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

博客园 - 三生石上(FineUI控件)
博客园 - Franky
GbyAI
GbyAI
B
Blog
WordPress大学
WordPress大学
D
Docker
小众软件
小众软件
月光博客
月光博客
博客园 - 【当耐特】
T
The Blog of Author Tim Ferriss
IT之家
IT之家
腾讯CDC
Engineering at Meta
Engineering at Meta
Vercel News
Vercel News
H
Help Net Security
M
MIT News - Artificial intelligence
L
LangChain Blog
云风的 BLOG
云风的 BLOG
S
SegmentFault 最新的问题
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
美团技术团队
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
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻

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
Measuring Miner Decentralization in Proof-of-Work Blockch...
Sishan Long, Soumya Basu, Emin Gün Sirer · 2022-03-30 · via cs.CR updates on arXiv.org

Proof of work cryptocurrencies began with the promise of a more egalitarian future with a decentralized monetary system with no powerful entities in charge. While this vision is far from realized, these cryptocurrencies are still touted to be much more decentralized than traditional centralized systems. While it is well understood that cryptocurrencies are centralized, it is still unclear what the underlying causes are. This work aims to address this gap and examines some of the forces behind mining centralization. The internals of cryptocurrency mining is very opaque and difficult to study since it traditionally requires forming relationships with miners, who are typically reticent to share internal information about their competitive advantages. This work takes a different approach by combining large-scale statistical techniques with publicly available blockchain data in order to answer previously intractable questions. The crux of our analysis technique is based on the simple observation that some miners can utilize their hashpower more efficiently due to their position in the network. By teasing out that effect, we de-bias the mining power distribution to get a more accurate estimate. Using that de-biased mining power distribution, we can answer questions about the network position of miners in each cryptocurrency network. Finally, during the course of this study, we observed some unusual mining behaviors which we highlight.