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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
Towards Automated Augmentation and Instrumentation of Leg...
Karim Eldefrawy, Michael Locasto, Norrathep Rattanavipanon, Hass · 2020-04-21 · via cs.CR updates on arXiv.org

Implementation flaws in cryptographic libraries, design flaws in underlying cryptographic primitives, and weaknesses in protocols using both, can all lead to exploitable vulnerabilities in software. Manually fixing such issues is challenging and resource consuming, especially when maintaining legacy software that contains broken or outdated cryptography, and for which source code may not be available. While there is existing work on identifying cryptographic primitives (often in the context of malware analysis), none of this prior work has focused on replacing such primitives with stronger (or more secure ones) after they have been identified. This paper explores feasibility of designing and implementing a toolchain for Augmentation and Legacy-software Instrumentation of Cryptographic Executables (ALICE). The key features of ALICE are: (i) automatically detecting and extracting implementations of weak or broken cryptographic primitives from binaries without requiring source code or debugging symbols, (ii) identifying the context and scope in which such primitives are used, and performing program analysis to determine the effects of replacing such implementations with more secure ones, and (iii) replacing implementations of weak primitives with those of stronger or more secure ones. We demonstrate practical feasibility of our approach on cryptographic hash functions with several popular cryptographic libraries and real-world programs of various levels of complexity. Our experimental results show that ALICE can locate and replace insecure hash functions, even in large binaries (we tested ones of size up to 1.5MB), while preserving existing functionality of the original binaries, and while incurring minimal execution-time overhead in the rewritten binaries. We also open source ALICE's code at https://github.com/SRI-CSL/ALICE.