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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
Cryptography: Against AI and QAI Odds
Sheetal Harris, Hassan Jalil Hadi, Umer Zukaib · 2023-09-13 · via cs.CR updates on arXiv.org

Artificial Intelligence (AI) presents prodigious technological prospects for development, however, all that glitters is not gold! The cyber-world faces the worst nightmare with the advent of AI and quantum computers. Together with Quantum Artificial Intelligence (QAI), they pose a catastrophic threat to modern cryptography. It would also increase the capability of cryptanalysts manifold, with its built-in persistent and extensive predictive intelligence. This prediction ability incapacitates the constrained message space in device cryptography. With the comparison of these assumptions and the intercepted ciphertext, the code-cracking process will considerably accelerate. Before the vigorous and robust developments in AI, we have never faced and never had to prepare for such a plaintext-originating attack. The supremacy of AI can be challenged by creating ciphertexts that would give the AI attacker erroneous responses stymied by randomness and misdirect them. AI threat is deterred by deviating from the conventional use of small, known-size keys and pattern-loaded ciphers. The strategy is vested in implementing larger secret size keys, supplemented by ad-hoc unilateral randomness of unbound limitations and a pattern-devoid technique. The very large key size can be handled with low processing and computational burden to achieve desired unicity distances. The strategy against AI odds is feasible by implementing non-algorithmic randomness, large and inexpensive memory chips, and wide-area communication networks. The strength of AI, i.e., randomness and pattern detection can be used to generate highly optimized ciphers and algorithms. These pattern-devoid, randomness-rich ciphers also provide a timely and plausible solution for NIST's proactive approach toward the quantum challenge.