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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Full-Blind Delegating Private Quantum Computation
Wen-Jie Liu, Zhen-Yu Chen, Jin-Suo Liu, Zhao-Feng Su, Lian-Hua C · 2020-02-03 · via cs.CR updates on arXiv.org

The delegating private quantum computation (DQC) protocol with the universal quantum gate set $\left\{ {X,Z,H,P,R,CNOT} \right\}$ was firstly proposed by Broadbent \emph{et al.}, and then Tan \emph{et al.} tried to put forward an half-blind DQC protocol (HDQC) with another universal set $\left\{ {H,P,CNOT,T} \right\}$. However, the decryption circuit of \emph{Toffoli} gate (i.e., \emph{T}) is a little redundant, and Tan \emph{et al}.'s protocol exists the information leak. In addition, both of these two protocols just focus on the blindness of data (i.e., the client's input and output), but do not consider the blindness of computation (i.e., the delegated quantum operation). For solving these problems, we propose a full-blind DQC protocol (FDQC) with quantum gate set $\left\{ {H,P,CNOT,T} \right\}$ , where the desirable delegated quantum operation, one of $\left\{ {H,P,CNOT,T} \right\}$ , is replaced by a fixed sequence $\left \{ {H,P,T,CZ,CNOT} \right\}$ to make the computation blind, and the decryption circuit of \emph{Toffoli} gate is also optimized. Analysis shows that our protocol can not only correctly perform any delegated quantum computation, but also holds the characteristics of data blindness and computation blindness.