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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
A Formally Verified Configuration for Hardware Security M...
Riccardo Focardi, Flaminia L. Luccio · 2021-09-28 · via cs.CR updates on arXiv.org

Hardware Security Modules (HSMs) are trusted machines that perform sensitive operations in critical ecosystems. They are usually required by law in financial and government digital services. The most important feature of an HSM is its ability to store sensitive credentials and cryptographic keys inside a tamper-resistant hardware, so that every operation is done internally through a suitable API, and such sensitive data are never exposed outside the device. HSMs are now conveniently provided in the cloud, meaning that the physical machines are remotely hosted by some provider and customers can access them through a standard API. The property of keeping sensitive data inside the device is even more important in this setting as a vulnerable application might expose the full API to an attacker. Unfortunately, in the last 20+ years a multitude of practical API-level attacks have been found and proved feasible in real devices. The latest version of PKCS#11, the most popular standard API for HSMs, does not address these issues leaving all the flaws possible. In this paper, we propose the first secure HSM configuration that does not require any restriction or modification of the PKCS#11 API and is suitable to cloud HSM solutions, where compliance to the standard API is of paramount importance. The configuration relies on a careful separation of roles among the different HSM users so that known API flaws are not exploitable by any attacker taking control of the application. We prove the correctness of the configuration by providing a formal model in the state-of-the-art Tamarin prover and we show how to implement the configuration in a real cloud HSM solution.