Total: 79
With the worldwide COVID-19 pandemic in 2020 and 2021 necessitating working from home, corporate Virtual Private Networks (VPNs) have become an important item securing the continued operation of companies around the globe. However, due to their different use case, corporate VPNs and how users interact with them differ from public VPNs, which are now commonly used by end-users. In this paper, we present a first explorative study of eleven experts' and seven non-experts' mental models in the context of corporate VPNs. We find a partial alignment of these models in the high-level technical understanding while diverging in important parameters of how, when, and why VPNs are being used. While, in general, experts have a deeper technical understanding of VPN technology, we also observe that even they sometimes hold false beliefs on security aspects of VPNs. In summary, we show that the mental models of corporate VPNs differ from those for related security technology, e.g., HTTPS. Our findings allow us to draft recommendations for practitioners to encourage a secure use of VPN technology (through training interventions, better communication, and system design changes in terms of device management). Furthermore, we identify avenues for future research, e.g., into experts' knowledge and balancing privacy and security between system operators and users.
Embedded (aka smart or IoT) devices are increasingly popular and becoming ubiquitous. Unsurprisingly, they are also attractive attack targets for exploits and malware. Low-end embedded devices, designed with strict cost, size, and energy limitations, are especially challenging to secure, given their lack of resources to implement sophisticated security services, available on higher-end computing devices. To this end, several tiny Roots-of-Trust (RoTs) were proposed to enable services, such as remote verification of device's software state and run-time integrity. Such RoTs operate reactively: they can prove whether a desired action (e.g., software update or program execution) was performed on a specific device. However, they can not guarantee that a desired action will be performed, since malware controlling the device can trivially block access to the RoT by ignoring/discarding received commands and other trigger events. This is an important problem because it allows malware to effectively "brick" or incapacitate a potentially huge number of (possibly mission-critical) devices. Though recent work made progress in terms of incorporating more active behavior atop existing RoTs, much of it relies on extensive hardware support in the form of Trusted Execution Environments (TEEs), which are generally too costly for low-end devices. In this paper, we set out to systematically design a minimal active RoT for low-end MCU-s. We begin with three questions: (1) What functionality is required to guarantee actions in the presence of malware? (2) How to implement this efficiently? and (3) What are the security benefits of such an active RoT architecture? We then design, implement, formally verify, and evaluate GAROTA : Generalized Active Root-Of-Trust Architecture. We believe that GAROTA is the first clean-slate design of an active RoT for low-end MCU-s. We show how GAROTA guarantees that even a fully software-compromised low-end MCU performs a desired action. We demonstrate its practicality by implementing GAROTA in the context of three types of applications where actions are triggered by: sensing hardware, network events and timers. We also formally specify and verify GAROTA functionality and properties.
DomainKeys Identified Mail (DKIM) is an email authentication protocol to protect the integrity of email contents. It has been proposed and standardized for over a decade and adopted by Yahoo!, Google, and other leading email service providers. However, little has been done to understand the adoption rate and potential security issues of DKIM due to the challenges of measuring DKIM deployment at scale. In this paper, we provide a large-scale and longitudinal measurement study on how well DKIM is deployed and managed. Our study was made possible by a broad collection of datasets, including 9.5 million DKIM records from passive DNS datasets over five years and 460 million DKIM signatures from real-world email headers. Moreover, we conduct an active measurement on Alexa Top 1 million domains. Our measurement results show that 28.1% of Alexa Top 1 million domains have enabled DKIM, of which 2.9% are misconfigured. We demonstrate that the issues of DKIM key management and DKIM signatures are prevalent in the real world, even for well-known email providers (e.g., Gmail and Mail.ru). We recommend the security community should pay more attention to the systemic problems of DKIM deployment and mitigate these issues from the perspective of protocol design.
The incumbent all-or-nothing model of access control on smartphones has been known to dissatisfy users, due to high overhead (both cognitive and physical) and lack of device-sharing support. Several alternative models have been proposed. However, their efficacy has not been evaluated and compared empirically, due to a lack of detailed quantitative data on users' authorization needs. This paper bridges this gap with a 30-day diary study. We probed a near-representative sample (N = 55) of US smartphone users to gather a comprehensive list of tasks they perform on their phones and their authorization needs for each task. Using this data, we quantify, for the first time, the efficacy of the all-or-nothing model, demonstrating frequent unnecessary or missed interventions (false positive rate (FPR) = 90%, false negative rate (FNR) = 21%). In comparison, we show that app- or task-level models can improve the FPR up to 88% and the FNR up to 20%, albeit with a modest (up to 15%) increase in required upfront configuration. We also demonstrate that the context in which phone sharing happens is consistent up to 75% of the time, showing promise for context-based solutions.
Secure two-party neural network inference (2PC-NN) can offer privacy protection for both the client and the server and is a promising technique in the machine-learning-as-a-service setting. However, the large overhead of the current 2PC-NN inference systems is still being a headache, especially when applied to deep neural networks such as ResNet50. In this work, we present Cheetah, a new 2PC-NN inference system that is faster and more communication-efficient than state-of-the-arts. The main contributions of Cheetah are two-fold: the first part includes carefully designed homomorphic encryption-based protocols that can evaluate the linear layers (namely convolution, batch normalization, and fully-connection) without any expensive rotation operation. The second part includes several lean and communication-efficient primitives for the non-linear functions (e.g., ReLU and truncation). Using Cheetah, we present intensive benchmarks over several large-scale deep neural networks. Take ResNet50 for an example, an end-to-end execution of Cheetah under a WAN setting costs less than 2.5 minutes and 2.3 gigabytes of communication, which outperforms CrypTFlow2 (ACM CCS 2020) by about 5.6× and 12.9×, respectively.
Explainable phishing detection approaches are usually based on references, i.e., they compare a suspicious webpage against a reference list of commonly targeted legitimate brands' webpages. If a webpage is detected as similar to any referenced website but their domains are not aligned, a phishing alert is raised with an explanation comprising its targeted brand. In comparison to other techniques, such explainable reference-based solutions are more robust to ever-changing phishing webpages. However, the webpage similarity is still measured by representations conveying only partial intentions (e.g., screenshot and logo), which (i) incurs considerable false positives and (ii) gives an adversary opportunities to compromise user confidence in the approaches. In this work, we propose, PhishIntention, to extract precise phishing intention of a webpage by visually (i) extracting its brand intention and credential-taking intention, and (ii) interacting with the webpage to confirm the credential-taking intention. We design PhishIntention as a heterogeneous system of deep learning vision models, overcoming various technical challenges. The models "look at" and "interact with" the webpage for its intention, which are robust to potential HTML obfuscation. We compare PhishIntention with four state-of-the-art reference-based approaches on the largest phishing identification dataset consisting of 50K phishing and benign webpages. For similar level of recall, PhishIntention achieves significantly higher precision than the baselines. Moreover, we conduct a continuous field study on the Internet for two months to discover emerging phishing webpages. PhishIntention detects 1,942 new phishing webpages (1,368 not reported by VirusTotal). Comparing to the best baseline, PhishIntention generates 86.5% less false alerts (139 vs. 1,033 false positives) while detecting similar number of real phishing webpages.
Electronic monitoring is the use of technology to track individuals accused or convicted of a crime (or civil violation) as an "alternative to incarceration." Traditionally, this technology has been in the form of ankle monitors, but recently federal, state, and local entities around the U.S. are shifting to using smartphone applications for electronic monitoring. These applications (apps) purport to make the monitoring simpler and more convenient for both the community supervisor and the person being monitored. However, due to the multipurpose nature of smartphones in people's lives and the amount of sensitive information (e.g., sensor data) smartphones make available, this introduces new risks to people coerced to use these apps. To understand what type of privacy-related and other risks might be introduced to people who use these applications, we conducted a privacy-oriented analysis of 16 Android apps used for electronic monitoring. We analyzed the apps first technically, with static and (limited) dynamic analysis techniques. We also analyzed user reviews in the Google Play Store to understand the experiences of the people using these apps, and also the privacy policies. We found that apps contain numerous trackers, the permissions requested by them vary widely (with the most common one being location), and the reviews indicate that people find the apps invasive and frequently dysfunctional. We end the paper by encouraging mobile app marketplaces to reconsider their role in the future of electronic monitoring apps, and computer security and privacy researchers to consider their potential role in auditing carceral technologies. We hope that this work will lead to more transparency in this obfuscated ecosystem.
We design and implement a privacy-preserving Boolean satisfiability (ppSAT) solver, which allows mutually distrustful parties to evaluate the conjunction of their input formulas while maintaining privacy. We first define a family of security guarantees reconcilable with the (known) exponential complexity of SAT solving, and then construct an oblivious variant of the classic DPLL algorithm which can be integrated with existing secure two-party computation (2PC) techniques. We further observe that most known SAT solving heuristics are unsuitable for 2PC, as they are highly data-dependent in order to minimize the number of exploration steps. Faced with how best to trade off between the number of steps and the cost of obliviously executing each one, we design three efficient oblivious heuristics, one deterministic and two randomized. As a result of this effort we are able to evaluate our ppSAT solver on small but practical instances arising from the haplotype inference problem in bioinformatics. We conclude by looking towards future directions for making ppSAT solving more practical, most especially the integration of conflict-driven clause learning (CDCL).
Given stigma and threats surrounding being gay or transgender, LGBTQ+ folks often seek support and information on navigating identity and personal (digital and physical) safety. While prior research on digital security advice focused on a general population and general advice, our work focuses on queer security, safety, and privacy advice-seeking to determine population-specific needs and takeaways for broader advice research. We conducted qualitative semi-structured interviews with 14 queer participants diverse across race, age, gender, sexuality, and socioeconomic status. We find that participants turn to their trusted queer support groups for advice, since they often experienced similar threats. We also document reasons that participants sometimes reject advice, including that it would interfere with their material livelihood and their potential to connect with others. Given our results, we recommend that queer-specific and general security and safety advice focus on specificity—why and how—over consistency, because advice cannot be one-size-fits-all. We also discuss the value of intersectionality as a framework for understanding vulnerability to harms in security research, since our participants' overlapping identities affected their threat models and advice perception.
Smart home devices, such as security cameras, are equipped with visual sensors, either for monitoring or improving user experience. Due to the sensitivity of the home environment, their visual sensing capabilities cause privacy and security concerns. In this paper, we design and implement the CamShield, a companion device to guarantee the privacy of smart security cameras, even if the whole camera system is fully compromised. At a high level, the CamShield is a shielding case that works by attaching it to the front of the security camera to blind it. Then, it uses its own camera for visual recording. The videos are first protected according to user-specified policies, and then transmitted to the security camera and hence to the Internet through a Visible Light Communication (VLC) channel. It ensures that only the authorized entities have full access to the protected videos. Since the CamShield is physically isolated from the shielded security camera and the Internet, it naturally resists many known attacks and can operate as it is expected to.
The adversarial patch attack against image classification models aims to inject adversarially crafted pixels within a restricted image region (i.e., a patch) for inducing model misclassification. This attack can be realized in the physical world by printing and attaching the patch to the victim object; thus, it imposes a real-world threat to computer vision systems. To counter this threat, we design PatchCleanser as a certifiably robust defense against adversarial patches. In PatchCleanser, we perform two rounds of pixel masking on the input image to neutralize the effect of the adversarial patch. This image-space operation makes PatchCleanser compatible with any state-of-the-art image classifier for achieving high accuracy. Furthermore, we can prove that PatchCleanser will always predict the correct class labels on certain images against any adaptive white-box attacker within our threat model, achieving certified robustness. We extensively evaluate PatchCleanser on the ImageNet, ImageNette, and CIFAR-10 datasets and demonstrate that our defense achieves similar clean accuracy as state-of-the-art classification models and also significantly improves certified robustness from prior works. Remarkably, PatchCleanser achieves 83.9% top-1 clean accuracy and 62.1% top-1 certified robust accuracy against a 2%-pixel square patch anywhere on the image for the 1000-class ImageNet dataset.
As users navigate the web they face a multitude of threats; among them, attacks that result in account compromise can be particularly devastating. In a world fraught with data breaches and sophisticated phishing attacks, web services strive to fortify user accounts by adopting new mechanisms that identify and prevent suspicious login attempts. More recently, browser fingerprinting techniques have been incorporated into the authentication workflow of major services as part of their decision-making process for triggering additional security mechanisms (e.g., two-factor authentication). In this paper we present the first comprehensive and in-depth exploration of the security implications of real-world systems relying on browser fingerprints for authentication. Guided by our investigation, we develop a tool for automatically constructing fingerprinting vectors that replicate the process of target websites, enabling the extraction of fingerprints from users' devices that exactly match those generated by target websites. Subsequently, we demonstrate how phishing attackers can replicate users' fingerprints on different devices to deceive the risk-based authentication systems of high-value web services (e.g., cryptocurrency trading) to completely bypass two-factor authentication. To gain a better understanding of whether attackers can carry out such attacks, we study the evolution of browser fingerprinting practices in phishing websites over time. While attackers do not generally collect all the necessary fingerprinting attributes, unfortunately that is not the case for attackers targeting certain financial institutions where we observe an increasing number of phishing sites capable of pulling off our attacks. To address the significant threat posed by our attack, we have disclosed our findings to the vulnerable vendors.
Memory-safety issues in operating systems and popular applications are still top security threats. As one widely exploited vulnerability, Use After Free (UAF) resulted in hundreds of new incidents every year. Existing bug diagnosis techniques report the locations that allocate or deallocate the vulnerable object, but cannot provide sufficient information for developers to reason about a bug or synthesize a correct patch. In this work, we identified incorrect reference counting as one common root cause of UAF bugs: if the developer forgets to increase the counter for a newly created reference, the program may prematurely free the actively used object, rendering other references dangling pointers. We call this problem reference miscounting. By proposing an omission-aware counting model, we developed an automatic method, FREEWILL, to diagnose UAF bugs. FREEWILL first reproduces a UAF bug and collects related execution trace. Then, it identifies the UAF object and related references. Finally, FREEWILL compares reference operations with our model to detect reference miscounting. We evaluated FREEWILL on 76 real-world UAF bugs and it successfully confirmed reference miscounting as root causes for 48 bugs and dangling usage for 18 bugs. FREEWILL also identified five null-pointer dereference bugs and failed to analyze five bugs. FREEWILL can complete its analysis within 15 minutes on average, showing its practicality for diagnosing UAF bugs.
In TrustZone-assisted TEEs, the trusted OS has unrestricted access to both secure and normal world memory. Unfortunately, this architectural limitation has opened an aisle of exploration for attackers, which have demonstrated how to leverage a chain of exploits to hijack the trusted OS and gain full control of the system, targeting (i) the rich execution environment (REE), (ii) all trusted applications (TAs), and (iii) the secure monitor. In this paper, we propose ReZone. The main novelty behind ReZone design relies on leveraging TrustZone-agnostic hardware primitives available on commercially off-the-shelf (COTS) platforms to restrict the privileges of the trusted OS. With ReZone, a monolithic TEE is restructured and partitioned into multiple sandboxed domains named zones, which have only access to private resources. We have fully implemented ReZone for the i.MX 8MQuad EVK and integrated it with Android OS and OP-TEE. We extensively evaluated ReZone using microbenchmarks and real-world applications. ReZone can sustain popular applications like DRM-protected video encoding with acceptable performance overheads. We have surveyed 80 CVE vulnerability reports and estimate that ReZone could mitigate 86.84% of them.
As the performance of general-purpose processors faces diminishing improvements, computing systems are increasingly equipped with domain-specific accelerators. Today's high-end servers tightly integrate such accelerators with the CPU, e.g., giving them direct access to the CPU's last-level cache (LLC). Caches are an important source of information leakage across security domains. This work explores combined cache attacks, complementing traditional co-tenancy with control over one or more accelerators. The constraints imposed on these accelerators, originally perceived as limitations, turn out to be advantageous to an attacker. We develop a novel approach for accelerators to find eviction sets, and leverage precise double-sided control over cache lines to expose undocumented behavior in non-inclusive Intel cache hierarchies. We develop a compact and extensible FPGA hardware accelerator to demonstrate our findings. It constructs eviction sets at unprecedented speeds (<200 µs), outperforming existing techniques with one to three orders of magnitude. It maintains excellent performance, even under high noise pressure. We also use the accelerator to set up a covert channel with fine spatial granularity, encoding more than 3 bits per cache set. Furthermore, it can efficiently evict shared targets with tiny eviction sets, refuting the common assumption that eviction sets must be as large as the cache associativity.
Browser extension fingerprinting has garnered considerable attention recently due to the twofold privacy loss that it incurs. Apart from facilitating tracking by augmenting browser fingerprints, the list of installed extensions can be directly used to infer sensitive user characteristics. However, prior research was performed in a vacuum, overlooking a core dimension of extensions' functionality: how they react to user actions. In this paper, we present the first exploration of user-triggered extension fingerprinting. Guided by our findings from a large-scale static analysis of browser extensions we devise a series of user action templates that enable dynamic extension-exercising frameworks to comprehensively uncover hidden extension functionality that can only be triggered through user interactions. Our experimental evaluation demonstrates the effectiveness of our proposed technique, as we are able to fingerprint 4,971 unique extensions, 36% of which are not detectable by state-of-the-art techniques. To make matters worse, we find that ≈67% of the extensions that require mouse or keyboard interactions lack appropriate safeguards, rendering them vulnerable to pages that simulate user actions through JavaScript. To assist extension developers in protecting users from this privacy threat, we build a tool that automatically includes origin checks for fortifying extensions against invasive sites.
A hypervisor is system software, managing and running virtual machines. Since the hypervisor is placed at the lowestlevel in the typical systems software stack, it has critical security implications. Once compromised, the entire software components running on top of the hypervisor (including all guest virtual machines and applications running within each guest virtual machine) are compromised as well, as the hypervisor has all the privileges to access those. This paper proposes MUNDOFUZZ, a hypervisor fuzzer to enable both coverage-guided and grammar-aware fuzzing. We find that the coverage measurement in hypervisors suffers from noises due to the hypervisor's asynchronous system event handling. In order to filter out such noises, MUNDOFUZZ develops a statistical differential coverage measurement methods, allowing MUNDOFUZZ to capture the clean coverage information for hypervisor inputs. Moreover, we observe that hypervisor inputs have complex input grammars because it supports many different devices and each device has its own input format. Thus, MUNDOFUZZ learns the input grammar through inspecting the coverage characteristics of the given hypervisor input, which is based on the idea that the hypervisor behaves in a different way if the grammatically correct (or incorrect) input is given. We evaluated MUNDOFUZZ with popular hypervisors, QEMU and Bhyve, and MUNDOFUZZ outperformed other state-of-the-art hypervisor fuzzers ranging from 4.91% to 6.60% in terms of coverage. More importantly, MUNDOFUZZ identified 40 previously unknown bugs (including 9 CVEs), demonstrating its strong practical effectiveness in finding real-world hypervisor vulnerabilities.
Among existing privacy attacks on the gradient of neural networks, data reconstruction attack, which reverse engineers the training batch from the gradient, poses a severe threat on the private training data. Despite its empirical success on large architectures and small training batches, unstable reconstruction accuracy is also observed when a smaller architecture or a larger batch is under attack. Due to the weak interpretability of existing learning-based attacks, there is little known on why, when and how data reconstruction attack is feasible. In our work, we perform the first analytic study on the security boundary of data reconstruction from gradient via a microcosmic view on neural networks with rectified linear units (ReLUs), the most popular activation function in practice. For the first time, we characterize the insecure/secure boundary of data reconstruction attack in terms of the neuron exclusivity state of a training batch, indexed by the number of Exclusively Activated Neurons (ExANs, i.e., a ReLU activated by only one sample in a batch). Intuitively, we show a training batch with more ExANs are more vulnerable to data reconstruction attack and vice versa. On the one hand, we construct a novel deterministic attack algorithm which substantially outperforms previous attacks for reconstructing training batches lying in the insecure boundary of a neural network. Meanwhile, for training batches lying in the secure boundary, we prove the impossibility of unique reconstruction, based on which an exclusivity reduction strategy is devised to enlarge the secure boundary for mitigation purposes.
Modern smartphones are equipped with Trusted Execution Environments (TEEs), offering security features resilient even against attackers able to fully compromise the normal operating system (e.g., Linux in Android devices). The academic community, as well as the smartphone manufacturers, have proposed to use TEEs to strengthen the security of authorization protocols. However, the usage of these protocols has been hampered by both practicality issues and lack of completeness in terms of security. To address these issues, in this paper, we design, implement, and evaluate SARA (Secure Android Remote Authorization),an Android library that uses the existing TEE-powered Android APIs to implement secure, end-to-end remote authorization for Android apps. SARA is practical in its design, as it makes use of Android APIs and TEE features that are already present in modern Android devices to implement a novel secure authorization protocol. In fact, SARA does not require any modifications to the Android operating system nor to the code running in TrustZone (the TEE powering existing Android devices). For this reason, it can be readily used in existing apps running on existing smartphones. Moreover, SARA is designed to ensure that even developers that have no experience in implementing security protocols can make use of it within their apps. At the same time, SARA is secure, since it allows implementing authorization protocols that are resilient even against attackers able to achieve root privileges on a compromised Android device. We first evaluate SARA by conducting a user study to ascertain its usability. Then, we prove SARA's security features by formally verifying its security protocol using ProVerif.
ARM-based Android smartphones rely on the TrustZone hardware support for a Trusted Execution Environment (TEE) to implement security-sensitive functions. The TEE runs a separate, isolated, TrustZone Operating System (TZOS), in parallel to Android. The implementation of the cryptographic functions within the TZOS is left to the device vendors, who create proprietary undocumented designs. In this work, we expose the cryptographic design and implementation of Android's Hardware-Backed Keystore in Samsung's Galaxy S8, S9, S10, S20, and S21 flagship devices. We reversed-engineered and provide a detailed description of the cryptographic design and code structure, and we unveil severe design flaws. We present an IV reuse attack on AES-GCM that allows an attacker to extract hardware-protected key material, and a downgrade attack that makes even the latest Samsung devices vulnerable to the IV reuse attack. We demonstrate working key extraction attacks on the latest devices. We also show the implications of our attacks on two higher-level cryptographic protocols between the TrustZone and a remote server: we demonstrate a working FIDO2 WebAuthn login bypass and a compromise of Google's Secure Key Import. We discuss multiple flaws in the design flow of TrustZone based protocols. Although our specific attacks only apply to the ≈100 million devices made by Samsung, it raises the much more general requirement for open and proven standards for critical cryptographic and security designs.
In this paper, we study the performance characteristics of nonbacktracking regex matchers and their vulnerability against ReDoS (regular expression denial of service) attacks. We focus on their known Achilles heel, which are extended regexes that use bounded quantifiers (e.g., '(ab){100}'). We propose a method for generating input texts that can cause ReDoS attacks on these matchers. The method exploits the bounded repetition and uses it to force expensive simulations of the deterministic automaton for the regex. We perform an extensive experimental evaluation of our and other state-of-the-art ReDoS generators on a large set of practical regexes with a comprehensive set of backtracking and nonbacktracking matchers, as well as experiments where we demonstrate ReDoS attacks on state-of-the-art real-world security applications containing SNORT with Hyperscan and the HW-accelerated regex matching engine on the NVIDIA BlueField-2 card. Our experiments show that bounded repetition is indeed a notable weakness of nonbacktracking matchers, with our generator being the only one capable of significantly increasing their running time.
Remote Attestation (RA) is a basic security mechanism that detects malicious presence on various types of computing components, e.g., IoT devices. In a typical IoT setting, RA involves a trusted Verifier that sends a challenge to an untrusted remote Prover, which must in turn reply with a fresh and authentic evidence of being in a trustworthy state. However, most current RA schemes assume a central Verifier, which represents a single point of failure. This feature is problematic when mutually suspicious stakeholders are involved. Furthermore, scalability issues arise as the number of IoT devices (Provers) grows. Although some RA schemes allow peer Provers to act as Verifiers, they involve unrealistic (for IoT devices) requirements, such as time synchronization and synchronous communication. Moreover, they incur heavy memory, computation, and communication burdens, while not considering sleeping or otherwise disconnected devices. Motivated by the need to address these limitations, we construct Scalable Collective Remote Attestation for Pub-Sub (SCRAPS), a novel collective RA scheme. It achieves scalability by outsourcing Verifier duties to a smart contract and mitigates DoS attacks against both Provers and Verifiers. It also removes the need for synchronous communication. Furthermore,RA evidence in SCRAPS is publicly verifiable, which significantly reduces the number of attestation evidence computations, thus lowering Prover burden. We report on a prototype of SCRAPS over Hyperledger Sawtooth (a blockchain geared for IoT use-cases) and evaluate its performance, scalability, and security aspects.
Local Differential Privacy (LDP) protocols enable an untrusted server to perform privacy-preserving, federated data analytics. Various LDP protocols have been developed for different types of data such as categorical data, numerical data, and key-value data. Due to their distributed settings, LDP protocols are fundamentally vulnerable to poisoning attacks, in which fake users manipulate the server's analytics results via sending carefully crafted data to the server. However, existing poisoning attacks focused on LDP protocols for simple data types such as categorical and numerical data, leaving the security of LDP protocols for more advanced data types such as key-value data unexplored. In this work, we aim to bridge the gap by introducing novel poisoning attacks to LDP protocols for key-value data. In such a LDP protocol, a server aims to simultaneously estimate the frequency and mean value of each key among some users, each of whom possesses a set of key-value pairs. Our poisoning attacks aim to simultaneously maximize the frequencies and mean values of some attacker-chosen target keys via sending carefully crafted data from some fake users to the sever. Specifically, since our attacks have two objectives, we formulate them as a two-objective optimization problem. Moreover, we propose a method to approximately solve the two-objective optimization problem, from which we obtain the optimal crafted data the fake users should send to the server. We demonstrate the effectiveness of our attacks to three LDP protocols for key-value data both theoretically and empirically. We also explore two defenses against our attacks, which are effective in some scenarios but have limited effectiveness in other scenarios. Our results highlight the needs for new defenses against our poisoning attacks.
In spite of their effectiveness in the context of vulnerability discovery, current state-of-the-art binary program analysis approaches are limited by inherent trade-offs between accuracy and scalability. In this paper, we identify a set of vulnerability properties that can aid both static and dynamic vulnerability detection techniques, improving the precision of the former and the scalability of the latter. By carefully integrating static and dynamic techniques, we detect vulnerabilities that exhibit these properties in real-world programs at a large scale. We implemented our technique, making several advancements in the analysis of binary code, and created a prototype called ARBITER. We demonstrate the effectiveness of our approach with a large-scale evaluation on four common vulnerability classes: CWE-131 (Incorrect Calculation of Buffer Size), CWE-252 (Unchecked Return Value), CWE-134 (Uncontrolled Format String), and CWE-337 (Predictable Seed in Pseudo-Random Number Generator). We evaluated our approach on more than 76,516 x86-64 binaries in the Ubuntu repositories and discovered new vulnerabilities, including a flaw inserted into programs during compilation.
Bridgefy is a messaging application that uses Bluetooth-based mesh networking. Its developers and others have advertised it for use in areas witnessing large-scale protests involving confrontations between protesters and state agents. In August 2020, a security analysis reported severe vulnerabilities that invalidated Bridgefy's claims of confidentiality, authentication, and resilience. In response, the developers adopted the Signal protocol and then continued to advertise their application as being suitable for use by higher-risk users. In this work, we analyse the security of the revised Bridgefy messenger and SDK and invalidate its security claims. One attack (targeting the messenger) enables an adversary to compromise the confidentiality of private messages by exploiting a time-of-check to time-of-use (TOCTOU) issue, side-stepping Signal's guarantees. The other attack (targeting the SDK) allows an adversary to recover broadcast messages without knowing the network-wide shared encryption key. We also found that the changes deployed in response to the August 2020 analysis failed to remedy the previously reported vulnerabilities. In particular, we show that (i) the protocol persisted to be susceptible to an active attacker-in-the-middle, (ii) an adversary continued to be able to impersonate other users in the broadcast channel of the Bridgefy messenger, (iii) the DoS attack using a decompression bomb was still applicable, albeit in a limited form, and that (iv) the privacy issues of Bridgefy remained largely unresolved.