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

A
About on SuperTechFans
有赞技术团队
有赞技术团队
人人都是产品经理
人人都是产品经理
月光博客
月光博客
美团技术团队
博客园 - 聂微东
阮一峰的网络日志
阮一峰的网络日志
WordPress大学
WordPress大学
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
博客园_首页
爱范儿
爱范儿
G
Google Developers Blog
aimingoo的专栏
aimingoo的专栏
T
The Blog of Author Tim Ferriss
MongoDB | Blog
MongoDB | Blog
小众软件
小众软件
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
I
InfoQ
B
Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
大猫的无限游戏
大猫的无限游戏
T
Tailwind CSS Blog
F
Fortinet All Blogs

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
Mending Wall: On the Implementation of Censorship in India
Devashish Gosain, Anshika Agarwal, Sahil Shekhawat, H. B. Achary · 2018-06-18 · via cs.CR updates on arXiv.org

This paper presents a study of the Internet infrastructure in India from the point of view of censorship. First, we show that the current state of affairs---where each ISP implements its own content filters (nominally as per a governmental blacklist)---results in dramatic differences in the censorship experienced by customers. In practice, a well-informed Indian citizen can escape censorship through a judicious choice of service provider. We then consider the question of whether India might potentially follow the Chinese model and institute a single, government-controlled filter. This would not be difficult, as the Indian Internet is quite centralized already. A few "key" ASes (approx 1% of Indian ASes) collectively intercept approx 95% of paths to the censored sites we sample in our study, and also to all publicly-visible DNS servers. 5,000 routers spanning these key ASes would suffice to carry out IP or DNS filtering for the entire country; approx 70% of these routers belong to only two private ISPs. If the government is willing to employ more powerful measures, such as an IP Prefix Hijacking attack, any one of several key ASes can censor traffic for nearly all Indian users. Finally, we demonstrate that such federated censorship by India would cause substantial collateral damage to non-Indian ASes whose traffic passes through Indian cyberspace (which do not legally come under Indian jurisdiction at all).