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

Vercel News
Vercel News
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
雷峰网
雷峰网
有赞技术团队
有赞技术团队
罗磊的独立博客
博客园 - 叶小钗
Jina AI
Jina AI
博客园 - 司徒正美
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
T
Tailwind CSS Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
人人都是产品经理
人人都是产品经理
Apple Machine Learning Research
Apple Machine Learning Research
阮一峰的网络日志
阮一峰的网络日志
Microsoft Security Blog
Microsoft Security Blog
大猫的无限游戏
大猫的无限游戏
量子位
MyScale Blog
MyScale Blog
V
Visual Studio Blog
博客园 - 聂微东
The Cloudflare Blog
Engineering at Meta
Engineering at Meta
小众软件
小众软件
宝玉的分享
宝玉的分享

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
State Machine Frameworks for Website Fingerprinting Defen...
Ethan Witwer · 2023-08-11 · via cs.CR updates on arXiv.org

Tor is an anonymity network used by millions of people every day to evade censorship and protect their browsing activity from privacy threats such as mass surveillance. Unfortunately, Tor has been shown to be vulnerable to website fingerprinting attacks, in which an adversary observes the connection between a user and the Tor network and uses features of the encrypted traffic, such as the timing and volume of packets, to identify the websites that are being visited. In response, researchers have proposed a number of defenses against website fingerprinting attacks, and a "circuit padding framework" has been added to the Tor software which supports the implementation of defenses. However, many proposed defenses are not supported by this framework, and no defenses are currently present in Tor. As Arti, a reimplementation of Tor in Rust, is being developed, the issue arises of whether a new state machine framework should be included or if alternative models should instead be considered for future defense implementation. We address this question by using an improved Rust-based state machine framework, Maybenot, to implement three state-of-the-art website fingerprinting defenses. Through our evaluation, we demonstrate the potential of state machine frameworks to support effective defenses, and we highlight important features that they should contain to do so. However, our evaluation also raises uncertainty about the long-term feasibility of state machine frameworks for defense implementation. We recommend enhancements to Maybenot and substantial further evaluation, along with consideration of alternative designs, before any decision is made regarding a mechanism for implementing website fingerprinting defenses in Arti.