FP-Agent: Fingerprinting AI Browsing Agents
For web administrators and security systems, this work provides a method to detect AI browsing agents, which are increasingly used but hard to distinguish from humans.
The paper presents the first controlled measurement study of seven AI browsing agents and humans, finding that behavioral fingerprints (typing, scrolling, mouse behavior) can reliably distinguish AI agents from humans and from each other, while browser fingerprints are limited. FP-Agent detects all seven agents, whereas Cloudflare detects only one.
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 power when shared by multiple AI browsing agents. Behavioral fingerprints, however, are distinctive: differences in typing, scrolling, and mouse behavior separate AI browsing agents from humans and one another. In a case study evaluating Cloudflare's bot detection, FP-Agent detects all seven AI browsing agents, whereas Cloudflare detects only one. Our findings show that behavioral fingerprints are a critical component to reliably detect and control this emerging form of web traffic.