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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
CryptSan: Leveraging ARM Pointer Authentication for Memor...
Konrad Hohentanner, Philipp Zieris, Julian Horsch · 2022-02-17 · via cs.CR updates on arXiv.org

Memory safety bugs remain in the top ranks of security vulnerabilities, even after decades of research on their detection and prevention. Various mitigations have been proposed for C/C++, ranging from language dialects to instrumentation. Among these, compiler-based instrumentation is particularly promising, not requiring manual code modifications and being able to achieve precise memory safety. Unfortunately, existing compiler-based solutions compromise in many areas, including performance but also usability and memory safety guarantees. New developments in hardware can help improve performance and security of compiler-based memory safety. ARM Pointer Authentication, added in the ARMv8.3 architecture, is intended to enable hardware-assisted Control Flow Integrity (CFI). But since its operations are generic, it also enables other, more comprehensive hardware-supported runtime integrity approaches. As such, we propose CryptSan, a memory safety approach based on ARM Pointer Authentication. CryptSan uses pointer signatures to retrofit memory safety to C/C++ programs, protecting heap, stack, and globals against temporal and spatial vulnerabilities. We present a full LLVM-based prototype implementation, running on an M1 MacBook Pro, i.e., on actual ARMv8.3 hardware. Our prototype evaluation shows that the system outperforms similar approaches under real-world conditions. This, together with its interoperability with uninstrumented libraries and cryptographic protection against attacks on metadata, makes CryptSan a viable solution for retrofitting memory safety to C/C++ programs.