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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
MESH: A Memory-Efficient Safe Heap for C/C++
Emanuel Q. Vintila, Philipp Zieris, Julian Horsch · 2021-08-19 · via cs.CR updates on arXiv.org

While memory corruption bugs stemming from the use of unsafe programming languages are an old and well-researched problem, the resulting vulnerabilities still dominate real-world exploitation today. Various mitigations have been proposed to alleviate the problem, mainly in the form of language dialects, static program analysis, and code or binary instrumentation. Solutions like AdressSanitizer (ASan) and Softbound/CETS have proven that the latter approach is very promising, being able to achieve memory safety without requiring manual source code adaptions, albeit suffering substantial performance and memory overheads. While performance overhead can be seen as a flexible constraint, extensive memory overheads can be prohibitive for the use of such solutions in memory-constrained environments. To address this problem, we propose MESH, a highly memory-efficient safe heap for C/C++. With its constant, very small memory overhead (configurable up to 2 MB on x86-64) and constant complexity for pointer access checking, MESH offers efficient, byte-precise spatial and temporal memory safety for memory-constrained scenarios. Without jeopardizing the security of safe heap objects, MESH is fully compatible with existing code and uninstrumented libraries, making it practical to use in heterogeneous environments. We show the feasibility of our approach with a full LLVM-based prototype supporting both major architectures, i.e., x86-64 and ARM64, in a Linux runtime environment. Our prototype evaluation shows that, compared to ASan and Softbound/CETS, MESH can achieve huge memory savings while preserving similar execution performance.