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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
oBAKE: an Online Biometric-Authenticated Key Exchange Pro...
Haochen M. Kotoi-Xie, Takumi Moriyama · 2024-04-16 · via cs.CR updates on arXiv.org

In this writing, we introduce a novel biometric-authenticated key exchange protocol that allows secure and privacy-preserving key establishment between a stateless biometric sensing system and a "smart" user token that possesses biometric templates of the user. The protocol yields a shared secret incorporating random nonce from both parties when they positively authenticate each other. Mutual positive authentication here is defined as when the feature vector calculated from the sensor data captured by the biometric sensing system only differs from the feature vector stored as the biometric template within the user token by less than a predefined threshold. The parties exchange only randomized data and cryptographically derived verifiers; no significant information regarding the vectors is ever exchanged. The protocol essentially utilizes the BBKDF scheme for feature vector matching, and as a result, the threshold is compared per component of the two vectors to be matched. This fact makes it straightforward to employ multiple biometric modalities. The protocol also allows online authentication where the biometric sensing system can potentially send multiple queries derived from different sensor data samples, in one or more rounds. The protocol is designed in such a way that the user token can very efficiently answer a multitude of such queries. This makes the protocol especially suitable for interactive systems while posing a minimal computational burden on the user token. The biometric sensing system can be made stateless, i.e. user registration in advance is not required. Furthermore, the protocol is bidirectionally privacy-preserving in the sense that unless mutual authentication is achieved first, neither the biometric sensing system, nor the user token can gain useful information, respectively regarding the biometric template, or sensor-data-derived feature vectors.