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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
Zero-Knowledge Proof of Traffic: A Deterministic and Priv...
Ye Tao, Ehsan Javanmardi, Pengfei Lin, Jin Nakazato, Yuze Jiang, · 2023-12-13 · via cs.CR updates on arXiv.org

Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT's cross-verification ratio ranges between 80 % to 96 %, and 80 % of the verification is achieved in 2 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.