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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
On White-box Cryptography and Obfuscation
Amitabh Saxena, Brecht Wyseur · 2008-05-30 · via cs.CR updates on arXiv.org

We study the relationship between obfuscation and white-box cryptography. We capture the requirements of any white-box primitive using a \emph{White-Box Property (WBP)} and give some negative/positive results. Loosely speaking, the WBP is defined for some scheme and a security notion (we call the pair a \emph{specification}), and implies that w.r.t. the specification, an obfuscation does not leak any ``useful'' information, even though it may leak some ``useless'' non-black-box information. Our main result is a negative one - for most interesting programs, an obfuscation (under \emph{any} definition) cannot satisfy the WBP for every specification in which the program may be present. To do this, we define a \emph{Universal White-Box Property (UWBP)}, which if satisfied, would imply that under \emph{whatever} specification we conceive, the WBP is satisfied. We then show that for every non-approximately-learnable family, there exist (contrived) specifications for which the WBP (and thus, the UWBP) fails. On the positive side, we show that there exists an obfuscator for a non-approximately-learnable family that achieves the WBP for a certain specification. Furthermore, there exists an obfuscator for a non-learnable (but approximately-learnable) family that achieves the UWBP. Our results can also be viewed as formalizing the distinction between ``useful'' and ``useless'' non-black-box information.