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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
Spin-Orbit Torque Devices for Hardware Security: From Det...
Satwik Patnaik, Nikhil Rangarajan, Johann Knechtel, Ozgur Sinano · 2019-03-31 · via cs.CR updates on arXiv.org

Protecting intellectual property (IP) has become a serious challenge for chip designers. Most countermeasures are tailored for CMOS integration and tend to incur excessive overheads, resulting from additional circuitry or device-level modifications. On the other hand, power density is a critical concern for sub-50 nm nodes, necessitating alternate design concepts. Although initially tailored for error-tolerant applications, imprecise computing has gained traction as a general-purpose design technique. Emerging devices are currently being explored to implement ultra-low-power circuits for inexact computing applications. In this paper, we quantify the security threats of imprecise computing using emerging devices. More specifically, we leverage the innate polymorphism and tunable stochastic behavior of spin-orbit torque (SOT) devices, particularly, the giant spin-Hall effect (GSHE) switch. We enable IP protection (by means of logic locking and camouflaging) simultaneously for deterministic and probabilistic computing, directly at the GSHE device level. We conduct a comprehensive security analysis using state-of-the-art Boolean satisfiability (SAT) attacks; this study demonstrates the superior resilience of our GSHE primitive when tailored for deterministic computing. We also demonstrate how probabilistic computing can thwart most, if not all, existing SAT attacks. Based on this finding, we propose an attack scheme called probabilistic SAT (PSAT) which can bypass the defense offered by logic locking and camouflaging for imprecise computing schemes. Further, we illustrate how careful application of our GSHE primitive can remain secure even on the application of the PSAT attack. Finally, we also discuss side-channel attacks and invasive monitoring, which are arguably even more concerning threats than SAT attacks.