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 - 聂微东
GbyAI
GbyAI
S
SegmentFault 最新的问题
H
Hackread – Cybersecurity News, Data Breaches, AI and More
V
Visual Studio Blog
WordPress大学
WordPress大学
Hugging Face - Blog
Hugging Face - Blog
B
Blog
宝玉的分享
宝玉的分享
Last Week in AI
Last Week in AI
雷峰网
雷峰网
爱范儿
爱范儿
Vercel News
Vercel News
人人都是产品经理
人人都是产品经理
U
Unit 42
Microsoft Azure Blog
Microsoft Azure Blog
Microsoft Security Blog
Microsoft Security Blog
Jina AI
Jina AI
P
Proofpoint News Feed
A
About on SuperTechFans
I
InfoQ
F
Fortinet All Blogs
L
LangChain Blog
T
Tailwind CSS 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
Towards Browser Controls to Protect Cookies from Maliciou...
Liam Tyler, Ivan De Oliveira Nunes · 2024-05-11 · via cs.CR updates on arXiv.org

Cookies maintain state across related web traffic. As such, cookies are commonly used for authentication by storing a user's session ID and replacing the need to re-enter credentials in subsequent traffic. These so-called ``session cookies'' are prime targets for attacks that aim to steal them to gain unauthorized access to user accounts. To mitigate these attacks, the Secure and HttpOnly cookie attributes limit a cookie's accessibility from malicious networks and websites. However, these controls overlook browser extensions: third-party HTML/JavaScript add-ons with access to privileged browser APIs and the ability to operate across multiple websites. Thus malicious or compromised extensions can provide unrestricted access to a user's session cookies. In this work, we first analyze the prevalence of extensions with access to ``risky'' APIs (those that enable cookie modification and theft) and find that they have hundreds of millions of users. Motivated by this, we propose a mechanism to protect cookies from malicious extensions by introducing two new cookie attributes: BrowserOnly and Monitored. The BrowserOnly attribute prevents extension access to cookies altogether. While effective, not all cookies can be made inaccessible. Thus cookies with the Monitored attribute remain accessible but are tied to a single browser and any changes made to the cookie are logged. As a result, stolen Monitored cookies are unusable outside their original browser and servers can validate the modifications performed. To demonstrate the proposed functionalities, we design and implement CREAM (Cookie Restrictions for Extension Abuse Mitigation) a modified version of the open-source Chromium browser realizing these controls. Our evaluation indicates that CREAM effectively protects cookies from malicious extensions while incurring little run-time overheads.