FP-Agent: Fingerprinting AI Browsing Agents
Ethan Wang, Zubair Shafiq, Yash Vekaria

TL;DR
This study evaluates the detectability of AI browsing agents versus humans using browser and behavioral fingerprints, revealing behavioral features are key for distinguishing AI bots from humans.
Contribution
First controlled measurement study analyzing fingerprint features of AI browsing agents and humans, demonstrating behavioral fingerprints' effectiveness in detection.
Findings
Behavioral fingerprints effectively distinguish AI agents from humans.
Browser fingerprints have limited discriminative power among AI agents.
FP-Agent detects all tested AI agents, outperforming Cloudflare's detection.
Abstract
AI browsing agents are an emerging class of AI-powered bots capable of autonomously navigating websites. Unlike traditional web bots, AI browsing agents typically operate using real browsers and perform everyday tasks, making them difficult to detect. Yet little is known about whether existing AI browsing agents can be distinguished from humans and one another based on their browser or behavioral fingerprints. In this paper, we present the first controlled measurement study of seven AI browsing agents and human users. Using an instrumented honey website, we collect browser and behavioral fingerprint features while AI browsing agents and humans perform three tasks: flight booking, online shopping, and forum interaction. We then train FP-Agent, a multi-class classifier, to evaluate the discriminative power of these features. We find that browser fingerprints provide limited discriminative…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
