惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

H
Help Net Security
爱范儿
爱范儿
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
博客园 - 三生石上(FineUI控件)
大猫的无限游戏
大猫的无限游戏
Hugging Face - Blog
Hugging Face - Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Vercel News
Vercel News
人人都是产品经理
人人都是产品经理
G
Google Developers Blog
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
雷峰网
雷峰网
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Jina AI
Jina AI
博客园 - 叶小钗
D
DataBreaches.Net
D
Docker
月光博客
月光博客
博客园 - 司徒正美
Last Week in AI
Last Week in AI
有赞技术团队
有赞技术团队
腾讯CDC
酷 壳 – CoolShell
酷 壳 – CoolShell

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
Quantum Shadows: The Dining Information Brokers
Theodore Andronikos, Constantinos Bitsakos, Konstantinos Nikas, · 2025-07-18 · via cs.CR updates on arXiv.org

This article introduces the innovative Quantum Dining Information Brokers Problem, presenting a novel entanglement-based quantum protocol to address it. The scenario involves $n$ information brokers, all located in distinct geographical regions, engaging in a metaphorical virtual dinner. The objective is for each broker to share a unique piece of information with all others simultaneously. Unlike previous approaches, this protocol enables a fully parallel, single-step communication exchange among all brokers, regardless of their physical locations. A key feature of this protocol is its ability to ensure both the anonymity and privacy of all participants are preserved, meaning no broker can discern the identity of the sender behind any received information. At its core, the Quantum Dining Information Brokers Problem serves as a conceptual framework for achieving anonymous, untraceable, and massively parallel information exchange in a distributed system. The proposed protocol introduces three significant advancements. First, while quantum protocols for one-to-many simultaneous information transmission have been developed, this is, to the best of our knowledge, one of the first quantum protocols to facilitate many-to-many simultaneous information exchange. Second, it guarantees complete anonymity and untraceability for all senders, a critical improvement over sequential applications of one-to-many protocols, which fail to ensure such robust anonymity. Third, leveraging quantum entanglement, the protocol operates in a fully distributed manner, accommodating brokers in diverse spatial locations. This approach marks a substantial advancement in secure, scalable, and anonymous communication, with potential applications in distributed environments where privacy and parallelism are paramount.