← Back to all news
arXiv AI By William Lugoloobi, Samuele Marro, Jabez Magomere, Joss Wright, Chris Russell

Known By Their Actions: Fingerprinting LLM Browser Agents via UI Traces

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Jun 16

MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks

arXiv:2602. 09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf.

By Georgios Syros, Evan Rose, Brian Grinstead, Christoph Kerschbaumer, William Robertson, Cristina Nita-Rotaru, Alina Oprea
llmsagentssafety
More like this →
arXiv Computation and Language
Nov 2

How Much Can Tool Calls Reveal About You? Privacy Leakage from Tool-Call Traces

The paper investigates how tool-call traces from large language model agents can reveal user attributes. Using the PrivacyTrace benchmark, the authors show that an attacker can infer user profiles with a 26.25% success rate from tool-call data, outperforming prior guessing methods. The study highlights that tool arguments and results are key evidence, and linking traces across sessions increases profiling risk.

By Donghang Duan, Qingyu Liu, Yifei Liu, Na Zou, Xia Hu, Dongrui Liu
llmsagentsbenchmarkssafety
More like this →
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.

By Steven Seiden, Triss Ren, Caroline Zhang, Taein Kim, Enze Liu, Emily Wenger
llmsagentsroboticssafety
More like this →