USENIX Security '23 Technical Sessions

ZigBee is a popular wireless communication standard for Internet of Things (IoT) networks. Since each ZigBee network uses hop-by-hop network-layer message authentication based on a common network key, it is highly vulnerable to packet-injection attacks, in which the adversary exploits the compromised network key to inject arbitrary fake packets from any spoofed address to disrupt network operations and consume the network/device resources. In this paper, we present PhyAuth, a PHY hop-by-hop message authentication framework to defend against packet-injection attacks in ZigBee networks. The key idea of PhyAuth is to let each ZigBee transmitter embed into its PHY signals a PHY one-time password (called POTP) derived from a device-specific secret key and an efficient cryptographic hash function. An authentic POTP serves as the transmitter's PHY transmission permission for the corresponding packet. PhyAuth provides three schemes to embed, detect, and verify POTPs based on different features of ZigBee PHY signals. In addition, PhyAuth involves lightweight PHY signal processing and no change to the ZigBee protocol stack. Comprehensive USRP experiments confirm that PhyAuth can efficiently detect fake packets with very low false-positive and false-negative rates while having a negligible negative impact on normal data transmissions.

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.

A large collection of research literature has identified the privacy risks of keystroke inference attacks that use statistical models to extract content typed onto a keyboard. Yet existing attacks cannot operate in realistic settings, and rely on strong assumptions of labeled training data, knowledge of keyboard layout, carefully placed sensors or data from other side-channels. This paper describes experiences developing and evaluating a general, video-based keystroke inference attack that operates in common public settings using a single commodity camera phone, with no pretraining, no keyboard knowledge, no local sensors, and no side-channels. We show that using a self-supervised approach, noisy finger tracking data from a video can be processed, labeled and filtered to train DNN keystroke inference models that operate accurately on the same video. Using IRB approved user studies, we validate attack efficacy across a variety of environments, keyboards, and content, and users with different typing behaviors and abilities. Our project website is located at: https://sandlab.cs.uchicago.edu/keystroke/.

Private set intersection (PSI) allows two mutually untrusting parties to compute an intersection of their sets, without revealing information about items that are not in the intersection. This work introduces a PSI variant called distance-aware PSI (DA-PSI) for sets whose elements lie in a metric space. DAPSI returns pairs of items that are within a specified distance threshold of each other. This paper puts forward DA-PSI constructions for two metric spaces: (i) Minkowski distance of order 1 over the set of integers (i.e., for integers a and b, their distance is |a−b|); and (ii) Hamming distance over the set of binary strings of length ℓ. In the Minkowski DA-PSI protocol, the communication complexity scales logarithmically in the distance threshold and linearly in the set size. In the Hamming DA-PSI protocol, the communication volume scales quadratically in the distance threshold and is independent of the dimensionality of string length ℓ. Experimental results with real applications confirm that DA-PSI provides more effective matching at lower cost than naïve solutions.

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.

Mass robocalls affect millions of people on a daily basis. Unfortunately, most current defenses against robocalls rely on phone blocklists and are ineffective against caller ID spoofing. To enable detection and blocking of spoofed robocalls, we propose a NLP based smartphone virtual assistant that automatically vets incoming calls. Similar to a human assistant, the virtual assistant picks up an incoming call and uses machine learning models to interact with the caller to determine if the call source is a human or a robocaller. It interrupts a user by ringing the phone only when the call is determined to be not from a robocaller. Security analysis performed by us shows that such a system can stop current and more sophisticated robocallers that might emerge in the future. We also conduct a user study that shows that the virtual assistant can preserve phone call user experience.

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).

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.

Since mobile apps' privacy policies are usually complex, various tools have been developed to examine whether privacy policies have contradictions and verify whether privacy policies are consistent with the apps' behaviors. However, to the best of our knowledge, no prior work answers whether the personal data collection practices (PDCPs) in an app's privacy policy are necessary for given purposes (i.e., whether to comply with the principle of data minimization). Though defined by most existing privacy regulations/laws such as GDPR, the principle of data minimization has been translated into different privacy practices depending on the different contexts (e.g., various developers and targeted users). In the end, the developers can collect personal data claimed in the privacy policies as long as they receive authorizations from the users.

Currently, it mainly relies on legal experts to manually audit the necessity of personal data collection according to the specific contexts, which is not very scalable for millions of apps. In this study, we aim to take the first step to automatically investigate whether PDCPs in an app's privacy policy are overbroad from the perspective of counterpart comparison. Our basic insight is that, if an app claims to collect much more personal data in its privacy policy than most of its counterparts, it is more likely to be conducting overbroad collection. To achieve this, POLICYCOMP, an automatic framework for detecting overbroad PDCPs is proposed. We use POLICYCOMP to perform a large-scale analysis on 10,042 privacy policies and flag 48.29% of PDCPs to be overbroad. We shared our findings with 2,000 app developers and received 52 responses from them, 39 of which acknowledged our findings and took actions (e.g., removing overbroad PDCPs).

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.

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.

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.

Reassembly, a branch of static binary rewriting, has become a focus of research today. However, despite its widespread use and research interest, there have been no systematic investigations on the techniques and challenges of reassemblers. In this paper, we formally define different types of errors that occur in current existing reassemblers, and present an automated tool named REASSESSOR to find such errors. We attempt to show through our tool and the large-scale benchmark we created the current challenges in the field and how they can be approached.

Publishing trajectory data (individual's movement information) is very useful, but it also raises privacy concerns. To handle the privacy concern, in this paper, we apply differential privacy, the standard technique for data privacy, together with Markov chain model, to generate synthetic trajectories. We notice that existing studies all use Markov chain model and thus propose a framework to analyze the usage of the Markov chain model in this problem. Based on the analysis, we come up with an effective algorithm PrivTrace that uses the first-order and second-order Markov model adaptively. We evaluate PrivTrace and existing methods on synthetic and real-world datasets to demonstrate the superiority of our method.

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.

Cache side-channel attacks extract secrets by examining how victim software accesses cache. To date, practical attacks on crypto systems and media libraries are demonstrated under different scenarios, inferring secret keys from crypto algorithms and reconstructing private media data such as images.

This work first presents eight criteria for designing a fullfledged detector for cache side-channel vulnerabilities. Then, we propose CacheQL, a novel detector that meets all of these criteria. CacheQL precisely quantifies information leaks of binary code, by characterizing the distinguishability of logged side channel traces. Moreover, CacheQL models leakage as a cooperative game, allowing information leakage to be precisely distributed to program points vulnerable to cache side channels. CacheQL is meticulously optimized to analyze whole side channel traces logged from production software (where each trace can have millions of records), and it alleviates randomness introduced by crypto blinding, ORAM, or real-world noises.

Our evaluation quantifies side-channel leaks of production crypto and media software. We further localize vulnerabilities reported by previous detectors and also identify a few hundred new vulnerable program points in recent OpenSSL (ver. 3.0.0), MbedTLS (ver. 3.0.0), Libgcrypt (ver. 1.9.4). Many of our localized program points are within the pre-processing modules of crypto libraries, which are not analyzed by existing works due to scalability. We also localize vulnerabilities in Libjpeg (ver. 2.1.2) that leak privacy about input images.

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.

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.

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.

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.

The Linux-based firmware running on Internet of Things (IoT) devices is complex and consists of user level programs as well as kernel level code. Both components have been shown to have serious security vulnerabilities, and the risk linked to kernel vulnerabilities is particularly high, as these can lead to full system compromise. However, previous work only focuses on the user space component of embedded firmware. In this paper, we present Firmware Solution (FirmSolo), a system designed to incorporate the kernel space into firmware analysis. FirmSolo features the Kernel Configuration Reverse Engineering (K.C.R.E.) process that leverages information (i.e., exported and required symbols and version magic) from the kernel modules found in firmware images to build a kernel that can load the modules within an emulated environment. This capability allows downstream analysis to broaden their scope into code executing in privileged mode.

We evaluated FirmSolo on 1,470 images containing 56,688 kernel modules where it loaded 64% of the kernel modules. To demonstrate how FirmSolo aids downstream analysis, we integrate it with two representative analysis systems; the TriforceAFL kernel fuzzer and Firmadyne, a dynamic firmware analysis tool originally devoid of kernel mode analysis capabilities. Our TriforceAFL experiments on a subset of 75 kernel modules discovered 19 previously-unknown bugs in 11 distinct proprietary modules. Through Firmadyne we confirmed the presence of these previously-unknown bugs in 84 firmware images. Furthermore, by using FirmSolo, Firmadyne confirmed a previously-known memory corruption vulnerability in five different versions of the closed-source Kcodes' NetUSB module across 15 firmware images.

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.

Democracy requires transparency. Consequently, courts of law must publish their decisions. At the same time, the interests of the persons involved in these court decisions must be protected. For this reason, court decisions in Europe are anonymized using a variety of techniques. To understand how well these techniques protect the persons involved, we conducted an empirical experiment with 54 law students, whom we asked to de-anonymize 50 German court decisions. We found that all anonymization techniques used in these court decisions were vulnerable, most notably the use of initials. Since even supposedly secure anonymization techniques proved vulnerable, our work empirically reveals the complexity involved in the anonymization of court decisions, and thus calls for further research to increase anonymity while preserving comprehensibility. Toward that end, we provide recommendations for improving anonymization quality. Finally, we provide an empirical notion of “reasonable effort,” to flesh out the definition of anonymity in the legal context. In doing so, we bridge the gap between the technical and the legal understandings of anonymity.

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.

Prototype pollution is a dangerous vulnerability affecting prototype-based languages like JavaScript and the Node.js platform. It refers to the ability of an attacker to inject properties into an object's root prototype at runtime and subsequently trigger the execution of legitimate code gadgets that access these properties on the object's prototype, leading to attacks such as Denial of Service (DoS), privilege escalation, and Remote Code Execution (RCE). While there is anecdotal evidence that prototype pollution leads to RCE, current research does not tackle the challenge of gadget detection, thus only showing feasibility of DoS attacks, mainly against Node.js libraries.

In this paper, we set out to study the problem in a holistic way, from the detection of prototype pollution to detection of gadgets, with the ambitious goal of finding end-to-end exploits beyond DoS, in full-fledged Node.js applications. We build the first multi-staged framework that uses multi-label static taint analysis to identify prototype pollution in Node.js libraries and applications, as well as a hybrid approach to detect universal gadgets, notably, by analyzing the Node.js source code. We implement our framework on top of GitHub's static analysis framework CodeQL to find 11 universal gadgets in core Node.js APIs, leading to code execution. Furthermore, we use our methodology in a study of 15 popular Node.js applications to identify prototype pollutions and gadgets. We manually exploit eight RCE vulnerabilities in three high-profile applications such as NPM CLI, Parse Server, and Rocket.Chat. Our results provide alarming evidence that prototype pollution in combination with powerful universal gadgets lead to RCE in Node.js.

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.

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.

This paper is under embargo and will be on the first day of the symposium.

In this paper, we investigate the security of academic VPNs around the globe, covering various protocols that are used to realize VPN services. Our study considers 3 aspects that can go wrong in a VPN setup, which include (i) the design and implementation of VPN front-ends, (ii) the client-side configurations, and (iii) the back-end configurations. For (i), we tested more than 140 front-ends, and discovered numerous design and implementation issues that enable stealthy but severe attacks, including credential theft and remote code execution. For (ii), we collected and evaluated 2097 VPN setup guides from universities, and discovered many instances of secret key leakage and lack of consideration to potential attacks, leaving many client-side setups vulnerable. Finally, for (iii), we probed more than 2000 VPN back-ends to evaluate their overall health, and uncovered some concerning configuration and maintenance issues on many of them. Our findings suggest that severe cracks exist in the VPN setups of many organizations, making them profitable targets for criminals.

As more users adopt VPNs for a variety of reasons, it is important to develop empirical knowledge of their needs and mental models of what a VPN offers. Moreover, studying VPN users alone is not enough because, by using a VPN, a user essentially transfers trust, say from their network provider, onto the VPN provider. To that end, we are the first to study the VPN ecosystem from both the users' and the providers' perspectives. In this paper, we conduct a quantitative survey of 1,252 VPN users in the U.S. and qualitative interviews of nine providers to answer several research questions regarding the motivations, needs, threat model, and mental model of users, and the key challenges and insights from VPN providers. We create novel insights by augmenting our multi-perspective results, and highlight cases where the user and provider perspectives are misaligned. Alarmingly, we find that users rely on and trust VPN review sites, but VPN providers shed light on how these sites are mostly motivated by money. Worryingly, we find that users have flawed mental models about the protection VPNs provide, and about data collected by VPNs. We present actionable recommendations for technologists and security and privacy advocates by identifying potential areas on which to focus efforts and improve the VPN ecosystem.

EDHOC is a key exchange proposed by IETF’s Lightweight Authenticated Key Exchange (LAKE) Working Group (WG). Its design focuses on small message sizes to be suitable for constrained IoT communication technologies. In this paper we provide an in-depth formal analysis of EDHOC–draft version 12, taking into account the different proposed authentication methods and various options. For our analysis we use the SAPIC+ protocol platform that allows to compile a single specification to 3 state-of-the-art protocol verification tools (PROVERIF, TAMARIN and DEEPSEC) and take advantage of the strengths of each of the tools. In our analysis we consider a large variety of compromise scenarios, and also exploit recent results that allow to model existing weaknesses in cryptographic primitives, relaxing the perfect cryptography assumption, common in symbolic analysis. While our analysis confirmed security for the most basic threat models, a number of weaknesses were uncovered in the current design when more advanced threat models were taken into account. These weaknesses have been acknowledged by the LAKE WG and the mitigations we propose (and prove secure) have been included in version 14 of the draft.

Post-Compromise Security (PCS) is a property of secure-channel establishment schemes, which limits the security breach of an adversary that has compromised one of the endpoint to a certain number of messages, after which the channel heals. An attractive property, especially in view of Snowden’s revelation of mass-surveillance, PCS was pioneered by the Signal messaging protocol, and is present in OTR. In this paper, we introduce a framework for quantifying and comparing PCS security, with respect to a broad taxonomy of adversaries. The generality and flexibility of our approach allows us to model the healing speed of a broad class of protocols, including Signal, but also an identity-based messaging protocol named SAID, and even a composition of 5G handover protocols.

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).

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.

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.

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.