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Major technology companies strive to protect the integrity of political advertising on their platforms by implementing and enforcing self-regulatory policies that impose transparency requirements on political ads. In this paper, we quantify whether Facebook’s current enforcement correctly identifies political ads and ensures compliance by advertisers. In a comprehensive, large-scale analysis of 4.2 million political and 29.6 million non-political ads from 215,030 advertisers, we identify ads correctly detected as political (true positives), ads incorrectly detected (false positives), and ads missed by detection (false negatives). Facebook’s current enforcement appears imprecise: 61% more ads are missed than are detected worldwide, and 55% of U.S. detected ads are in fact non-political. Detection performance is uneven across countries, with some having up to 53 times higher false negative rates among clearly political pages than in the U.S. Moreover, enforcement appears inadequate for preventing systematic violations of political advertising policies: for example, advertisers were able to continue running political ads without disclosing them while they were temporarily prohibited in the U.S. We attribute these flaws to five gaps in Facebook’s current enforcement and transparency implementation, and close with recommendations to improve the security of the online political ad ecosystem.
We tracked the largest volunteer security information sharing community known to date: the COVID-19 Cyber Threat Coalition, with over 4,000 members. This enabled us to address long-standing questions on threat information sharing. First, does collaboration at scale lead to better coverage? And second, does making threat data freely available improve the ability of defenders to act? We found that the CTC mostly aggregated existing industry sources of threat information. User-submitted domains often did not make it to the CTC's blocklist as a result of the high threshold posed by its automated quality assurance using VirusTotal. Although this ensured a low false positive rate, it also caused the focus of the blocklist to drift away from domains related to COVID-19 (1.4%-3.6%) to more generic abuse, such as phishing, for which established mitigation mechanisms already exist. However, in the slice of data that was related to COVID-19, we found promising evidence of the added value of a community like the CTC: just 25.1% of these domains were known to existing abuse detection infrastructures at time of listing, as compared to 58.4% of domains on the overall blocklist. From the unique experiment that the CTC represented, we draw three lessons for future threat data sharing initiatives.
Causality analysis on system auditing data has emerged as an important solution for attack investigation. Given a POI (Point-Of-Interest) event (e.g., an alert fired on a suspicious file creation), causality analysis constructs a dependency graph, in which nodes represent system entities (e.g., processes and files) and edges represent dependencies among entities, to reveal the attack sequence. However, causality analysis often produces a huge graph (> 100,000 edges) that is hard for security analysts to inspect. From the dependency graphs of various attacks, we observe that (1) dependencies that are highly related to the POI event often exhibit a different set of properties (e.g., data flow and time) from the less-relevant dependencies; (2) the POI event is often related to a few attack entries (e.g., downloading a file). Based on these insights, we propose DEPIMPACT, a framework that identifies the critical component of a dependency graph (i.e., a subgraph) by (1) assigning discriminative dependency weights to edges to distinguish critical edges that represent the attack sequence from less-important dependencies, (2) propagating dependency impacts backward from the POI event to entry points, and (3) performing forward causality analysis from the top-ranked entry nodes based on their dependency impacts to filter out edges that are not found in the forward causality analysis. Our evaluations on the 150 million real system auditing events of real attacks and the DARPA TC dataset show that DEPIMPACT can significantly reduce the large dependency graphs (∼ 1,000,000 edges) to a small graph (∼ 234 edges), which is 4611× smaller. The comparison with the other state-of-the-art causality analysis techniques shows that DEPIMPACT is 106× more effective in reducing the dependency graphs while preserving the attack sequences.
This paper presents the first comprehensive analysis of contention-based security vulnerabilities in a high-performance simultaneous mulithreaded (SMT) processor. It features a characterization of contention throughout the shared pipeline, and potential resulting leakage channels for each resource. Further, it presents a set of unified mitigation/isolation strategies that dramatically cut that leakage while preserving most of the performance of a full, insecure SMT implementation. These results lay the groundwork for considering SMT execution, with its performance benefits, a reasonable choice even for security-sensitive applications.
Social media and other platforms rely on automated detection of abusive content to help combat disinformation, harassment, and abuse. One common approach is to check user content for similarity against a server-side database of problematic items. However, this method fundamentally endangers user privacy. Instead, we target client-side detection, notifying only the users when such matches occur to warn them against abusive content. Our solution is based on privacy-preserving similarity testing. Existing approaches rely on expensive cryptographic protocols that do not scale well to large databases and may sacrifice the correctness of the matching. To contend with this challenge, we propose and formalize the concept of similarity-based bucketization~(SBB). With SBB, a client reveals a small amount of information to a database-holding server so that it can generate a bucket of potentially similar items. The bucket is small enough for efficient application of privacy-preserving protocols for similarity. To analyze the privacy risk of the revealed information, we introduce a framework for measuring an adversary's confidence in inferring a predicate about the client input correctly. We develop a practical SBB protocol for image content, and evaluate its client privacy guarantee with real-world social media data. We then combine SBB with various similarity protocols, showing that the combination with SBB provides a speedup of at least 29x on large-scale databases compared to that without, while retaining correctness of over 95%.
Digital resources (streaming services, banking accounts, collaborative documents, etc.) are commonly shared among small, social groups. Yet, the security and privacy (S&P) controls for these resources map poorly onto the reality of shared access and ownership (e.g., one shared Netflix password for roommates). One challenge is that the design space for social S&P controls remains unclear. We bridged this gap by engaging end-users in participatory design workshops to envision social solutions to S&P challenges common to their groups. In analyzing the generated ideas and group discussions, we identified four design considerations salient to social S&P controls: social transparency; structures of governance; stakes and responsibility; and, promoting pro-group S&P behaviors. Additionally, we discovered trade-offs and challenges that arise when designing social S&P controls: balancing group security versus individual privacy; combating social friction; mitigating social herding behaviors; and, minimizing coordination costs.
Though playing an essential role in smart home systems, smart speakers are vulnerable to voice spoofing attacks. Passive liveness detection, which utilizes only the collected audio rather than the deployed sensors to distinguish between live-human and replayed voices, has drawn increasing attention. However, it faces the challenge of performance degradation under the different environmental factors as well as the strict requirement of the fixed user gestures. In this study, we propose a novel liveness feature, array fingerprint, which utilizes the microphone array inherently adopted by the smart speaker to determine the identity of collected audios. Our theoretical analysis demonstrates that by leveraging the circular layout of microphones, compared with existing schemes, array fingerprint achieves a more robust performance under the environmental change and user's movement. Then, to leverage such a fingerprint, we propose ARRAYID, a lightweight passive detection scheme, and elaborate a series of features working together with array fingerprint. Our evaluation on the dataset containing 32,780 audio samples and 14 spoofing devices shows that ARRAYID achieves an accuracy of 99.84%, which is superior to existing passive liveness detection schemes.
We design Aardvark, a novel authenticated dictionary with short proofs of correctness for lookups and modifications. Our design reduces storage requirements for transaction validation in cryptocurrencies by outsourcing data from validators to untrusted servers, which supply proofs of correctness of this data as needed. In this setting, short proofs are particularly important because proofs are distributed to many validators, and the transmission of long proofs can easily dominate costs. A proof for a piece of data in an authenticated dictionary may change whenever any (even unrelated) data changes. This presents a problem for concurrent issuance of cryptocurrency transactions, as proofs become stale. To solve this problem, Aardvark employs a versioning mechanism to safely accept stale proofs for a limited time. On a dictionary with 100 million keys, operation proof sizes are about 1KB in a Merkle Tree versus 100–200B in Aardvark. Our evaluation shows that a 32-core validator processes 1492–2941 operations per second, saving about 800× in storage costs relative to maintaining the entire state.
Virtual reality (VR) is an emerging technology that enables new applications but also introduces privacy risks. In this paper, we focus on Oculus VR (OVR), the leading platform in the VR space and we provide the first comprehensive analysis of personal data exposed by OVR apps and the platform itself, from a combined networking and privacy policy perspective. We experimented with the Quest 2 headset and tested the most popular VR apps available on the official Oculus and the SideQuest app stores. We developed OVRseen, a methodology and system for collecting, analyzing, and comparing network traffic and privacy policies on OVR. On the networking side, we captured and decrypted network traffic of VR apps, which was previously not possible on OVR, and we extracted data flows, defined as〈app, data type, destination〉. Compared to the mobile and other app ecosystems, we found OVR to be more centralized and driven by tracking and analytics, rather than by third-party advertising. We show that the data types exposed by VR apps include personally identifiable information (PII), device information that can be used for fingerprinting, and VR-specific data types. By comparing the data flows found in the network traffic with statements made in the apps' privacy policies, we found that approximately 70% of OVR data flows were not properly disclosed. Furthermore, we extracted additional context from the privacy policies, and we observed that 69% of the data flows were used for purposes unrelated to the core functionality of apps.
Modern operating systems fundamentally rely on the strict isolation of user applications from the kernel. This isolation is enforced by the hardware. On Intel CPUs, this isolation has been shown to be imperfect, for instance, with the prefetch side-channel. With Meltdown, it was even completely circumvented. Both the prefetch side channel and Meltdown have been mitigated with the same software patch on Intel. As AMD is believed to be not vulnerable to these attacks, this software patch is not active by default on AMD CPUs. In this paper, we show that the isolation on AMD CPUs suffers from the same type of side-channel leakage. We discover timing and power variations of the prefetch instruction that can be observed from unprivileged user space. In contrast to previous work on prefetch attacks on Intel, we show that the prefetch instruction on AMD leaks even more information. We demonstrate the significance of this side channel with multiple case studies in real-world scenarios. We demonstrate the first microarchitectural break of (fine-grained) KASLR on AMD CPUs. We monitor kernel activity, e.g., if audio is played over Bluetooth, and establish a covert channel. Finally, we even leak kernel memory with 52.85 B/s with simple Spectre gadgets in the Linux kernel. We show that stronger page table isolation should be activated on AMD CPUs by default to mitigate our presented attacks successfully.
Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied, in depth, several kinds of attacks, they have done so in isolation. As a result, we lack a comprehensive picture of the risks caused by the attacks, e.g., the different scenarios they can be applied to, the common factors that influence their performance, the relationship among them, or the effectiveness of possible defenses. In this paper, we fill this gap by presenting a first-of-its-kind holistic risk assessment of different inference attacks against machine learning models. We concentrate on four attacks -- namely, membership inference, model inversion, attribute inference, and model stealing -- and establish a threat model taxonomy. Our extensive experimental evaluation, run on five model architectures and four image datasets, shows that the complexity of the training dataset plays an important role with respect to the attack's performance, while the effectiveness of model stealing and membership inference attacks are negatively correlated. We also show that defenses like DP-SGD and Knowledge Distillation can only mitigate some of the inference attacks. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models, and equally serves as a benchmark tool for researchers and practitioners.
Effective syscall filtering is a key component for withstanding the numerous exploitation techniques and privilege escalation attacks we face today. For example, modern browsers use sandboxing techniques with syscall filtering in order to isolate critical code. Cloud computing heavily uses containers, which virtualize the syscall interface. Recently, cloud providers are switching to in-process containers for performance reasons, calling for better isolation primitives. A new isolation primitive that has the potential to fill this gap is called Protection Keys for Userspace (PKU). Unfortunately, prior research highlights severe deficiencies in how PKU-based systems manage syscalls, questioning their security and practicability. In this work, we comprehensively investigate syscall filtering for PKU-based memory isolation systems. First, we identify new syscall-based attacks that can break a PKU sandbox. Second, we derive syscall filter rules necessary for protecting PKU domains and show efficient ways of enforcing them. Third, we do a comparative study on different syscall interposition techniques with respect to their suitability for PKU, which allows us to design a secure syscall interposition technique that is both fast and flexible. We design and prototype Jenny– a PKU-based memory isolation system that provides powerful syscall filtering capabilities in userspace. Jenny supports various interposition techniques (e.g., seccomp and ptrace), and allows for domain-specific syscall filtering in a nested way. Furthermore, it handles asynchronous signals securely. Our evaluation shows a minor performance impact of 0–5% for nginx.
Depth estimation-based obstacle avoidance has been widely adopted by autonomous systems (drones and vehicles) for safety purpose. It normally relies on a stereo camera to automatically detect obstacles and make flying/driving decisions, e.g., stopping several meters ahead of the obstacle in the path or moving away from the detected obstacle. In this paper, we explore new security risks associated with the stereo vision-based depth estimation algorithms used for obstacle avoidance. By exploiting the weaknesses of the stereo matching in depth estimation algorithms and the lens flare effect in optical imaging, we propose DoubleStar, a long-range attack that injects fake obstacle depth by projecting pure light from two complementary light sources. DoubleStar includes two distinctive attack formats: beams attack and orbs attack, which leverage projected light beams and lens flare orbs respectively to cause false depth perception. We successfully attack two commercial stereo cameras designed for autonomous systems (ZED and Intel RealSense). The visualization of fake depth perceived by the stereo cameras illustrates the false stereo matching induced by DoubleStar. We further use Ardupilot to simulate the attack and demonstrate its impact on drones. To validate the attack on real systems, we perform a real-world attack towards a commercial drone equipped with state-of-the-art obstacle avoidance algorithms. Our attack can continuously bring a flying drone to a sudden stop or drift it away across a long distance under various lighting conditions, even bypassing sensor fusion mechanisms. Specifically, our experimental results show that DoubleStar creates fake depth up to 15 meters in distance at night and up to 8 meters during the daytime. To mitigate this newly discovered threat, we provide discussions on potential countermeasures to defend against DoubleStar.
Continuous compliance with privacy regulations, such as GDPR and CCPA, has become a costly burden for companies from small-sized start-ups to business giants. The culprit is the heavy reliance on human auditing in today's compliance process, which is expensive, slow, and error-prone. To address the issue, we propose PrivGuard, a novel system design that reduces human participation required and improves the productivity of the compliance process. PrivGuard is mainly comprised of two components: (1) PrivAnalyzer, a static analyzer based on abstract interpretation for partly enforcing privacy regulations, and (2) a set of components providing strong security protection on the data throughout its life cycle. To validate the effectiveness of this approach, we prototype PrivGuard and integrate it into an industrial-level data governance platform. Our case studies and evaluation show that PrivGuard can correctly enforce the encoded privacy policies on real-world programs with reasonable performance overhead.
Disassembly is the cornerstone of many binary analysis tasks. Traditional disassembly approaches (e.g., linear and recursive) are not accurate enough, while more sophisticated approaches (e.g., Probabilistic Disassembly, Datalog Disassembly, and XDA) have high overhead, which hinders them from being widely used in time-critical security practices. In this paper, we propose DEEPDI, a novel approach that achieves both accuracy and efficiency. The key idea of DEEPDI is to use a graph neural network model to capture and propagate instruction relations. Specifically, DEEPDI firstly uses superset disassembly to get a superset of instructions. Then we construct a graph model called Instruction Flow Graph to capture different instruction relations. Then a Relational Graph Convolutional Network is used to propagate instruction embeddings for accurate instruction classification. DEEPDI also provides heuristics to recover function entrypoints. We evaluate DEEPDI on several large-scale datasets containing real-world and obfuscated binaries. We show that DEEPDI is comparable or superior to the state-of-the-art disassemblers in terms of accuracy, and is robust against unseen binaries, compilers, platforms, obfuscated binaries, and adversarial attacks. Its CPU version is two times faster than IDA Pro, and its GPU version is 350 times faster.
We study the temporal dynamics of potentially harmful apps (PHAs) on Android by leveraging 8.8M daily on-device detections collected among 11.7M customers of a popular mobile security product between 2019 and 2020. We show that the current security model of Android, which limits security products to run as regular apps and prevents them from automatically removing malicious apps opens a significant window of opportunity for attackers. Such apps warn users about the newly discovered threats, but users do not promptly act on this information, allowing PHAs to persist on their device for an average of 24 days after they are detected. We also find that while app markets remove PHAs after these become known, there is a significant delay between when PHAs are identified and when they are removed: PHAs persist on Google Play for 77 days on average and 34 days on third party marketplaces. Finally, we find evidence of PHAs migrating to other marketplaces after being removed on the original one. This paper provides an unprecedented view of the Android PHA landscape, showing that current defenses against PHAs on Android are not as effective as commonly thought, and identifying multiple research directions that the security community should pursue, from orchestrating more effective PHA takedowns to devising better alerts for mobile security products.
Double-fetch bugs are a plague across all major operating system kernels. They occur when data is fetched twice across the user/kernel trust boundary while allowing concurrent modification. Such bugs enable an attacker to illegally access memory, cause denial of service, or to escalate privileges. So far, the only protection against double-fetch bugs is to detect and fix them. However, they remain incredibly hard to find. Similarly, they fundamentally prohibit efficient, kernel-based stateful system call filtering. We propose Midas to mitigate double-fetch bugs. Midas creates on-demand snapshots and copies of accessed data, enforcing our key invariant that throughout a syscall's lifetime, every read to a userspace object will return the same value. Midas shows no noticeable drop in performance when evaluated on compute-bound workloads. On system call heavy workloads, Midas incurs 0.2-14% performance overhead, while protecting the kernel against any TOCTTOU attacks. On average, Midas shows a 3.4% overhead on diverse workloads across two benchmark suites.
Load Value Injection (LVI) uses Meltdown-type data flows in Spectre-like confused-deputy attacks. LVI has been demonstrated in practical attacks on Intel SGX enclaves, and consequently, mitigations were deployed that incur tremendous overheads of factor 2 to 19. However, as we discover, on fixed hardware LVI-NULL leakage is still present. Hence, to mitigate LVI-NULL in SGX enclaves on LVI-fixed CPUs, the expensive mitigations would still be necessary. In this paper, we propose a lightweight mitigation focused on LVI-NULL in SGX, LVI-NULLify. We systematically analyze and categorize LVI-NULL variants. Our analysis reveals that previously proposed mitigations targeting LVI-NULL are not effective. Our novel mitigation addresses this problem by repurposing segmentation, a fast legacy hardware mechanism that x86 already uses for every memory operation. LVI-NULLify consists of a modified SGX-SDK and a compiler extension which put the enclave in control of LVI-NULL-exploitable memory locations. We evaluate LVI-NULLify on the LVI-fixed Comet Lake CPU and observe a performance overhead below 10% for the worst case, which is substantially lower than previous defenses with a prohibitive overhead of 1220% in the worst case. We conclude that LVI-NULLify is a practical solution to protect SGX enclaves against LVI-NULL today.
Sender-anonymous end-to-end encrypted messaging allows sending messages to a recipient without revealing the sender's identity to the messaging platform. Signal recently introduced a sender anonymity feature that includes an abuse mitigation mechanism meant to allow the platform to block malicious senders on behalf of a recipient. We explore the tension between sender anonymity and abuse mitigation. We start by showing limitations of Signal's deployed mechanism, observing that it results in relatively weak anonymity properties and showing a new griefing attack that allows a malicious sender to drain a victim's battery. We therefore design a new protocol, called Orca, that allows recipients to register a privacy-preserving blocklist with the platform. Without learning the sender's identity, the platform can check that the sender is not on the blocklist and that the sender can be identified by the recipient. We construct Orca using a new type of group signature scheme, for which we give formal security notions. Our prototype implementation showcases Orca's practicality.
Browser rendering utilizes hardware resources shared within and across browsers to display web contents, thus inevitably being vulnerable to side channel attacks. Prior works have studied rendering side channels that are caused by rendering time differences of one frame, such as URL color change. However, it still remains unclear how rendering contentions play a role in side-channel attacks and covert communications. In this paper, we design a novel rendering contention channel. Specifically, we stress the browser's rendering resource with stable, self-adjustable pressure and measure the time taken to render a sequence of frames. The measured time sequence is further used to infer any co-rendering event of the browser. To better understand the channel, we study its cause via a method called single variable testing. That is, we keep all variables the same but only change one to test whether the changed variable contributes to the contention. Our results show that CPU, GPU and screen buffer are all part of the contention. To demonstrate the channel's feasibility, we design and implement a prototype, open-source framework, called SIDER, to launch four attacks using the rendering contention channel, which are (i) cross-browser, cross-mode cookie synchronization, (ii) history sniffing, (iii) website fingerprinting, and (iv) keystroke logging. Our evaluation shows the effectiveness and feasibility of all four attacks.
Google's CECPQ1 experiment in 2016 integrated a post-quantum key-exchange algorithm, newhope1024, into TLS 1.2. The Google-Cloudflare CECPQ2 experiment in 2019 integrated a more efficient key-exchange algorithm, ntruhrss701, into TLS 1.3. This paper revisits the choices made in CECPQ2, and shows how to achieve higher performance for post-quantum key exchange in TLS 1.3 using a higher-security algorithm, sntrup761. Previous work had indicated that ntruhrss701 key generation was much faster than sntrup761 key generation, but this paper makes sntrup761 key generation much faster by generating a batch of keys at once. Batch key generation is invisible at the TLS protocol layer, but raises software-engineering questions regarding the difficulty of integrating batch key exchange into existing TLS libraries and applications. This paper shows that careful choices of software layers make it easy to integrate fast post-quantum software, including batch key exchange, into TLS with minor changes to TLS libraries and no changes to applications. As a demonstration of feasibility, this paper reports successful integration of its fast sntrup761 library, via a lightly patched OpenSSL, into an unmodified web browser and an unmodified TLS terminator. This paper also reports TLS 1.3 handshake benchmarks, achieving more TLS 1.3 handshakes per second than any software included in OpenSSL.
A voiceprint is the distinctive pattern of human voices that is spectrographically produced and has been widely used for authentication in the voice assistants. This paper investigates the impact of speech contents on the distinctiveness of voiceprint, and has obtained answers to three questions by studying 2457 speakers and 14,600,000 test samples: 1) What are the influential factors that determine the distinctiveness of voiceprints? 2) How to quantify the distinctiveness of voiceprints for given words, e.g., wake-up words in commercial voice assistants? 3) How to construct wake-up words whose voiceprints have high distinctiveness levels. To answer those questions, we break down voiceprint into phones, and experimentally obtain the correlation between the false recognition rates and the richness of the phone types, the order, the length, and the elements of the phones. Then, we define PROLE Score that can be easily calculated based on speech content yet can reflect the voice distinctiveness. Under the guidance of PROLE Score, we tested 30 wake-up words of 19 commercial voice assistant products, e.g., "Hey, Siri'', "OK, Google'' and "Nihao, Xiaona'' in both English and Chinese. Finally, we provide recommendations for both users and manufacturers, on selecting secure voiceprint words.
Recently, several hardware-assisted security architectures have been proposed to mitigate the ever-growing cyber-attacks on Internet-connected devices. However, such proposals are not compatible with a large portion of the already deployed resource-constrained embedded devices due to hardware limitations. To fill this gap, we propose PISTIS, a pure-software trusted computing architecture for bare-metal low-end embedded devices. PISTIS enables several security services, such as memory isolation, remote attestation and secure code update, while fully supporting critical features such as Direct Memory Access (DMA) and interrupts. PISTIS targets a wide range of embedded devices including those that lack any hardware protection mechanisms, while only requiring a few kilobytes of Flash memory to store its root of trust (RoT) software. The entire architecture of PISTIS is built from the ground up by leveraging memory protection-enabling techniques such as assembly-level code verification and selective software virtualisation. Most importantly, PISTIS achieves strong security guarantees supported by a formally verified design. We implement and evaluate PISTIS on MSP430 architecture, showing a reasonable overhead in terms of runtime, memory footprint, and power consumption.