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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
Devil is Virtual: Reversing Virtual Inheritance in C++ Bi...
Rukayat Ayomide Erinfolami, Aravind Prakash · 2020-03-11 · via cs.CR updates on arXiv.org

Complexities that arise from implementation of object-oriented concepts in C++ such as virtual dispatch and dynamic type casting have attracted the attention of attackers and defenders alike. Binary-level defenses are dependent on full and precise recovery of class inheritance tree of a given program. While current solutions focus on recovering single and multiple inheritances from the binary, they are oblivious to virtual inheritance. Conventional wisdom among binary-level defenses is that virtual inheritance is uncommon and/or support for single and multiple inheritances provides implicit support for virtual inheritance. In this paper, we show neither to be true. Specifically, (1) we present an efficient technique to detect virtual inheritance in C++ binaries and show through a study that virtual inheritance can be found in non-negligible number (more than 10\% on Linux and 12.5\% on Windows) of real-world C++ programs including Mysql and libstdc++. (2) we show that failure to handle virtual inheritance introduces both false positives and false negatives in the hierarchy tree. These false positves and negatives either introduce attack surface when the hierarchy recovered is used to enforce CFI policies, or make the hierarchy difficult to understand when it is needed for program understanding (e.g., during decompilation). (3) We present a solution to recover virtual inheritance from COTS binaries. We recover a maximum of 95\% and 95.5\% (GCC -O0) and a minimum of 77.5\% and 73.8\% (Clang -O2) of virtual and intermediate bases respectively in the virtual inheritance tree.