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cs.CR updates on arXiv.org

Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
Security Review of Ethereum Beacon Clients
Jean-Philippe Aumasson, Denis Kolegov, Evangelia Stathopoulou · 2021-09-24 · via cs.CR updates on arXiv.org

The beacon chain is the backbone of the Ethereum's evolution towards a proof-of-stake-based scalable network. Beacon clients are the applications implementing the services required to operate the beacon chain, namely validators, beacon nodes, and slashers. Security defects in beacon clients could lead to loss of funds, consensus rules violation, network congestion, and other inconveniences. We reported more than 35 issues to the beacon client developers, including various security improvements, specification inconsistencies, missing security checks, exposure to known vulnerabilities. None of our findings appears to be high-severity. We covered the four main beacon clients, namely Lighthouse (Rust), Nimbus (Nim), Prysm (Go), and Teku (Java). We looked for bugs in the logic and implementation of the new security-critical components (BLS signatures, slashing, networking protocols, and API) over a 3-month project that followed a preliminary analysis of BLS signatures code. We focused on Lighthouse and Prysm, the most popular clients, and thus the highest-value targets. Furthermore, we identify protocol-level issues, including replay attacks and incomplete forward secrecy. In addition, we reviewed the network fingerprints of beacon clients, discussing the information obtainable from passive and active searches, and we analyzed the supply chain risk related to third-party dependencies, providing indicators and recommendations to reduce the risk of backdoors and unpatchable vulnerabilities. Our results suggest that despite intense scrutiny by security auditors and independent researchers, the complexity and constant evolution of a platform like Ethereum requires regular expert review and thorough SSDLC practices.