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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
SPChain: Blockchain-based Medical Data Sharing and Privac...
2020-09-21 · via cs.CR updates on arXiv.org

The development of eHealth systems has brought great convenience to people's life. Researchers have been combining new technologies to make eHealth systems work better for patients. The Blockchain-based eHealth system becomes popular because of its unique distributed tamper-resistant and privacy-preserving features. However, due to the security issues of the blockchain system, there are many security risks in eHealth systems utilizing the blockchain technology. i.e. 51% attacks can destroy blockchain-based systems. Besides, trivial transactions and frequent calls of smart contracts in the blockchain system bring additional costs and security risks to blockchain-based eHealth systems. Worse still, electronic medical records (EMRs) are controlled by medical institutions rather than patients, which causes privacy leakage issues. In this paper, we propose a medical data Sharing and Privacy-preserving eHealth system based on blockChain technology (SPChain). We combine RepuCoin with the SNARKs-based chameleon hash function to resist underlying blockchain attacks, and design a new chain structure to make microblocks contribute to the weight of blockchain. The system allows patients to share their EMRs among different medical institutions in a privacy-preserving way. Besides, authorized medical institutions can label wrong EMRs with the patients' permissions in the case of misdiagnosis. Security analysis and performance evaluation demonstrate that the proposed system can provide a strong security guarantee with a high efficiency.