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

Y
Y Combinator Blog
Jina AI
Jina AI
雷峰网
雷峰网
有赞技术团队
有赞技术团队
WordPress大学
WordPress大学
美团技术团队
V
V2EX
酷 壳 – CoolShell
酷 壳 – CoolShell
小众软件
小众软件
博客园 - Franky
博客园 - 三生石上(FineUI控件)
月光博客
月光博客
博客园 - 叶小钗
大猫的无限游戏
大猫的无限游戏
爱范儿
爱范儿
Hugging Face - Blog
Hugging Face - Blog
宝玉的分享
宝玉的分享
Last Week in AI
Last Week in AI
Apple Machine Learning Research
Apple Machine Learning Research
量子位
IT之家
IT之家
人人都是产品经理
人人都是产品经理
博客园_首页
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

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
Privatizing user credential information of Web services i...
Pinaki Mitra, Rinku Das, Girish Sundaram · 2013-08-16 · via cs.CR updates on arXiv.org

User credentials security is one of the most important tasks in Web World. Most Web sites on the Internet that support user accounts store the users credentials in a database. Now a days, most of the web browsers offer auto login feature for the favorite web sites such as yahoo, google, gmail etc. using these credential information. This facilitates the misuse of user credentials. Privatizing user credential information of web services in a shared user environment provides a feature enhancement where the root user will be able to privatize his stored credentials by enforcing some masking techniques such that even a user logs on to the system with root user credentials, he will not be able to access privatized data. In case of web browsers auto login feature, a root user can disable the feature manually by deleting entries from web browsers' saved password list. But this involves spending a considerable amount of time and the biggest problem is that he has to insert those credentials once again when he next visits these websites. This application resumes auto login feature whenever root user disable the masked mode. The application includes two parts: Masked Application Mode and Disabling the Masked Application Mode. When the system goes for masked application mode, the other user will not be able to use the credentials of the root user.If the other user tries to access any of the web pages which have been masked, the other user will have to authenticate with his own credentials. Disabling the masked mode requires authentication from the root user. As long as this credential is not shared, masked mode can be disabled only by the root user.