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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
FADEWICH: Fast Deauthentication over the Wireless Channel
Mauro Conti, Giulio Lovisotto, Ivan Martinovic, Gene Tsudik · 2016-12-27 · via cs.CR updates on arXiv.org

Both authentication and deauthentication are instrumental for preventing unauthorized access to computer and data assets. While there are obvious motivating factors for using strong authentication mechanisms, convincing users to deauthenticate is not straight-forward, since deauthentication is not considered mandatory. A user who leaves a logged-in workstation unattended (especially for a short time) is typically not inconvenienced in any way; in fact, the other way around: no annoying reauthentication is needed upon return. However, an unattended workstation is trivially susceptible to the well-known "lunchtime attack" by any nearby adversary who simply takes over the departed user's log-in session. At the same time, since deathentication does not intrinsically require user secrets, it can, in principle, be made unobtrusive. To this end, this paper designs the first automatic user deauthentication system, FADEWICH, that does not rely on biometric- or behavior-based techniques (e.g., keystroke dynamics) and does not require users to carry any devices. It uses physical properties of wireless signals and the effect of human bodies on their propagation. To assess FADEWICH's feasibility and performance, extensive experiments were conducted with its prototype. Results show that it suffices to have nine inexpensive wireless sensors deployed in a shared office setting to correctly deauthenticate all users within six seconds (90% within four seconds) after they leave their workstation's vicinity. We considered two realistic scenarios where the adversary attempts to subvert FADEWICH and showed that lunchtime attacks fail.