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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
Design and Evaluate Recomposited OR-AND-XOR-PUF
Jianrong Yao, Lihui Pang, Zhi Zhang, Wei Yang, Anmin Fu, Yansong · 2021-10-03 · via cs.CR updates on arXiv.org

Physical Unclonable Function (PUF) is a hardware security primitive with a desirable feature of low-cost. Based on the space of challenge-response pairs (CRPs), it has two categories:weak PUF and strong PUF. Though designing a reliable and secure lightweight strong PUF is challenging, there is continuing efforts to fulfill this gap due to wide range of applications enabled by strong PUF. It was prospected that the combination of MAX and MIN bit-wise operation is promising for improving the modeling resilience when MAX and MIN are employed in the PUF recomposition. The main rationale lies on the fact that each bit-wise might be mainly vulnerable to one specific type of modeling attack, combining them can have an improved holistic resilience. This work is to first evaluate the main PUF performance, in particular,uniformity and reliability of the OR-AND-XOR-PUF(OAX-PUF)-(x, y, z)-OAX-PUF. Compared with the most used l-XOR-PUF, the (x, y, z)-OAX-PUF eventually exhibits better reliability given l=x+y+z without degrading the uniformity retaining to be 50%. We further examine the modeling resilience of the (x, y, z)-OAX-PUF with four powerful attacking strategies to date, which are Logistic Regression (LR) attack, reliability assisted CMA-ES attack, multilayer perceptron (MLP) attack, and the most recent hybrid LR-reliability attack. In comparison with the XOR-APUF, the OAX-APUF successfully defeats the CAM-ES attack. However, it shows no notable modeling accuracy drop against other three attacks, though the attacking times have been greatly prolonged to LR and hybrid LR-reliability attacks. Overall, the OAX recomposition could be an alternative lightweight recomposition method compared to XOR towards constructing strong PUFs if the underlying PUF, e.g., FF-APUF, has exhibited improved resilience to modeling attack, because the OAX incurs smaller reliability degradation compared to XOR.