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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
A Security and Usability Analysis of Local Attacks Agains...
Tarun Kumar Yadav, Kent Seamons · 2023-08-06 · via cs.CR updates on arXiv.org

The FIDO2 protocol aims to strengthen or replace password authentication using public-key cryptography. FIDO2 has primarily focused on defending against attacks from afar by remote attackers that compromise a password or attempt to phish the user. In this paper, we explore threats from local attacks on FIDO2 that have received less attention -- a browser extension compromise and attackers gaining physical access to an HSK. Our systematic analysis of current implementations of FIDO2 reveals four underlying flaws, and we demonstrate the feasibility of seven attacks that exploit those flaws. The flaws include (1) Lack of confidentiality/integrity of FIDO2 messages accessible to browser extensions, (2) Broken clone detection algorithm, (3) Potential for user misunderstanding from social engineering and notification/error messages, and (4) Cookie life cycle. We build malicious browser extensions and demonstrate the attacks on ten popular web servers that use FIDO2. We also show that many browser extensions have sufficient permissions to conduct the attacks if they were compromised. A static and dynamic analysis of current browser extensions finds no evidence of the attacks in the wild. We conducted two user studies confirming that participants do not detect the attacks with current error messages, email notifications, and UX responses to the attacks. We provide an improved clone detection algorithm and recommendations for relying part