FP-Agent: Fingerprinting AI Browsing Agents

2026-05-02

AI Summary

This study addresses the challenge of effectively distinguishing AI-powered browsing agents from human users, a task where existing detection mechanisms fall short. The authors construct honeypot websites to collect browser fingerprints and fine-grained behavioral data—including keystrokes, scrolling, and mouse interactions—from seven prominent AI agents and human participants performing typical web tasks. They propose FP-Agent, a multiclass classifier trained on this multimodal dataset, which leverages behavioral fingerprints to differentiate both between AI agents and humans and among distinct AI agent types. This work is the first to systematically demonstrate the critical role of behavioral fingerprints in AI agent identification, overcoming the limitations of approaches relying solely on traditional browser fingerprinting. Experimental results show that FP-Agent accurately identifies all seven AI agent categories, substantially outperforming mainstream anti-bot services such as Cloudflare, which detect only one type, thereby affirming the decisive value of behavioral fingerprints in AI agent 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 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.

Problem

• AI browsing agents
• browser fingerprinting
• behavioral fingerprinting
• bot detection
• web traffic

Innovation

• behavioral fingerprinting
• AI browsing agents
• bot detection
• browser fingerprinting
• FP-Agent