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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
How Memory-Safe is IoT? Assessing the Impact of Memory-Pr...
Vadim Safronov, Ionut Bostan, Nicholas Allott, Andrew Martin · 2024-11-03 · via cs.CR updates on arXiv.org

The rapid development of the Internet of Things (IoT) has enabled novel user-centred applications, including many in safety-critical areas such as healthcare, smart environment security, and emergency response systems. The diversity in IoT manufacturers, standards, and devices creates a combinatorial explosion of such deployment scenarios, leading to increased security and safety threats due to the difficulty of managing such heterogeneity. In almost every IoT deployment, wireless gateways are crucial for interconnecting IoT devices and providing services, yet they are vulnerable to external threats and serve as key entry points for large-scale IoT attacks. Memory-based vulnerabilities are among the most serious threats in software, with no universal solution yet available. Legacy memory protection mechanisms, such as canaries, RELRO, NX, and Fortify, have enhanced memory safety but remain insufficient for comprehensive protection. Emerging technologies like ARM-MTE, CHERI, and Rust are based on more universal and robust Secure-by-Design (SbD) memory safety principles, yet each entails different trade-offs in hardware or code modifications. Given the challenges of balancing security levels with associated overheads in IoT systems, this paper explores the impact of memory safety on the IoT domain through an empirical large-scale analysis of memory-related vulnerabilities in modern wireless gateways. Our results show that memory vulnerabilities constitute the majority of IoT gateway threats, underscoring the necessity for SbD solutions, with the choice of memory-protection technology depending on specific use cases and associated overheads.