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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
Meta-Sealing: A Revolutionizing Integrity Assurance Proto...
Mahesh Vaijainthymala Krishnamoorthy · 2024-10-31 · via cs.CR updates on arXiv.org

The Artificial intelligence in critical sectors-healthcare, finance, and public safety-has made system integrity paramount for maintaining societal trust. Current verification methods for AI systems lack comprehensive lifecycle assurance, creating significant vulnerabilities in deployment of both powerful and trustworthy AI. This research introduces Meta-Sealing, a cryptographic framework that fundamentally changes integrity verification in AI systems throughout their operational lifetime. Meta-Sealing surpasses traditional integrity protocols through its implementation of cryptographic seal chains, establishing verifiable, immutable records for all system decisions and transformations. The framework combines advanced cryptography with distributed verification, delivering tamper-evident guarantees that achieve both mathematical rigor and computational efficiency. Our implementation addresses urgent regulatory requirements for AI system transparency and auditability. The framework integrates with current AI governance standards, specifically the EU's AI Act and FDA's healthcare AI guidelines, enabling organizations to maintain operational efficiency while meeting compliance requirements. Testing on financial institution data demonstrated Meta-Sealing's capability to reduce audit timeframes by 62% while enhancing stakeholder confidence by 47%. Results can establish a new benchmark for integrity assurance in enterprise AI deployments. This research presents Meta-Sealing not merely as a technical solution, but as a foundational framework ensuring AI system integrity aligns with human values and regulatory requirements. As AI continues to influence critical decisions, provides the necessary bridge between technological advancement and verifiable trust. Meta-Sealing serves as a guardian of trust, ensuring that the AI systems we depend on are as reliable and transparent as they are powerful.