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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
CompartOS: CHERI Compartmentalization for Embedded Systems
Hesham Almatary, Michael Dodson, Jessica Clarke, Peter Rugg, Iva · 2022-06-07 · via cs.CR updates on arXiv.org

Existing high-end embedded systems face frequent security attacks. Software compartmentalization is one technique to limit the attacks' effects to the compromised compartment and not the entire system. Unfortunately, the existing state-of-the-art embedded hardware-software solutions do not work well to enforce software compartmentalization for high-end embedded systems. MPUs are not fine-grained and suffer from significant scalability limitations as they can only protect a small and fixed number of memory regions. On the other hand, MMUs suffer from non-determinism and coarse-grained protection. This paper introduces CompartOS as a lightweight linkage-based compartmentalization model for high-end, complex, mainstream embedded systems. CompartOS builds on CHERI, a capability-based hardware architecture, to meet scalability, availability, compatibility, and fine-grained security goals. Microbenchmarks show that CompartOS' protection-domain crossing is 95% faster than MPU-based IPC. We applied the CompartOS model, with low effort, to complex existing systems, including TCP servers and a safety-critical automotive demo. CompartOS not only catches 10 out of 13 FreeRTOS-TCP published vulnerabilities that MPU-based protection (e.g., uVisor) cannot catch but can also recover from them. Further, our TCP throughput evaluations show that our CompartOS prototype is 52% faster than relevant MPU-based compartmentalization models (e.g., ACES), with a 15% overhead compared to an unprotected system. This comes at an FPGA's LUTs overhead of 10.4% to support CHERI for an unprotected baseline RISC-V processor, compared to 7.6% to support MPU, while CHERI only incurs 1.3% of the registers area overhead compared to 2% for MPU.