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

L
LangChain Blog
B
Blog RSS Feed
阮一峰的网络日志
阮一峰的网络日志
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
H
Help Net Security
MyScale Blog
MyScale Blog
WordPress大学
WordPress大学
Microsoft Azure Blog
Microsoft Azure Blog
GbyAI
GbyAI
小众软件
小众软件
大猫的无限游戏
大猫的无限游戏
Martin Fowler
Martin Fowler
Vercel News
Vercel News
S
SegmentFault 最新的问题
M
MIT News - Artificial intelligence
Microsoft Security Blog
Microsoft Security Blog
G
Google Developers Blog
Last Week in AI
Last Week in AI
Hugging Face - Blog
Hugging Face - Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 【当耐特】
Google DeepMind News
Google DeepMind News
Engineering at Meta
Engineering at Meta
云风的 BLOG
云风的 BLOG

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
A Security Analysis of Browser Extensions
Abhay Rana, Rushil Nagda · 2014-03-13 · via cs.CR updates on arXiv.org

Browser Extensions (often called plugins or addons) are small pieces of code that let developers add additional functionality to the browser. However, with extensions comes a security price: the user must trust the developer. We look at ways in which this trust can be broken and malicious extensions installed. We also look at silent installations of plugins in various browsers and work on ways to make silent installations possible in browsers that work against it. We compare the browser extension mechanism among various browsers, and try to create a set of rules to maintain the principle of least privileges in the browser. We track various plugins and determine whether the least privileges required match with the privileges asked for. We also work on a survey of extensions (for various browsers) and determine the nature of attacks possible. For eg, if a developer account gets hacked, updating of a normal extension with a malicious one is possible. We look at privilege abuse and survey extensions that ask for more privileges than they use. We finally provide a solution and allow a person to check the authenticity of the extension even before they download it.