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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
A Systematic Study on Static Control Flow Obfuscation Tec...
Renuka Kumar, Anjana Mariam Kurian · 2018-09-28 · via cs.CR updates on arXiv.org

Control flow obfuscation (CFO) alters the control flow path of a program without altering its semantics. Existing literature has proposed several techniques; however, a quick survey reveals a lack of clarity in the types of techniques proposed, and how many are unique. What is also unclear is whether there is a disparity in the theory and practice of CFO. In this paper, we systematically study CFO techniques proposed for Java programs, both from papers and commercially available tools. We evaluate 13 obfuscators using a dataset of 16 programs with varying software characteristics, and different obfuscator parameters. Each program is carefully reverse engineered to study the effect of obfuscation. Our study reveals that there are 36 unique techniques proposed in the literature and 7 from tools. Three of the most popular commercial obfuscators implement only 13 of the 36 techniques in the literature. Thus there appears to be a gap between the theory and practice of CFO. We propose a novel classification of the obfuscation techniques based on the underlying component of a program that is transformed. We identify the techniques that are potent against reverse engineering attacks, both from the perspective of a human analyst and an automated program decompiler. Our analysis reveals that majority of the tools do not implement these techniques, thus defeating the protection obfuscation offers. We furnish examples of select techniques and discuss our findings. To the best of our knowledge, we are the first to assemble such a research. This study will be useful to software designers to decide upon the best techniques to use based upon their needs, for researchers to understand the state-of-the-art and for commercial obfuscator developers to develop new techniques.