourne; Eyal Ronen, Tel Aviv University; Yuval Yarom, The University of Adelaide

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For over two decades, cache attacks have been shown to pose a significant risk to the security of computer systems. In particular, a large number of works show that cache attacks provide a stepping stone for implementing transient-execution attacks. However, much less effort has been expended investigating the reverse direction—how transient execution can be exploited for cache attacks. In this work, we answer this question.

We first show that using transient execution, we can perform arbitrary manipulations of the cache state. Specifically, we design versatile logical gates whose inputs and outputs are the caching state of memory addresses. Our gates are generic enough that we can implement them in WebAssembly. Moreover, the gates work on processors from multiple vendors, including Intel, AMD, Apple, and Samsung. We demonstrate that these gates are Turing complete and allow arbitrary computation on cache states, without exposing the logical values to the architectural state of the program.

We then show two use cases for our gates in cache attacks. The first use case is to amplify the cache state, allowing us to create timing differences of over 100 millisecond between the cases that a specific memory address is cached or not. We show how we can use this capability to build eviction sets in WebAssembly, using only a low-resolution (0.1 millisecond) timer. For the second use case, we present the Prime+Scope attack, a variant of Prime+Probe that decouples the sampling of cache states from the measurement of said state. Prime+Store is the first timing-based cache attack that can sample the cache state at a rate higher than the clock rate. We show how to use Prime+Store to obtain bits from a concurrently executing modular exponentiation, when the only timing signal is at a resolution of 0.1 millisecond.

The Most Dangerous Codec in the World: Finding and Exploiting Vulnerabilities in H.264 Decoders

Willy R. Vasquez, The University of Texas at Austin; Stephen Checkoway, Oberlin College; Hovav Shacham, The University of Texas at Austin

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Modern video encoding standards such as H.264 are a marvel of hidden complexity. But with hidden complexity comes hidden security risk. Decoding video in practice means interacting with dedicated hardware accelerators and the proprietary, privileged software components used to drive them. The video decoder ecosystem is obscure, opaque, diverse, highly privileged, largely untested, and highly exposed—a dangerous combination.

We introduce and evaluate H26FORGE, domain-specific infrastructure for analyzing, generating, and manipulating syntactically correct but semantically spec-non-compliant video files. Using H26FORGE, we uncover insecurity in depth across the video decoder ecosystem, including kernel memory corruption bugs in iOS, memory corruption bugs in Firefox and VLC for Windows, and video accelerator and application processor kernel memory bugs in multiple Android devices.

The OK Is Not Enough: A Large Scale Study of Consent Dialogs in Smartphone Applications

Simon Koch, TU Braunschweig; Benjamin Altpeter, e.V.; Martin Johns, TU Braunschweig

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Mobile applications leaking personal information is a well established observation pre and post GDPR. The legal requirements for personal data collection in the context of tracking are specified by GDPR and the common understanding is, that tracking must be based on proper consent. Studies of the consent dialogs on websites revealed severe issues including dark patterns. However, the mobile space is currently underexplored with initial observations pointing towards a similar state of affairs. To address this research gap we analyze a subset of possible consent dialogs, namely privacy consent dialogs, in 3006 Android and 1773 iOS applications. We show that 22.3% of all apps have any form of dialog with only 11.9% giving the user some form of actionable choice, e.g., at least an accept button. However, this choice is limited as a large proportion of all such dialogs employ some form of dark pattern coercing the user to consent.

The Role of Professional Product Reviewers in Evaluating Security and Privacy

Wentao Guo, Jason Walter, and Michelle L. Mazurek, University of Maryland

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Consumers who use Internet-connected products are often exposed to security and privacy vulnerabilities that they lack time or expertise to evaluate themselves. Can professional product reviewers help by evaluating security and privacy on their behalf? We conducted 17 interviews with product reviewers about their procedures, incentives, and assumptions regarding security and privacy. We find that reviewers have some incentives to evaluate security and privacy, but they also face substantial disincentives and challenges, leading them to consider a limited set of relevant criteria and threat models. We recommend future work to help product reviewers provide useful advice to consumers in ways that align with reviewers' business models and incentives. These include developing usable resources and tools, as well as validating the heuristics they use to judge security and privacy expediently.

Three Lessons From Threema: Analysis of a Secure Messenger

Kenneth G. Paterson, Matteo Scarlata, and Kien Tuong Truong, ETH Zurich

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We provide an extensive cryptographic analysis of Threema, a Swiss-based encrypted messaging application with more than 10 million users and 7000 corporate customers. We present seven different attacks against the protocol in three different threat models. We discuss impact and remediations for our attacks, which have all been responsibly disclosed to Threema and patched. Finally, we draw wider lessons for developers of secure protocols.

Downfall: Exploiting Speculative Data Gathering

Daniel Moghimi, UCSD

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We introduce Downfall attacks, new transient execution attacks that undermine the security of computers running everywhere across the internet. We exploit the gather instruction on high-performance x86 CPUs to leak data across boundaries of user-kernel, processes, virtual machines, and trusted execution environments. We also develop practical and end-to-end attacks to steal cryptographic keys, program’s runtime data, and even data at rest (arbitrary data). Our findings, exploitation techniques, and demonstrated attacks defeat all previous defenses, calling for critical hardware fixes and security updates for widely-used client and server computers.

Token Spammers, Rug Pulls, and Sniper Bots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)

Federico Cernera, Massimo La Morgia, Alessandro Mei, and Francesco Sassi, Sapienza University of Rome

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In this work, we perform a longitudinal analysis of the BNB Smart Chain and Ethereum blockchain from their inception to March 2022. We study the ecosystem of the tokens and liquidity pools, highlighting analogies and differences between the two blockchains. We discover that about 60% of tokens are active for less than one day. Moreover, we find that 1% of addresses create an anomalous number of tokens (between 20% and 25%). We discover that these tokens are used as disposable tokens to perform a particular type of rug pull, which we call 1-day rug pull. We quantify the presence of this operation on both blockchains discovering its prevalence on the BNB Smart Chain. We estimate that 1-day rug pulls generated $240 million in profits. Finally, we present sniper bots, a new kind of trader bot involved in these activities, and we detect their presence and quantify their activity in the rug pull operations.

TreeSync: Authenticated Group Management for Messaging Layer Security

Théophile Wallez, Inria Paris; Jonathan Protzenko, Microsoft Research; Benjamin Beurdouche, Mozilla; Karthikeyan Bhargavan, Inria Paris

Distinguished Paper Award Winner and Co-Winner of the 2023 Internet Defense Prize

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Messaging Layer Security (MLS), currently undergoing standardization at the IETF, is an asynchronous group messaging protocol that aims to be efficient for large dynamic groups, while providing strong guarantees like forward secrecy (FS) and post-compromise security (PCS). While prior work on MLS has extensively studied its group key establishment component (called TreeKEM), many flaws in early designs of MLS have stemmed from its group integrity and authentication mechanisms that are not as well-understood. In this work, we identify and formalize TreeSync: a sub-protocol of MLS that specifies the shared group state, defines group management operations, and ensures consistency, integrity, and authentication for the group state across all members.

We present a precise, executable, machine-checked formal specification of TreeSync, and show how it can be composed with other components to implement the full MLS protocol. Our specification is written in F* and serves as a reference implementation of MLS; it passes the RFC test vectors and is interoperable with other MLS implementations. Using the DY* symbolic protocol analysis framework, we formalize and prove the integrity and authentication guarantees of TreeSync, under minimal security assumptions on the rest of MLS. Our analysis identifies a new attack and we propose several changes that have been incorporated in the latest MLS draft. Ours is the first testable, machine-checked, formal specification for MLS, and should be of interest to both developers and researchers interested in this upcoming standard.

TRIDENT: Towards Detecting and Mitigating Web-based Social Engineering Attacks

Zheng Yang, Joey Allen, and Matthew Landen, Georgia Institute of Technology; Roberto Perdisci, Georgia Tech and University of Georgia; Wenke Lee, Georgia Institute of Technology

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As the weakest link in cybersecurity, humans have become the main target of attackers who take advantage of sophisticated web-based social engineering techniques. These attackers leverage low-tier ad networks to inject social engineering components onto web pages to lure users into websites that the attackers control for further exploitation. Most of these exploitations are Web-based Social Engineering Attacks (WSEAs), such as reward and lottery scams. Although researchers have proposed systems and tools to detect some WSEAs, these approaches are very tailored to specific scam techniques (i.e., tech support scams, survey scams) only. They were not designed to be effective against a broad set of attack techniques. With the ever-increasing diversity and sophistication of WSEAs that any user can encounter, there is an urgent need for new and more effective in-browser systems that can accurately detect generic WSEAs.

To address this need, we propose TRIDENT, a novel defense system that aims to detect and block generic WSEAs in real-time. TRIDENT stops WSEAs by detecting Social Engineering Ads (SE-ads), the entry point of general web social engineering attacks distributed by low-tier ad networks at scale. Our extensive evaluation shows that TRIDENT can detect SE-ads with an accuracy of 92.63% and a false positive rate of 2.57% and is robust against evasion attempts. We also evaluated TRIDENT against the state-of-the-art ad-blocking tools. The results show that TRIDENT outperforms these tools with a 10% increase in accuracy. Additionally, TRIDENT only incurs 2.13% runtime overhead as a median rate, which is small enough to deploy in production.

Trojan Source: Invisible Vulnerabilities

Nicholas Boucher, University of Cambridge; Ross Anderson, University of Cambridge and University of Edinburgh

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We present a new type of attack in which source code is maliciously encoded so that it appears different to a compiler and to the human eye. This attack exploits subtleties in text-encoding standards such as Unicode to produce source code whose tokens are logically encoded in a different order from the one in which they are displayed, leading to vulnerabilities that cannot be perceived directly by human code reviewers. 'Trojan Source' attacks, as we call them, pose an immediate threat both to first-party software and of supply-chain compromise across the industry. We present working examples of Trojan Source attacks in C, C++, C#, JavaScript, Java, Rust, Go, Python SQL, Bash, Assembly, and Solidity. We propose definitive compiler-level defenses, and describe other mitigating controls that can be deployed in editors, repositories, and build pipelines while compilers are upgraded to block this attack. We document an industry-wide coordinated disclosure for these vulnerabilities; as they affect most compilers, editors, and repositories, the exercise teaches how different firms, open-source communities, and other stakeholders respond to vulnerability disclosure.

TRust: A Compilation Framework for In-process Isolation to Protect Safe Rust against Untrusted Code

Inyoung Bang and Martin Kayondo, Seoul National University; Hyungon Moon, UNIST (Ulsan National Institute of Science and Technology); Yunheung Paek, Seoul National University

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Rust was invented to help developers build highly safe systems. It comes with a variety of programming constructs that put emphasis on safety and control of memory layout. Rust enforces strict discipline about a type system and ownership model to enable compile-time checks of all spatial and temporal safety errors. Despite this advantage in security, the restrictions imposed by Rust’s type system make it difficult or inefficient to express certain designs or computations. To ease or simplify their programming, developers thus often include untrusted code from unsafe Rust or external libraries written in other languages. Sadly, the programming practices embracing such untrusted code for flexibility or efficiency subvert the strong safety guarantees by safe Rust. This paper presents TRUST, a compilation framework which against untrusted code present in the program, provides trustworthy protection of safe Rust via in-process isolation. Its main strategy is allocating objects in an isolated memory region that is accessible to safe Rust but restricted from being written by the untrusted. To enforce this, TRUST employs software fault isolation and x86 protection keys. It can be applied directly to any Rust code without requiring manual changes. Our experiments reveal that TRUST is effective and efficient, incurring runtime overhead of only 7.55% and memory overhead of 13.30% on average when running 11 widely used crates in Rust.

Tubes Among Us: Analog Attack on Automatic Speaker Identification

Shimaa Ahmed and Yash Wani, University of Wisconsin-Madison; Ali Shahin Shamsabadi, Alan Turing Institute; Mohammad Yaghini, University of Toronto and Vector Institute; Ilia Shumailov, Vector Institute and University of Oxford; Nicolas Papernot, University of Toronto and Vector Institute; Kassem Fawaz, University of Wisconsin-Madison

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Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning. Yet, machine learning has proven to be vulnerable to adversarial examples. A large number of modern systems protect themselves against such attacks by targeting artificiality, i.e., they deploy mechanisms to detect the lack of human involvement in generating the adversarial examples. However, these defenses implicitly assume that humans are incapable of producing meaningful and targeted adversarial examples. In this paper, we show that this base assumption is wrong. In particular, we demonstrate that for tasks like speaker identification, a human is capable of producing analog adversarial examples directly with little cost and supervision: by simply speaking through a tube, an adversary reliably impersonates other speakers in eyes of ML models for speaker identification. Our findings extend to a range of other acoustic-biometric tasks such as liveness detection, bringing into question their use in security-critical settings in real life, such as phone banking.

Ultimate SLH: Taking Speculative Load Hardening to the Next Level

Zhiyuan Zhang, The University of Adelaide; Gilles Barthe, MPI-SP and IMDEA Software Institute; Chitchanok Chuengsatiansup, The University of Melbourne; Peter Schwabe, MPI-SP and Radboud University; Yuval Yarom, The University of Adelaide

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In this paper we revisit the Spectre v1 vulnerability and software-only countermeasures. Specifically, we systematically investigate the performance penalty and security properties of multiple variants of speculative load hardening (SLH). As part of this investigation we implement the "strong SLH" variant by Patrignani and Guarnieri (CCS 2021) as a compiler extension to LLVM. We show that none of the existing variants, including strong SLH, is able to protect against all Spectre v1 attacks in practice. We do this by demonstrating, for the first time, that variable-time arithmetic instructions leak secret information even if they are executed only speculatively. We extend strong SLH to include protections also against this kind of leakage, implement the resulting full protection in LLVM, and use the SPEC2017 benchmarks to compare its performance to the existing variants of SLH and to code that uses fencing instructions to completely prevent speculative execution. We show that our proposed countermeasure offers full protection against Spectre v1 attacks at much better performance than code using fences. In fact, for several benchmarks our approach is more than twice as fast.

Understand Users' Privacy Perception and Decision of V2X Communication in Connected Autonomous Vehicles

Zekun Cai and Aiping Xiong, The Pennsylvania State University

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Connected autonomous vehicles (CAVs) offer opportunities to improve road safety and enhance traffic efficiency. Vehicle-to-everything (V2X) communication allows CAVs to communicate with any entity that may affect, or may be affected by, the vehicles. The implementation of V2X in CAVs is inseparable from sharing and receiving a wide variety of data. Nevertheless, the public is not necessarily aware of such ubiquitous data exchange or does not understand their implications. We conducted an online study (N = 595) examining drivers’ privacy perceptions and decisions of four V2X application scenarios. Participants perceived more benefits but fewer risks of data sharing in the V2X scenarios where data collection is critical for driving than otherwise. They also showed more willingness to share data in those scenarios. In addition, we found that participants’ awareness of privacy risks (priming) and their experience on driving assistance and connectivity functions impacted their data-sharing decisions. Qualitative data confirmed that benefits, especially safety, come first, indicating a privacy-safety tradeoff. Moreover, factors such as misconceptions and novel expectations about CAV data collection and use moderated participants’ privacy decisions. We discuss implications of the obtained results to inform CAV privacy design and development.

User Awareness and Behaviors Concerning Encrypted DNS Settings in Web Browsers

Alexandra Nisenoff, Carnegie Mellon University and University of Chicago; Ranya Sharma and Nick Feamster, University of Chicago

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Recent developments to encrypt the Domain Name System (DNS) have resulted in major browser and operating system vendors deploying encrypted DNS functionality, often enabling various configurations and settings by default. In many cases, default encrypted DNS settings have implications for performance and privacy; for example, Firefox’s default DNS setting sends all of a user’s DNS queries to Cloudflare, potentially introducing new privacy vulnerabilities. In this paper, we confirm that most users are unaware of these developments—with respect to the rollout of these new technologies, the changes in default settings, and the ability to customize encrypted DNS configuration to balance user preferences between privacy and performance. Our findings suggest several important implications for the designers of interfaces for encrypted DNS functionality in both browsers and operating systems, to help improve user awareness concerning these settings, and to ensure that users retain the ability to make choices that allow them to balance tradeoffs concerning DNS privacy and performance.

V1SCAN: Discovering 1-day Vulnerabilities in Reused C/C++ Open-source Software Components Using Code Classification Techniques

Seunghoon Woo, Eunjin Choi, Heejo Lee, and Hakjoo Oh, Korea University

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We present V1SCAN, an effective approach for discovering 1-day vulnerabilities in reused C/C++ open-source software (OSS) components. Reusing third-party OSS has many benefits, but can put the entire software at risk owing to the vulnerabilities they propagate. In mitigation, several techniques for detecting propagated vulnerabilities, which can be classified into version- and code-based approaches, have been proposed. However, state-of-the-art techniques unfortunately produce many false positives or negatives when OSS projects are reused with code modifications.

In this paper, we show that these limitations can be addressed by improving version- and code-based approaches and synergistically combining them. By classifying reused code from OSS components, V1SCAN only considers vulnerabilities contained in the target program and filters out unused vulnerable code, thereby reducing false alarms produced by version-based approaches. V1SCAN improves the coverage of code-based approaches by classifying vulnerable code and then detecting vulnerabilities propagated with code changes in various code locations. Evaluation on GitHub popular C/C++ software showed that V1SCAN outperformed state-of-the-art vulnerability detection approaches by discovering 50% more vulnerabilities than they detected. In addition, V1SCAN reduced the false positive rate of the simple integration of existing version- and code-based approaches from 71% to 4% and the false negative rate from 33% to 7%. With V1SCAN, developers can detect propagated vulnerabilities with high accuracy, maintaining a secure software supply chain.

VeriZexe: Decentralized Private Computation with Universal Setup

Alex Luoyuan Xiong, Espresso Systems, National University of Singapore; Binyi Chen and Zhenfei Zhang, Espresso Systems; Benedikt Bünz, Espresso Systems, Stanford University; Ben Fisch, Espresso Systems, Yale University; Fernando Krell and Philippe Camacho, Espresso Systems

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Traditional blockchain systems execute program state transitions on-chain, requiring each network node participating in state-machine replication to re-compute every step of the program when validating transactions. This limits both scalability and privacy. Recently, Bowe et al. introduced a primitive called decentralized private computation (DPC) and provided an instantiation called Zexe, which allows users to execute arbitrary computations off-chain without revealing the program logic to the network. Moreover, transaction validation takes only constant time, independent of the off-chain computation. However, Zexe required a separate trusted setup for each application, which is highly impractical. Prior attempts to remove this per-application setup incurred significant performance loss.

We propose a new DPC instantiation VeriZexe that is highly efficient and requires only a single universal setup to support an arbitrary number of applications. Our benchmark improves the state-of-the-art by 9x in transaction generation time and by 3.4x in memory usage. Along the way, we also design efficient gadgets for variable-base multi-scalar multiplication and modular arithmetic within the Plonk constraint system, leading to a Plonk verifier gadget using only ∼ 21k Plonk constraints.

Watch your Watch: Inferring Personality Traits from Wearable Activity Trackers

Noé Zufferey and Mathias Humbert, University of Lausanne, Switzerland; Romain Tavenard, University of Rennes, CNRS, LETG, France; Kévin Huguenin, University of Lausanne, Switzerland

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Wearable devices, such as wearable activity trackers (WATs), are increasing in popularity. Although they can help to improve one's quality of life, they also raise serious privacy issues. One particularly sensitive type of information has recently attracted substantial attention, namely personality, as it provides a means to influence individuals (e.g., voters in the Cambridge Analytica scandal). This paper presents the first empirical study to show a significant correlation between WAT data and personality traits (Big Five). We conduct an experiment with 200+ participants. The ground truth was established by using the NEO-PI-3 questionnaire. The participants' step count, heart rate, battery level, activities, sleep time, etc. were collected for four months. By following a principled machine-learning approach, the participants' personality privacy was quantified. Our results demonstrate that WATs data brings valuable information to infer the openness, extraversion, and neuroticism personality traits. We further study the importance of the different features (i.e., data types) and found that step counts play a key role in the inference of extraversion and neuroticism, while openness is more related to heart rate.

We Really Need to Talk About Session Tickets: A Large-Scale Analysis of Cryptographic Dangers with TLS Session Tickets

Sven Hebrok, Paderborn University; Simon Nachtigall, Paderborn University and achelos GmbH; Marcel Maehren and Nurullah Erinola, Ruhr University Bochum; Robert Merget, Technology Innovation Institute and Ruhr University Bochum; Juraj Somorovsky, Paderborn University; Jörg Schwenk, Ruhr University Bochum

Distinguished Artifact Award Winner

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Session tickets improve the performance of the TLS protocol. They allow abbreviating the handshake by using secrets from a previous session. To this end, the server encrypts the secrets using a Session Ticket Encryption Key (STEK) only know to the server, which the client stores as a ticket and sends back upon resumption. The standard leaves details such as data formats, encryption algorithms, and key management to the server implementation.

TLS session tickets have been criticized by security experts, for undermining the security guarantees of TLS. An adversary, who can guess or compromise the STEK, can passively record and decrypt TLS sessions and may impersonate the server. Thus, weak implementations of this mechanism may completely undermine TLS security guarantees.

We performed the first systematic large-scale analysis of the cryptographic pitfalls of session ticket implementations. (1) We determined the data formats and cryptographic algorithms used by 12 open-source implementations and designed online and offline tests to identify vulnerable implementations. (2) We performed several large-scale scans and collected session tickets for extended offline analyses.

We found significant differences in session ticket implementations and critical security issues in the analyzed servers. Vulnerable servers used weak keys or repeating keystreams in the used tickets, allowing for session ticket decryption. Among others, our analysis revealed a widespread implemen tation flaw within the Amazon AWS ecosystem that allowed for passive traffic decryption for at least 1.9% of the Tranco Top 100k servers.

Work-From-Home and COVID-19: Trajectories of Endpoint Security Management in a Security Operations Center

Kailani R. Jones and Dalton A. Brucker-Hahn, University of Kansas; Bradley Fidler, Independent Researcher; Alexandru G. Bardas, University of Kansas

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The COVID-19 surge of "Work From Home" (WFH) Internet use incentivized many organizations to strengthen their endpoint security monitoring capabilities. This trend has significant implications for how Security Operations Centers (SOCs) manage these end devices on their enterprise networks: in their organizational roles, regulatory environment, and required skills. By intersecting historical analysis (starting in the 1970s) and ethnography (analyzed 352 field notes across 1,000+ hours in a SOC over 34 months) whilst complementing with quantitative interviews (covering 7 other SOCs), we uncover causal forces that have pushed network management toward endpoints. We further highlight the negative impacts on end user privacy and analyst burnout. As such, we assert that SOCs should consider preparing for a continual, long-term shift from managing the network perimeter and the associated devices to commanding the actual user endpoints while facing potential privacy challenges and more burnout.

X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

Aishan Liu and Jun Guo, Beihang University; Jiakai Wang, Zhongguancun Laboratory; Siyuan Liang, Chinese Academy of Sciences; Renshuai Tao, Beihang University; Wenbo Zhou, University of Science and Technology of China; Cong Liu, iFLYTEK; Xianglong Liu, Beihang University, Zhongguancun Laboratory, and Hefei Comprehensive National Science Center; Dacheng Tao, JD Explore Academy

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Adversarial attacks are valuable for evaluating the robustness of deep learning models. Existing attacks are primarily conducted on the visible light spectrum (e.g., pixel-wise texture perturbation). However, attacks targeting texture-free X-ray images remain underexplored, despite the widespread application of X-ray imaging in safety-critical scenarios such as the X-ray detection of prohibited items. In this paper, we take the first step toward the study of adversarial attacks targeted at X-ray prohibited item detection, and reveal the serious threats posed by such attacks in this safety-critical scenario. Specifically, we posit that successful physical adversarial attacks in this scenario should be specially designed to circumvent the challenges posed by color/texture fading and complex overlapping. To this end, we propose X-Adv to generate physically printable metals that act as an adversarial agent capable of deceiving X-ray detectors when placed in luggage. To resolve the issues associated with color/texture fading, we develop a differentiable converter that facilitates the generation of 3D-printable objects with adversarial shapes, using the gradients of a surrogate model rather than directly generating adversarial textures. To place the printed 3D adversarial objects in luggage with complex overlapped instances, we design a policy-based reinforcement learning strategy to find locations eliciting strong attack performance in worst-case scenarios whereby the prohibited items are heavily occluded by other items. To verify the effectiveness of the proposed X-Adv, we conduct extensive experiments in both the digital and the physical world (employing a commercial X-ray security inspection system for the latter case). Furthermore, we present the physical-world X-ray adversarial attack dataset XAD. We hope this paper will draw more attention to the potential threats targeting safety-critical scenarios. Our codes and XAD dataset are available at

XCheck: Verifying Integrity of 3D Printed Patient-Specific Devices via Computing Tomography

Zhiyuan Yu, Yuanhaur Chang, Shixuan Zhai, Nicholas Deily, and Tao Ju, Washington University in St. Louis; XiaoFeng Wang, Indiana University Bloomington; Uday Jammalamadaka, Rice University; Ning Zhang, Washington University in St. Louis

Distinguished Artifact Award Winner

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3D printing is bringing revolutionary changes to the field of medicine, with applications ranging from hearing aids to regrowing organs. As our society increasingly relies on this technology to save lives, the security of these systems is a growing concern. However, existing defense approaches that leverage side channels may require domain knowledge from computer security to fully understand the impact of the attack.

To bridge the gap, we propose XCheck, which leverages medical imaging to verify the integrity of the printed patient-specific device (PSD). XCheck follows a defense-in-depth approach and directly compares the computed tomography (CT) scan of the printed device to its original design. XCheck utilizes a voxel-based approach to build multiple layers of defense involving both 3D geometric verification and multivariate material analysis. To further enhance usability, XCheck also provides an adjustable visualization scheme that allows practitioners' inspection of the printed object with varying tolerance thresholds to meet the needs of different applications. We evaluated the system with 47 PSDs representing different medical applications to validate the efficacy.

ZBCAN: A Zero-Byte CAN Defense System

Khaled Serag, Rohit Bhatia, Akram Faqih, and Muslum Ozgur Ozmen, Purdue University; Vireshwar Kumar, Indian Institute of Technology, Delhi; Z. Berkay Celik and Dongyan Xu, Purdue University

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Controller Area Network (CAN) is a widely used network protocol. In addition to being the main communication medium for vehicles, it is also used in factories, medical equipment, elevators, and avionics. Unfortunately, CAN was designed without any security features. Consequently, it has come under scrutiny by the research community, showing its security weakness. Recent works have shown that a single compromised ECU on a CAN bus can launch a multitude of attacks ranging from message injection, to bus flooding, to attacks exploiting CAN's error-handling mechanism. Although several works have attempted to secure CAN, we argue that none of their approaches could be widely adopted for reasons inherent in their design. In this work, we introduce ZBCAN, a defense system that uses zero bytes of the CAN frame to secure against the most common CAN attacks, including message injection, impersonation, flooding, and error handling, without using encryption or MACs, while taking into consideration performance metrics such as delay, busload, and data-rate.