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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
Automated CFI Policy Assessment with Reckon
Paul Muntean · 2018-12-20 · via cs.CR updates on arXiv.org

Protecting programs against control-flow hijacking attacks recently has become an arms race between defenders and attackers. While certain defenses, e.g., \textit{Control Flow Integrity} (CFI), restrict the targets of indirect control-flow transfers through static and dynamic analysis, attackers could search the program for available gadgets that fall into the legitimate target sets to bypass the defenses. There are several tools helping both attackers in developing exploits and analysts in strengthening their defenses. Yet, these tools fail to adequately (1) model the deployed defenses, (2) compare them in a head-to-head way, and (3) use program semantic information to help craft the attack and the countermeasures. Control Flow Integrity (CFI) has proved to be one of the promising defenses against control flow hijacks and tons of efforts have been made to improve CFI in various ways in the past decade. However, there is a lack of a systematic assessment of the existing CFI defenses. In this paper, we present Reckon, a static source code analysis tool for assessing state-of-the-art static CFI defenses, by first precisely modeling them and then evaluating them in a unified framework. Reckon helps determine the level of security offered by different CFI defenses, and find usable code gadgets even after the CFI defenses were applied, thus providing an important step towards successful exploits and stronger defenses. We have used Reckon to assess eight state-of-the-art static CFI defenses on real-world programs such as Google's Chrome and Apache Httpd. Reckon provides precise measurements of the residual attack surfaces, and accordingly ranks CFI policies against each other. It also successfully paves the way to construct code reuse attacks and to eliminate the remaining attack surface, by disclosing calltargets under one of the most restrictive CFI defenses.