arXiv AI
Whose Agent Are You? Multi-Layer Fingerprinting and Attribution of Autonomous Web Agents
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arXiv AI
Sep 4
Identifying AI Web Scrapers Using Canary Tokens
The paper introduces a method to detect which web scrapers feed data to large language models (LLMs) by deploying dynamic websites that issue unique canary tokens to each scraper. By querying LLMs for information about these sites, the authors can identify when an LLM consistently outputs the unique tokens, indicating exposure to a specific scraper. Experiments on 22 production LLM systems show the technique reliably uncovers both known and undisclosed scrapers, offering a tool for third parties to monitor and control unwanted web scraping.
arXiv AI
4d ago
Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces
The paper demonstrates that LLM-based browser agents can be identified by passive JavaScript tracking of their UI actions and timings, achieving up to 96% F1 within a single harness and 0.74–0.81 Macro‑F1 when pooling across harnesses. Identification is possible with only about one‑third of the traces and improves as more events are observed. Randomised timing delays reduce but do not eliminate classifier performance, and retraining on delayed data largely restores accuracy.
arXiv AI
Jun 15
Same-Origin Policy for Agentic Browsers
arXiv:2606. 14027v1 Announce Type: cross Abstract: Agentic browsers integrate autonomous AI agents into web browsers, enabling users to accomplish web tasks through natural-language instructions.
arXiv AI
Jul 22
Broken Gates: Re-evaluating Web Bot Defenses in the Age of LLM Agents
arXiv:2607. 18659v1 Announce Type: cross Abstract: LLM-based browser agents are rapidly changing the threat landscape for web security.
arXiv AI
Aug 13
BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents
arXiv:2511. 20597v2 Announce Type: replace-cross Abstract: The integration of artificial intelligence (AI) agents into web browsers introduces security challenges that go beyond traditional web application threat models.
Hugging Face Trending Papers
Jul 21
Broken Gates: Re-evaluating Web Bot Defenses in the Age of LLM Agents
LLM-based browser agents are rapidly changing the threat landscape for web security. Unlike traditional automation frameworks that execute predefined scripts, these agents can autonomously navigate websites, reason about page content, and interact with web interfaces using natural-language instructions.