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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
Arbitrary-Threshold Fully Homomorphic Encryption with Low...
Yijia Chang, Songze Li · 2025-01-20 · via cs.CR updates on arXiv.org

Threshold fully homomorphic encryption (ThFHE) enables multiple parties to compute functions over their sensitive data without leaking data privacy. Most of existing ThFHE schemes are restricted to full threshold and require the participation of \textit{all} parties to output computing results. Compared with these full-threshold schemes, arbitrary threshold (ATh)-FHE schemes are robust to non-participants and can be a promising solution to many real-world applications. However, existing AThFHE schemes are either inefficient to be applied with a large number of parties $N$ and a large data size $K$, or insufficient to tolerate all types of non-participants. In this paper, we propose an AThFHE scheme to handle all types of non-participants with lower complexity over existing schemes. At the core of our scheme is the reduction from AThFHE construction to the design of a new primitive called \textit{approximate secret sharing} (ApproxSS). Particularly, we formulate ApproxSS and prove the correctness and security of AThFHE on top of arbitrary-threshold (ATh)-ApproxSS's properties. Such a reduction reveals that existing AThFHE schemes implicitly design ATh-ApproxSS following a similar idea called ``noisy share''. Nonetheless, their ATh-ApproxSS design has high complexity and become the performance bottleneck. By developing ATASSES, an ATh-ApproxSS scheme based on a novel ``encrypted share'' idea, we reduce the computation (resp. communication) complexity from $\mathcal{O}(N^2K)$ to $\mathcal{O}(N^2+K)$ (resp. from $\mathcal{O}(NK)$ to $\mathcal{O}(N+K)$). We not only theoretically prove the (approximate) correctness and security of ATASSES, but also empirically evaluate its efficiency against existing baselines. Particularly, when applying to a system with one thousand parties, ATASSES achieves a speedup of $3.83\times$ -- $15.4\times$ over baselines.