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

IT之家
IT之家
博客园 - 聂微东
雷峰网
雷峰网
Microsoft Azure Blog
Microsoft Azure Blog
WordPress大学
WordPress大学
Hugging Face - Blog
Hugging Face - Blog
S
SegmentFault 最新的问题
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
T
Tailwind CSS Blog
博客园 - 三生石上(FineUI控件)
V
Visual Studio Blog
博客园 - 司徒正美
爱范儿
爱范儿
月光博客
月光博客
阮一峰的网络日志
阮一峰的网络日志
博客园_首页
博客园 - 【当耐特】
Jina AI
Jina AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
酷 壳 – CoolShell
酷 壳 – CoolShell
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
人人都是产品经理
人人都是产品经理
V
V2EX

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
SightSteeple: Agreeing to Disagree with Functional Blockc...
Aditya Ahuja · 2022-05-02 · via cs.CR updates on arXiv.org

Classical and contemporary distributed consensus protocols, may they be for binary agreement, state machine replication, or blockchain consensus, require all protocol participants in a peer-to-peer system to agree on exactly the same information as part of the consensus payload. Although this model of consensus is extensively studied, and is useful for most consensus based decentralized applications, it falls short of defining correct distributed systems which mandate participant credential based privileged visibility into the consensus payload, through the consensus protocol itself. We introduce a new paradigm for distributed consensus, called functional blockchain consensus. Functional blockchain consensus allows each blockchain protocol participant to agree on some distinct sub-information of the list of transactions, as a function of the credentials of the participant in the blockchain system, instead of agreeing on the entire list of transactions. We motivate two adversary models, one with a standard crash-fault adversary and another with a novel rational-fault adversary, to compromise functional blockchain consensus. We then present two versions of a blockchain protocol called SightSteeple, that achieves functional blockchain consensus in the said fault models. SightSteeple relies on a novel combination of standard blockchain consensus and functional encryption, among other primitives, to achieve its goals of correctness. Finally, we discuss practical uses of functional blockchain consensus based asymmetric distributed ledgers, and motivate off-shoot constructions that can result from this new consensus paradigm.