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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
BioZero: Privacy-Preserving and Publicly Verifiable On-Ch...
Zibin Lin, Taotao Wang, Junhao Lai, Shengli Zhang, Qing Yang, So · 2024-09-26 · via cs.CR updates on arXiv.org

Decentralized identity systems promise user-controlled identifiers and cross-domain verification without a shared identity provider, yet authentication still reduces to possession of keys or credentials once secrets are leaked, reused, or replayed. We present BioZero, a privacy-preserving biometric authentication protocol for decentralized identity that binds an enrolled identity to a biometric witness without revealing biometric templates, while enabling publicly verifiable on-chain decisions. BioZero combines Pedersen commitment-homomorphic computation, consistency spot-checks, and Groth16 zero-knowledge proofs to achieve identity-bound authentication with succinct on-chain verification. We analyze acceptance soundness, freshness, template privacy, and non-malleability under an open decentralized threat model including replay, timing, brute-force, oracle, and forgery attacks. On an Ethereum testbed, BioZero achieves up to 67.8x lower network-adjusted total authentication latency and up to 266.4x faster client-side proving than a zk-SNARK-only baseline. Verification stays in the millisecond range (28.8-41.2 ms vs. 35.4-77.6 ms). With lambda=1 spot-checking, gas grows from 336,778 to 954,066 as N increases from 2 to 128, becomes lower than the baseline from N>=16, and is 2.59x lower at N=128. LFW experiments on 128D and 512D models show accuracy loss below 1% across practical quantization ranges. These results indicate that BioZero is a practical authentication layer for decentralized biometric identity systems.