Reviewerly/exHarmony huggingface dataset:
Splice: Efficiently Removing a User's Data from In-memory Application State

Splice is a new programming framework that allows security-conscious applications to efficiently locate and delete a user's in-memory state. The core technical challenge is determining how to delete a user's memory values without breaking application-specific semantic invariants involving the memory state of remaining users. Splice solves this problem using three techniques: taint tracking (which traces how a user's data flows through memory), deletion by synthesis (which overwrites each user-owned memory value in place, replacing it with a value that preserves the symbolic constraints of enclosing data structures), and a novel type system (which forces applications to employ defensive programming to avoid computing over synthesize-deleted values in unsafe ways). Using four realistic applications that we ported to Splice, we show that Splice's type system and defensive programming requirements are not onerous for developers. We also demonstrate that Splice's run-time overheads are similar to those of prior taint tracking systems, while enabling strong deletion semantics.
Pakistani Teens and Privacy - How Gender Disparities, Religion and Family Values Impact the Privacy Design Space

The understanding of how teenagers perceive, manage and perform privacy is less well-understood in spaces outside of Western, educated, industrialised, rich and democratic countries.
Homomorphic Multiple Precision Multiplication for CKKS and Reduced Modulus Consumption

Homomorphic Encryption (HE) schemes such as BGV, BFV, and CKKS consume some ciphertext modulus for each multiplication. Bootstrapping (BTS) restores the modulus and allows homomorphic computation to continue, but it is time-consuming and requires a significant amount of modulus. For these reasons, decreasing modulus consumption is crucial topic for BGV, BFV and CKKS, on which numerous studies have been conducted.
SHERLOC: Secure and Holistic Control-Flow Violation Detection on Embedded Systems

Microcontroller-based embedded systems are often programmed in low-level languages and are vulnerable to control-flow hijacking attacks. One approach to prevent such attacks is to enforce control-flow integrity (CFI), but inlined CFI enforcement can pose challenges in embedded systems. For example, it increases binary size and changes memory layout. Trace-based control-flow violation detection (CFVD) offers an alternative that doesn't require instrumentation of the protected software or changes to its memory layout. However, existing CFVD methods used in desktop systems require kernel modifications to store and analyze the trace, which limits their use to monitoring unprivileged applications. But, embedded systems are interrupt-driven, with the majority of processing taking place in the privileged mode. Therefore, it is critical to provide a holistic and system-oriented CFVD solution that can monitor control-flow transfers both within and among privileged and unprivileged components.
Poster: Towards a Dataset for the Discrimination between Warranted and Unwarranted Emails

In this research, the prevailing issue we address is the over-generalized perspective of spam/ham (non-spam) classification. Despite the intricacies of spam classification, reliance on user feedback may inadvertently skew filters to misclassify legitimate and malicious emails, as users are prone to flag innocuous commercial mail as spam rather than unsubscribing. Current spam datasets have a propensity to include such user-flagged spam which can lead to further misclassification, leading to filters biased against warranted commercial correspondence. Motivated to address this concern, we introduce two new classification categories that delve deeper into the nuances of spam. 'Warranted spam', refers to consensual communications, from a credible source with transparent and safe opt-out mechanisms, and 'unwarranted spam' describes unsolicited messages, often of a malicious nature. Utilizing these classifications, we propose an innovative and dynamic 'warranted spam' dataset that seeks to pave the way for researchers to develop more sophisticated spam filtering techniques. Furthermore, our study delves into pioneering machine learning and natural language processing approaches, harnessing our dataset's potential. The overarching aspiration of our work is to augment online safety, preserve brand integrity, and optimize both the user experience and the efficacy of email marketing campaigns.
Poster: Query-efficient Black-box Attack for Image Forgery Localization via Reinforcement Learning

Recently, deep learning has been widely used in forensics tools to detect and localize forgery images. However, its susceptibility to adversarial attacks highlights the need for the exploration of anti-forensics research. To achieve this, we introduce an innovative and query-efficient black-box anti-forensics framework tailored for the generation of adversarial forgery images. This framework is designed to simulate the query dynamics of online forensic services, utilizing a Markov Decision Process formulation within the paradigm of reinforcement learning. We further introduce a novel reward function, which evaluates the efficacy of attacks based on the disjunction between query results and attack targets. To improve the query efficiency of these attacks, an actor-critic algorithm is employed to maximize cumulative rewards. Empirical findings substantiate the efficacy of our proposed methodology. Specifically, it demonstrates pronounced adversarial effects on a range of prevailing image forgery detectors, while ensuring negligible visually perceptible distortions in the resultant anti-forensics images.
Stolen Risks of Models with Security Properties

Verifiable robust machine learning, as a new trend of ML security defense, enforces security properties (e.g., Lipschitzness, Monotonicity) on machine learning models and achieves satisfying accuracy-security trade-off. Such security properties identify a series of evasion strategies of ML security attackers and specify logical constraints on their effects on a classifier (e.g., the classifier is monotonically increasing along some feature dimensions). However, little has been done so far to understand the side effect of those security properties on the model privacy.
When Free Tier Becomes Free to Enter: A Non-Intrusive Way to Identify Security Cameras with no Cloud Subscription

Wireless security cameras may deter intruders. Accompanying the hardware, consumers may pay recurring monthly fees for recording videos to the cloud, or use the free tier offering motion alerts and sometimes live streams via the camera app. Many users may purchase the hardware without buying the subscription to save money, which inherently reduces their efficacy. We discover that the wireless traffic generated by a camera responding to stimulating motion may disclose whether or not video is being streamed. A malicious user such as a burglar may use such knowledge to target homes with a ''weak camera'' that does not upload video or turn on live view mode. In such cases, criminal activities would not be recorded though they are performed within the monitoring area of the camera. Accordingly, we describe a novel technique called WeakCamID that creates motion stimuli and sniffs resultant wireless traffic to infer the camera state. We perform a survey involving a total of 220 users, finding that all users think cameras have a consistent security guarantee regardless of the subscription status. Our discovery breaks such ''common sense''. We implement WeakCamID in a mobile app and experiment with 11 popular wireless cameras to show that WeakCamID can identify weak cameras with a mean accuracy of around 95% and within less than 19 seconds.
TileMask: A Passive-Reflection-based Attack against mmWave Radar Object Detection in Autonomous Driving

In autonomous driving, millimeter wave (mmWave) radar has been widely adopted for object detection because of its robustness and reliability under various weather and lighting conditions. For radar object detection, deep neural networks (DNNs) are becoming increasingly important because they are more robust and accurate, and can provide rich semantic information about the detected objects, which is critical for autonomous vehicles (AVs) to make decisions. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Despite the rapid development of DNN-based radar object detection models, there have been no studies on their vulnerability to adversarial attacks. Although some spoofing attack methods are proposed to attack the radar sensor by actively transmitting specific signals using some special devices, these attacks require sub-nanosecond-level synchronization between the devices and the radar and are very costly, which limits their practicability in real world. In addition, these attack methods can not effectively attack DNN-based radar object detection. To address the above problems, in this paper, we investigate the possibility of using a few adversarial objects to attack the DNN-based radar object detection models through passive reflection. These objects can be easily fabricated using 3D printing and metal foils at low cost. By placing these adversarial objects at some specific locations on a target vehicle, we can easily fool the victim AV's radar object detection model. The experimental results demonstrate that the attacker can achieve the attack goal by using only two adversarial objects and conceal them as car signs, which have good stealthiness and flexibility. To the best of our knowledge, this is the first study on the passive-reflection-based attacks against the DNN-based radar object detection models using low-cost, readily-available and easily concealable geometric shaped objects.
Fuzz on the Beach: Fuzzing Solana Smart Contracts

Solana has quickly emerged as a popular platform for building decentralized applications (DApps), such as marketplaces for non-fungible tokens (NFTs). A key reason for its success are Solana's low transaction fees and high performance, which is achieved in part due to its stateless programming model. Although the literature features extensive tooling support for smart contract security, current solutions are largely tailored for the Ethereum Virtual Machine. Unfortunately, the very stateless nature of Solana's execution environment introduces novel attack patterns specific to Solana requiring a rethinking for building vulnerability analysis methods.
MDTD: A Multi-Domain Trojan Detector for Deep Neural Networks

Machine learning models that use deep neural networks (DNNs) are vulnerable to backdoor attacks. An adversary carrying out a backdoor attack embeds a predefined perturbation called a trigger into a small subset of input samples and trains the DNN such that the presence of the trigger in the input results in an adversary-desired output class. Such adversarial retraining however needs to ensure that outputs for inputs without the trigger remain unaffected and provide high classification accuracy on clean samples. Existing defenses against backdoor attacks are computationally expensive, and their success has been demonstrated primarily on image-based inputs. The increasing popularity of deploying pretrained DNNs to reduce costs of re/training large models makes defense mechanisms that aim to detect 'suspicious' input samples preferable.
CryptoBap: A Binary Analysis Platform for Cryptographic Protocols

We introduce CryptoBap, a platform to verify weak secrecy and authentication for the (ARMv8 and RISC-V) machine code of cryptographic protocols. We achieve this by first transpiling the binary of protocols into an intermediate representation and then performing a crypto-aware symbolic execution to automatically extract a model of the protocol that represents all its execution paths. Our symbolic execution resolves indirect jumps and supports bounded loops using the loop-summarization technique, which we fully automate. The extracted model is then translated into models amenable to automated verification via ProVerif and CryptoVerif using a third-party toolchain. We prove the soundness of the proposed approach and used CryptoBap to verify multiple case studies ranging from toy examples to real-world protocols, TinySSH, an implementation of SSH, and WireGuard, a modern VPN protocol.
Control, Confidentiality, and the Right to be Forgotten

Recent digital rights frameworks give users the right to delete their data from systems that store and process their personal information (e.g., the "right to be forgotten" in the GDPR).
The Locality of Memory Checking

Motivated by the extended deployment of authenticated data structures (e.g., Merkle Patricia Tries) for verifying massive amounts of data in blockchain systems, we begin a systematic study of the I/O efficiency of such systems. We first explore the fundamental limitations of memory checking, a previously-proposed abstraction for verifiable storage, in terms of its locality-a complexity measure that we introduce for the first time and is defined as the number of non-contiguous memory regions a checker must query to verifiably answer a read or a write query. Our central result is an Ω(log n/log log n) lower bound for the locality of any memory checker. Then we turn our attention to (dense and sparse) Merkle trees, one of the most celebrated memory checkers, and provide stronger lower bounds for their locality. For example, we show that any dense Merkle tree layout will have average locality at least (1/3)log n. Furthermore, if we allow node duplication, we show that if any write operation has at most polylog complexity, then the read locality cannot be less than log n/log log n. Our lower bounds help us construct two new locality-optimized authenticated data structures (DupTree and PrefixTree) which we implement and evaluate on random operations and real workloads, and which are shown to outperform traditional Merkle trees, especially as the number of leaves increases.
Improving Security Tasks Using Compiler Provenance Information Recovered At the Binary-Level

The complex optimizations supported by modern compilers allow for compiler provenance recovery at many levels. For instance, it is possible to identify the compiler family and optimization level used when building a binary, as well as the individual compiler passes applied to functions within the binary. Yet, many downstream applications of compiler provenance remain unexplored. To bridge that gap, we train and evaluate a multi-label compiler provenance model on data collected from over 27,000 programs built using LLVM 14, and apply the model to a number of security-related tasks. Our approach considers 68 distinct compiler passes and achieves an average F-1 score of 84.4%. We first use the model to examine the magnitude of compiler-induced vulnerabilities, identifying 53 information leak bugs in 10 popular projects. We also show that several compiler optimization passes introduce a substantial amount of functional code reuse gadgets that negatively impact security. Beyond vulnerability detection, we evaluate other security applications, including using recovered provenance information to verify the correctness of Rich header data in Windows binaries (e.g., forensic analysis), as well as for binary decomposition tasks (e.g., third party library detection).
ADEM: An Authentic Digital EMblem

In times of armed conflict, the emblems of the red cross, red crescent, and red crystal are used to mark physical infrastructure. This enables military units to identify assets as protected under international humanitarian law to avoid attacking them. In this paper, we tackle the novel security problem of how to extend such protection to digital, network-connected infrastructure through a digital emblem. A digital emblem has a unique combination of security requirements, namely, authentication, accountability, and a property that we call covert inspection. Covert inspection states that those wishing to authenticate assets as protected must be able to do so without revealing that they may attack unprotected entities.
Interactive Proofs For Differentially Private Counting

Differential Privacy (DP) is often presented as a strong privacy-enhancing technology with broad applicability and advocated as a de facto standard for releasing aggregate statistics on sensitive data. However, in many embodiments, DP introduces a new attack surface: a malicious entity entrusted with releasing statistics could manipulate the results and use the randomness of DP as a convenient smokescreen to mask its nefariousness. Since revealing the random noise would obviate the purpose of introducing it, the miscreant may have a perfect alibi. To close this loophole, we introduce the idea of Interactive Proofs For Differential Privacy, which requires the publishing entity to output a zero knowledge proof that convinces an efficient verifier that the output is both DP and reliable. Such a definition might seem unachievable, as a verifier must validate that DP randomness was generated faithfully without learning anything about the randomness itself. We resolve this paradox by carefully mixing private and public randomness to compute verifiable DP counting queries with theoretical guarantees and show that it is also practical for real-world deployment. We also demonstrate that computational assumptions are necessary by showing a separation between information-theoretic DP and computational DP under our definition of verifiability.
Whole-Program Control-Flow Path Attestation

Path attestation is an approach to remotely attest the execution of a program ℘. In path attestation, a prover platform, which executes ℘, convinces a remote verifier V of the integrity of ℘ by recording the path that ℘ takes as it executes a particular input. While a number of prior techniques have been developed for path attestation, they have generally been applied to record paths only for parts of the execution of ℘. In this paper, we consider the problem of whole program control-flow path attestation, i.e., to attest the execution of the entire program path in ℘. We show that prior approaches for path attestation use sub-optimal techniques that fundamentally fail to scale to whole program paths, and impose a large runtime overhead on the execution of ℘. We then develop Blast, an approach that reduces these overheads using a number of novel approaches inspired by prior work from the program profiling literature. Our experiments show that Blast makes path attestation more practical for use on a wide variety of embedded programs.
Immersive innovations for the communication of heritage, handcraft and sustainability

Textile and fashion brands convey core values through marketing, and in slow-fashion heritage brands this often includes skilled craftsmanship, authenticity, sustainability, and provenance. As industry digitalisation continues, brands are employing immersive technologies – virtual (VR) or augmented (AR) reality – however there is scarce evidence of these being used to communicate craftsmanship or provenance. To explore this potential, survey participants were shown immersive and traditional marketing content and perceptions of brand image were sought. The immersive content elicited new brand associations compared with the traditional content, which may be desirable if targeting a younger or more fashion-forward demographic. However, the immersive content was less effective at inspiring participants to make a purchase, with many participants overwhelmed by the virtual environment. The research concludes that effective immersive content should incorporate a streamlined representation of brand identity and core values, utilising an accessible platform while the adoption of VR headsets remains limited.
Black Ostrich: Web Application Scanning with String Solvers

Securing web applications remains a pressing challenge. Unfortunately, the state of the art in web crawling and security scanning still falls short of deep crawling. A major roadblock is the crawlers' limited ability to pass input validation checks when web applications require data of a certain format, such as email, phone number, or zip code. This paper develops Black Ostrich, a principled approach to deep web crawling and scanning. The key idea is to equip web crawling with string constraint solving capabilities to dynamically infer suitable inputs from regular expression patterns in web applications and thereby pass input validation checks. To enable this use of constraint solvers, we develop new automata-based techniques to process JavaScript regular expressions. We implement our approach extending and combining the Ostrich constraint solver with the Black Widow web crawler. We evaluate Black Ostrich on a set of 8,820 unique validation patterns gathered from over 21,667,978 forms from a combination of the July 2021 Common~Crawl and Tranco top 100K. For these forms and reconstructions of input elements corresponding to the patterns, we demonstrate that Black Ostrich achieves a 99% coverage of the form validations compared to an average of 36% for the state-of-the-art scanners. Moreover, out of the 66,377 domains using these patterns, we solve all patterns on 66,309 (99%) while the combined efforts of the other scanners cover 52,632 (79%). We further show that our approach can boost coverage by evaluating it on three open-source applications. Our empirical studies include a study of email validation patterns, where we find that 213 (26%) out of the 825 found email validation patterns liberally admit XSS injection payloads.
CookieGraph: Understanding and Detecting First-Party Tracking Cookies

As third-party cookie blocking is becoming the norm in mainstream web browsers, advertisers and trackers have started to use first-party cookies for tracking. To understand this phenomenon, we conduct a differential measurement study with versus without third-party cookies. We find that first-party cookies are used to store and exfiltrate identifiers to known trackers even when third-party cookies are blocked.
Towards Practical Sleepy BFT

Bitcoin's longest-chain protocol pioneered consensus under dynamic participation, also known as sleepy consensus, where nodes do not need to be permanently active. However, existing solutions for sleepy consensus still face two major issues, which we address in this work. First, existing sleepy consensus protocols have high latency (either asymptotically or concretely). We tackle this problem and achieve 4Δ latency (Δ is the bound on network delay) in the best case, which is comparable to classic BFT protocols without dynamic participation support. Second, existing protocols have to assume that the set of corrupt participants remains fixed throughout the lifetime of the protocol due to a problem we call costless simulation. We resolve this problem and support growing participation of corrupt nodes. Our new protocol also offers several other important advantages, including support for arbitrary fluctuation of honest participation as well as an efficient recovery mechanism for new active nodes.
CoCo: Efficient Browser Extension Vulnerability Detection via Coverage-guided, Concurrent Abstract Interpretation

Extensions complement web browsers with additional functionalities and also bring new vulnerability venues, allowing privilege escalations from adversarial web pages to use extension APIs. Prior works on extension vulnerability detection adopt classic static analysis, which is unable to handle dynamic JavaScript features such as those function calls as part of array lookups. At the same time, prior abstract interpretation focuses on lightweight server-side JavaScript, which often cannot scale to client-side extension code due to object explosions in the abstract domain.