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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
Profile-Guided, Multi-Version Binary Rewriting
Xiaozhu Meng, Buddhika Chamith, Ryan Newton · 2020-02-19 · via cs.CR updates on arXiv.org

The static instrumentation of machine code, also known as binary rewriting, is a power technique, but suffers from high runtime overhead compared to compiler-level instrumentation. Recent research has shown that tools can achieve near-to-zero overhead when rewriting binaries (excluding the overhead from the application specific instrumentation). However, the users of binary rewriting tools often have difficulties in understanding why their instrumentation is slow and how to optimize their instrumentation. We are inspired by a traditional program optimization workflow, where one can profile the program execution to identify performance hot spots, modify the source code or apply suitable compiler optimizations, and even apply profile-guided optimization. We present profile-guided, Multi-Version Binary Rewriting to enable this optimization workflow for static binary instrumentation. Our new techniques include three components. First, we augment existing binary rewriting to support call path profiling; one can interactively view instrumentation costs and understand the calling contexts where the costs incur. Second, we present Versioned Structure Binary Editing, which is a general binary transformation technique. Third, we use call path profiles to guide the application of binary transformation. We apply our new techniques to shadow stack and basic block code coverage. Our instrumentation optimization workflow helps us identify several opportunities with regard to code transformation and instrumentation data layout. Our evaluation on SPEC CPU 2017 shows that the geometric overhead of shadow stack and block coverage is reduced from 7.6% and 161.3% to 1.4% and 4.0%, respectively. We also achieve promising results on Apache HTTP Server, where the shadow stack overhead is reduced from about 20% to 3.5%.