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

G
Google Developers Blog
阮一峰的网络日志
阮一峰的网络日志
博客园 - 聂微东
F
Fortinet All Blogs
H
Help Net Security
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
D
DataBreaches.Net
MyScale Blog
MyScale Blog
B
Blog
I
InfoQ
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
GbyAI
GbyAI
Google DeepMind News
Google DeepMind News
IT之家
IT之家
The GitHub Blog
The GitHub Blog
有赞技术团队
有赞技术团队
博客园_首页
L
LangChain Blog
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
T
The Blog of Author Tim Ferriss
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - Franky

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
Charlotte: Composable Authenticated Distributed Data Stru...
Isaac Sheff, Xinwen Wang, Haobin Ni, Robbert van Renesse, Andrew · 2019-05-10 · via cs.CR updates on arXiv.org

We present Charlotte, a framework for composable, authenticated distributed data structures. Charlotte data is stored in blocks that reference each other by hash. Together, all Charlotte blocks form a directed acyclic graph, the blockweb; all observers and applications use subgraphs of the blockweb for their own data structures. Unlike prior systems, Charlotte data structures are composable: applications and data structures can operate fully independently when possible, and share blocks when desired. To support this composability, we define a language-independent format for Charlotte blocks and a network API for Charlotte servers. An authenticated distributed data structure guarantees that data is immutable and self-authenticating: data referenced will be unchanged when it is retrieved. Charlotte extends these guarantees by allowing applications to plug in their own mechanisms for ensuring availability and integrity of data structures. Unlike most traditional distributed systems, including distributed databases, blockchains, and distributed hash tables, Charlotte supports heterogeneous trust: different observers may have their own beliefs about who might fail, and how. Despite heterogeneity of trust, Charlotte presents each observer with a consistent, available view of data. We demonstrate the flexibility of Charlotte by implementing a variety of integrity mechanisms, including consensus and proof of work. We study the power of disentangling availability and integrity mechanisms by building a variety of applications. The results from these examples suggest that developers can use Charlotte to build flexible, fast, composable applications with strong guarantees.