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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
Diversity-By-Design for Dependable and Secure Cyber-Physi...
Qisheng Zhang, Abdullah Zubair Mohammed, Zelin Wan, Jin-Hee Cho, · 2020-07-17 · via cs.CR updates on arXiv.org

Diversity-based security approaches have been studied for several decades since the 1970's. The concept of diversity-by-design emerged in the 1980's and, since then, diversity-based system design research has been explored to build more secure and dependable systems. In this work, we are particularly interested in providing an in-depth, comprehensive survey of existing diversity-based approaches, insights, and future work directions for those who want to conduct research on developing secure and dependable cyber-physical systems (CPSs) using diversity as a system design feature. To be specific, this survey paper provides: (i) The common concept of diversity based on a multidisciplinary study of diversity from nine different fields along with the historical evolution of diversity-by-design for security; (ii) The design principles of diversity-based approaches; (iii) The key benefits and caveats of using diversity-by-design; (iv) The key concerns of CPS environments in introducing diversity-by-design; (v) A variety of existing diversity-based approaches based on five different classifications; (vi) The types of attacks mitigated by existing diversity-based approaches; (vii) The overall trends of evaluation methodologies used in diversity-based approaches, in terms of metrics, datasets, and testbeds; and (viii) The insights, lessons, and gaps identified from this extensive survey.