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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
Drndalo: Lightweight Control Flow Obfuscation Through Min...
Novak Boskov, Mihailo Isakov, Michel A. Kinsy · 2019-11-29 · via cs.CR updates on arXiv.org

Binary analysis is traditionally used in the realm of malware detection. However, the same technique may be employed by an attacker to analyze the original binaries in order to reverse engineer them and extract exploitable weaknesses. When a binary is distributed to end users, it becomes a common remotely exploitable attack point. Code obfuscation is used to hinder reverse engineering of executable programs. In this paper, we focus on securing binary distribution, where attackers gain access to binaries distributed to end devices, in order to reverse engineer them and find potential vulnerabilities. Attackers do not however have means to monitor the execution of said devices. In particular, we focus on the control flow obfuscation --- a technique that prevents an attacker from restoring the correct reachability conditions for the basic blocks of a program. By doing so, we thwart attackers in their effort to infer the inputs that cause the program to enter a vulnerable state (e.g., buffer overrun). We propose a compiler extension for obfuscation and a minimal hardware modification for dynamic deobfuscation that takes advantage of a secret key stored in hardware. We evaluate our experiments on the LLVM compiler toolchain and the BRISC-V open source processor. On PARSEC benchmarks, our deobfuscation technique incurs only a 5\% runtime overhead. We evaluate the security of Drndalo by training classifiers on pairs of obfuscated and unobfuscated binaries. Our results shine light on the difficulty of producing obfuscated binaries of arbitrary programs in such a way that they are statistically indistinguishable from plain binaries.