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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
UA-Radar: Exploring the Impact of User Agents on the Web
2023-11-17 · via cs.CR updates on arXiv.org

In the early days of the web, giving the same web page to different browsers could provide very different results. As the rendering engine behind each browser would differ, some elements of a page could break or be positioned in the wrong location. At that time, the User Agent (UA) string was introduced for content negotiation. By knowing the browser used to connect to the server, a developer could provide a web page that was tailored for that specific browser to remove any usability problems. Over the past three decades, the UA string remained exposed by browsers, but its current usefulness is being debated. Browsers now adopt the exact same standards and use the same languages to display the same content to users, bringing the question if the content of the UA string is still relevant today, or if it is a relic of the past. Moreover, the diversity of means to browse the web has become so large that the UA string is one of the top contributors to tracking users in the field of browser fingerprinting, bringing a sense of urgency to deprecate it. In this paper, our goal is to understand the impact of the UA on the web and if this legacy string is still actively used to adapt the content served to users. We introduce UA-Radar, a web page similarity measurement tool that compares in-depth two web pages from the code to their actual rendering, and highlights the similarities it finds. We crawled 270, 048 web pages from 11, 252 domains using 3 different browsers and 2 different UA strings to observe that 100% of the web pages were similar before any JavaScript was executed, demonstrating the absence of differential serving. Our experiments also show that only a very small number of websites are affected by the lack of UA information, which can be fixed in most cases by updating code to become browser-agnostic. Our study brings some proof that it may be time to turn the page on the UA string and retire it from current web browsers.