AI RESEARCH
Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces
arXiv CS.LG
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ArXi:2605.14786v1 Announce Type: cross As LLM-based agents increasingly browse the web on users' behalf, a natural question arises: can websites passively identify which underlying model powers an agent? Doing so would represent a significant security risk, enabling targeted attacks tailored to known model vulnerabilities. Across 14 frontier LLMs and four web environments spanning information retrieval and shopping tasks, we show that an agent's actions and interaction timings, captured via a passive JavaScript tracker, are sufficient to identify the underlying model with up to 96\% F1.