Scaling DeFi with ZK Rollups: Design, Deployment, and Evaluation of a Real-Time Proof-Of-Concept

Ethereum's scalability limitations pose significant challenges for the adoption of decentralized applications dApps. Zero-Knowledge Rollups ZK Rollups present a promising solution, bundling transactions off-chain and submitting validity proofs on-chain to enhance throughput and efficiency. In thi...

Unlearning Inversion Attacks for Graph Neural Networks

Graph unlearning methods aim to efficiently remove the impact of sensitive data from trained GNNs without full retraining, assuming that deleted information cannot be recovered. In this work, we challenge this assumption by introducing the graph unlearning inversion attack: given only black-box...

PatchDEMUX: a Certifiably Robust Framework for Multi-Label Classifiers against Adversarial Patches

Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophically impair performance. The physically realizable nature of these attacks calls for certifiable defenses, which featur...

CHIP: Chameleon Hash-Based Irreversible Passport for Robust Deep Model Ownership Verification and Active Usage Control

The pervasion of large-scale Deep Neural Networks DNNs and their enormous training costs make their intellectual property IP protection of paramount importance. Recently introduced passport-based methods attempt to steer DNN watermarking towards strengthening ownership verification against...

LPASS: Linear Probes As Stepping Stones for Vulnerability Detection Using Compressed LLMs

Large Language Models LLMs are being extensively used for cybersecurity purposes. One of them is the detection of vulnerable codes. For the sake of efficiency and effectiveness, compression and fine-tuning techniques are being developed, respectively. However, they involve spending substantial...

The Cost of Restaking Vs. Proof-Of-Stake

We compare the efficiency of restaking and Proof-of-Stake PoS protocols in terms of stake requirements. First, we consider the sufficient condition for the restaking graph to be secure. We show that the condition implies that it is always possible to transform such a restaking graph into secure P...

Looking for Attention: Randomized Attention Test Design for Validator Monitoring in Optimistic Rollups

Optimistic Rollups ORUs significantly enhance blockchain scalability but inherently suffer from the verifier's dilemma, particularly concerning validator attentiveness. Current systems lack mechanisms to proactively ensure validators are diligently monitoring L2 state transitions, creating a...

Breaking the Gold Standard: Extracting Forgotten Data under Exact Unlearning in Large Language Models

Large language models are typically trained on datasets collected from the web, which may inadvertently contain harmful or sensitive personal information. To address growing privacy concerns, unlearning methods have been proposed to remove the influence of specific data from trained models. Of...

Verifiable Weighted Secret Sharing

Traditionally, threshold secret sharing TSS schemes assume all parties have equal weight, yet emerging systems like blockchains reveal disparities in party trustworthiness, such as stake or reputation. Weighted Secret Sharing WSS addresses this by assigning varying weights to parties, ensuring...

Transaction Proximity: a Graph-Based Approach to Blockchain Fraud Prevention

This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities EAIs", wallets with direct transaction...

MUSE: Model-Agnostic Tabular Watermarking Via Multi-Sample Selection

We introduce MUSE, a watermarking algorithm for tabular generative models. Previous approaches typically leverage DDIM invertibility to watermark tabular diffusion models, but tabular diffusion models exhibit significantly poorer invertibility compared to other modalities, compromising performanc...

A Reward-Driven Automated Webshell Malicious-Code Generator for Red-Teaming

Whitepaper called A Reward-Driven Automated Webshell Malicious-Code Generator For Red-Teaming...

Dynamic Malware Classification of Windows PE Files Using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion

Malware detection and classification remains a topic of concern for cybersecurity, since it is becoming common for attackers to use advanced obfuscation on their malware to stay undetected. Conventional static analysis is not effective against polymorphic and metamorphic malware as these change...

Light As Deception: GPT-Driven Natural Relighting against Vision-Language Pre-Training Models

Whitepaper called Light As Deception: GPT-Driven Natural Relighting Against Vision-Language Pre-Training Models...

Adversarial Machine Learning for Robust Password Strength Estimation

Passwords remain one of the most common methods for securing sensitive data in the digital age. However, weak password choices continue to pose significant risks to data security and privacy. This study aims to solve the problem by focusing on developing robust password strength estimation models...

Dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation

We propose dpmm, an open-source library for synthetic data generation with Differentially Private DP guarantees. It includes three popular marginal models -- PrivBayes, MST, and AIM -- that achieve superior utility and offer richer functionality compared to alternative implementations...

Local Frames: Exploiting Inherited Origins to Bypass Content Blockers

We present a study of how local frames i.e., iframes with non-URL sources like "about:blank" are mishandled by a wide range of popular Web security and privacy tools. As a result, users of these tools remain vulnerable to the very attack techniques they seek to protect against, including browser...

Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation RAG systems, which integrate Large Language Models LLMs with external knowledge sources, are vulnerable to a range of adversarial attack vectors. This paper examines the importance of RAG systems through recent industry adoption trends and identifies the prominent...

3D Gaussian Splat Vulnerabilities

With 3D Gaussian Splatting 3DGS being increasingly used in safety-critical applications, how can an adversary manipulate the scene to cause harm? We introduce CLOAK, the first attack that leverages view-dependent Gaussian appearances - colors and textures that change with viewing angle - to embed...

Compact and Selective Disclosure for Verifiable Credentials

Self-Sovereign Identity SSI is a novel identity model that empowers individuals with full control over their data, enabling them to choose what information to disclose, with whom, and when. This paradigm is rapidly gaining traction worldwide, supported by numerous initiatives such as the European...

Heterogeneous Graph Backdoor Attack

Heterogeneous Graph Neural Networks HGNNs excel in modeling complex, multi-typed relationships across diverse domains, yet their vulnerability to backdoor attacks remains unexplored. To address this gap, we conduct the first investigation into the susceptibility of HGNNs to existing graph backdoo...

Talking Transactions: Decentralized Communication through Ethereum Input Data Messages (IDMs)

Can you imagine, blockchain transactions can talk! In this paper, we study how they talk and what they talk about. We focus on the input data field of Ethereum transactions, which is designed to allow external callers to interact with smart contracts. In practice, this field also enables users to...

Hush! Protecting Secrets during Model Training: an Indistinguishability Approach

We consider the problem of secret protection, in which a business or organization wishes to train a model on their own data, while attempting to not leak secrets potentially contained in that data via the model. The standard method for training models to avoid memorization of secret information i...

Asymmetry by Design: Boosting Cyber Defenders with Differential Access to AI

As AI-enabled cyber capabilities become more advanced, we propose "differential access" as a strategy to tilt the cybersecurity balance toward defense by shaping access to these capabilities. We introduce three possible approaches that form a continuum, becoming progressively more restrictive for...

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges

The rapid adoption of machine learning ML technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations MLOps has emerged as an integrative approach addressing these requirements by...

Adaptive Privacy-Preserving SSD

Data remanence in NAND flash complicates complete deletion on IoT SSDs. We design an adaptive architecture offering four privacy levels PL0-PL3 that select among address, data, and parity deletion techniques. Quantitative analysis balances efficacy, latency, endurance, and cost. Machine-learning...

Shadow Defense against Gradient Inversion Attack in Federated Learning

Federated learning FL has emerged as a transformative framework for privacy-preserving distributed training, allowing clients to collaboratively train a global model without sharing their local data. This is especially crucial in sensitive fields like healthcare, where protecting patient data is...

Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake Detection

The performance of existing audio deepfake detection frameworks degrades when confronted with new deepfake attacks. Rehearsal-based continual learning CL, which updates models using a limited set of old data samples, helps preserve prior knowledge while incorporating new information. However,...

Randextract: a Reference Library to Test and Validate Privacy Amplification Implementations

Quantum cryptographic protocols do not rely only on quantum-physical resources, they also require reliable classical communication and computation. In particular, the secrecy of any quantum key distribution protocol critically depends on the correct execution of the privacy amplification step. Th...

So, I Climbed to the Top of the Pyramid of Pain -- Now What?

This paper explores the evolving dynamics of cybersecurity in the age of advanced AI, from the perspective of the introduced Human Layer Kill Chain framework. As traditional attack models like Lockheed Martin's Cyber Kill Chain become inadequate in addressing human vulnerabilities exploited by...

VoiceMark: Zero-Shot Voice Cloning-Resistant Watermarking Approach Leveraging Speaker-Specific Latents

Voice cloning VC-resistant watermarking is an emerging technique for tracing and preventing unauthorized cloning. Existing methods effectively trace traditional VC models by training them on watermarked audio but fail in zero-shot VC scenarios, where models synthesize audio from an audio prompt...

Data Flows in You: Benchmarking and Improving Static Data-Flow Analysis on Binary Executables

Data-flow analysis is a critical component of security research. Theoretically, accurate data-flow analysis in binary executables is an undecidable problem, due to complexities of binary code. Practically, many binary analysis engines offer some data-flow analysis capability, but we lack...

Authentication and Authorization in Data Spaces: a Relationship-Based Access Control Approach for Policy Specification Based on ODRL

Data has become a crucial resource in the digital economy, fostering initiatives for secure and sovereign data sharing frameworks such as Data Spaces. However, these distributed environments require fine-grained access control mechanisms that balance openness with sovereignty and security. This...

Keeping an Eye on LLM Unlearning: the Hidden Risk and Remedy

Although Large Language Models LLMs have demonstrated impressive capabilities across a wide range of tasks, growing concerns have emerged over the misuse of sensitive, copyrighted, or harmful data during training. To address these concerns, unlearning techniques have been developed to remove the...

Chances and Challenges of the Model Context Protocol in Digital Forensics and Incident Response

Large language models hold considerable promise for supporting forensic investigations, but their widespread adoption is hindered by a lack of transparency, explainability, and reproducibility. This paper explores how the emerging Model Context Protocol can address these challenges and support th...

Safety Alignment Can Be Not Superficial with Explicit Safety Signals

Recent studies on the safety alignment of large language models LLMs have revealed that existing approaches often operate superficially, leaving models vulnerable to various adversarial attacks. Despite their significance, these studies generally fail to offer actionable solutions beyond data...

When GPT Spills the Tea: Comprehensive Assessment of Knowledge File Leakage in GPTs

Knowledge files have been widely used in large language model LLM agents, such as GPTs, to improve response quality. However, concerns about the potential leakage of knowledge files have grown significantly. Existing studies demonstrate that adversarial prompts can induce GPTs to leak knowledge...

Roundcube Webmail 1.6.7 Cross Site Scripting

Roundcube Webmail versions 1.6.7 and below email capture listener and cross site scripting proof of concept exploit...

Shill Bidding Prevention in Decentralized Auctions Using Smart Contracts

In online auctions, fraudulent behaviors such as shill bidding pose significant risks. This paper presents a conceptual framework that applies dynamic, behavior-based penalties to deter auction fraud using blockchain smart contracts. Unlike traditional post-auction detection methods, this approac...

A Human Study of Cognitive Biases in Web Application Security

Cybersecurity training has become a crucial part of computer science education and industrial onboarding. Capture the Flag CTF competitions have emerged as a valuable, gamified approach for developing and refining the skills of cybersecurity and software engineering professionals. However, while...

Next Generation Authentication for Data Spaces: an Authentication Flow Based on Grant Negotiation and Authorization Protocol for Verifiable Presentations (GNAP4VP)

Identity verification in Data Spaces is a fundamental aspect of ensuring security and privacy in digital environments. This paper presents an identity verification protocol tailored for shared data environments within Data Spaces. This protocol extends the Grant Negotiation and Authorization...

ScadaFlare 1.0 ScadaBR Authenticated RCE Toolkit

This is a modular post-authentication remote code execution exploit targeting ScadaBR versions prior to 1.1.0. This tool is enhanced for red team ops...

Towards a Global Quantum Internet: a Review of Challenges Facing Aerial Quantum Networks

Quantum networks use principles of quantum physics to create secure communication networks. Moving these networks off the ground using drones, balloons, or satellites could help increase the scalability of these networks. This article reviews how such aerial links work, what makes them difficult ...

Quantum Hilbert Transform

The Hilbert transform has been one of the foundational transforms in signal processing, finding it's way into multiple disciplines from cryptography to biomedical sciences. However, there does not exist any quantum analogue for the Hilbert transform. In this work, we introduce a formulation for t...

Merge Hijacking: Backdoor Attacks to Model Merging of Large Language Models

Model merging for Large Language Models LLMs directly fuses the parameters of different models finetuned on various tasks, creating a unified model for multi-domain tasks. However, due to potential vulnerabilities in models available on open-source platforms, model merging is susceptible to...

Quasi-Periodic Optical Key-Enabled Hybrid Cryptography: Merging Diffractive Physics and Deep Learning for High-Dimensional Security

Optical encryption inherently provides strong security advantages, with hybrid optoelectronic systems offering additional degrees of freedom by integrating optical and algorithmic domains. However, existing optical encryption schemes heavily rely on electronic computation, limiting overall...

Disrupting Vision-Language Model-Driven Navigation Services Via Adversarial Object Fusion

We present Adversarial Object Fusion AdvOF, a novel attack framework targeting vision-and-language navigation VLN agents in service-oriented environments by generating adversarial 3D objects. While foundational models like Large Language Models LLMs and Vision Language Models VLMs have enhanced...

Fooling the Watchers: Breaking AIGC Detectors Via Semantic Prompt Attacks

The rise of text-to-image T2I models has enabled the synthesis of photorealistic human portraits, raising serious concerns about identity misuse and the robustness of AIGC detectors. In this work, we propose an automated adversarial prompt generation framework that leverages a grammar tree...

Securing AI Agents with Information-Flow Control

As AI agents become increasingly autonomous and capable, ensuring their security against vulnerabilities such as prompt injection becomes critical. This paper explores the use of information-flow control IFC to provide security guarantees for AI agents. We present a formal model to reason about t...

LLM Agents Should Employ Security Principles

Large Language Model LLM agents show considerable promise for automating complex tasks using contextual reasoning; however, interactions involving multiple agents and the system's susceptibility to prompt injection and other forms of context manipulation introduce new vulnerabilities related to...