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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
Your DRM Can Watch You Too: Exploring the Privacy Implica...
2023-08-10 · via cs.CR updates on arXiv.org

Thanks to HTML5, users can now view videos on Web browsers without installing plug-ins or relying on specific devices. In 2017, W3C published Encrypted Media Extensions (EME) as the first official Web standard for Digital Rights Management (DRM), with the overarching goal of allowing seamless integration of DRM systems on browsers. EME has prompted numerous voices of dissent with respect to the inadequate protection of users. Of particular interest, privacy concerns were articulated, especially that DRM systems inherently require uniquely identifying information on users' devices to control content distribution better. Despite this anecdotal evidence, we lack a comprehensive overview of how browsers have supported EME in practice and what privacy implications are caused by their implementations. In this paper, we fill this gap by investigating privacy leakage caused by EME relying on proprietary and closed-source DRM systems. We focus on Google Widevine because of its versatility and wide adoption. We conduct empirical experiments to show that browsers diverge when complying EME privacy guidelines, which might undermine users' privacy. For instance, we find that many browsers gladly give away the identifying Widevine Client ID with no or little explicit consent from users. Moreover, we characterize the privacy risks of users tracking when browsers miss applying EME guidelines regarding privacy. Because of being closed-source, our work involves reverse engineering to dissect the contents of EME messages as instantiated by Widevine. Finally, we implement EME Track, a tool that automatically exploits bad Widevine-based implementations to break privacy.