FP-Agent: Fingerprinting AI Browsing Agents
- URL: http://arxiv.org/abs/2605.01247v1
- Date: Sat, 02 May 2026 04:58:08 GMT
- Title: FP-Agent: Fingerprinting AI Browsing Agents
- Abstract summary: We present the first controlled measurement study of seven AI browsing agents and human users.<n>We find that browser fingerprints provide limited discriminative power when shared by multiple AI browsing agents.<n> Behavioral fingerprints, however, are distinctive: differences in typing, scrolling, and mouse behavior separate AI browsing agents from humans and one another.
- Score: 11.678899044315243
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- On the Internet, Nobody Knows You're an LLM Bot: Unmasking Web Agents with Multi-Layer Fingerprinting [1.4726407245786044]
Since 2023, a new class of bots has emerged: Web Agents.<n>They can automate complex tasks on the Web, going beyond traditional browser automation tools such as Selenium, Puppeteer, or Playwright.<n>It is becoming increasingly difficult for website administrators to detect and block these LLM-based bots.
arXiv Detail & Related papers (2026-06-29T10:51:13Z) - Whose Agent Are You? Multi-Layer Fingerprinting and Attribution of Autonomous Web Agents [14.225575847186592]
We show that AI web agents can be effectively distinguished from humans and traditional crawlers using a multi-layer fingerprint.<n>We implement this mechanism as a programmatic logging framework that can be deployed on a live, instrumented domain.<n>Our findings demonstrate that cross-layer agent tracking provides a robust, evasion-resistant strategy for content protection and web security policy enforcement.
arXiv Detail & Related papers (2026-06-18T20:07:39Z) - WebAgentGuard: A Reasoning-Driven Guard Model for Detecting Prompt Injection Attacks in Web Agents [117.65855863464863]
Web agents are highly vulnerable to prompt injection attacks.<n>Existing defenses, including system prompt defenses and direct fine-tuning of agents, have shown limited effectiveness.<n>We introduce WebAgentGuard, a reasoning-driven, multimodal guard model for prompt injection detection.
arXiv Detail & Related papers (2026-04-14T04:50:35Z) - RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction [51.46634975923564]
This paper proposes the first adversarial multi-agent Reinforcement learning framework for social Bot control attacks (RoBCtrl)<n> Specifically, we use a diffusion model to generate high-fidelity bot accounts by reconstructing existing account data with minor modifications.<n>We then employ a Multi-Agent Reinforcement Learning (MARL) method to simulate bots adversarial behavior.
arXiv Detail & Related papers (2025-10-16T02:41:49Z) - BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions [48.194688161526756]
BrowserAgent operates directly on raw web pages via Playwright through a set of predefined browser actions.<n>We introduce an explicit memory mechanism to store key conclusions across steps, further enhancing the model's reasoning capabilities.
arXiv Detail & Related papers (2025-10-12T15:43:37Z) - A Whole New World: Creating a Parallel-Poisoned Web Only AI-Agents Can See [0.0]
A malicious website can identify an incoming request as originating from an AI agent and dynamically serve a different, "cloaked" version of its content.<n>While human users see a benign webpage, the agent is presented with a visually identical page embedded with hidden, malicious instructions.<n>This work formalizes the threat model, details the mechanics of agent fingerprinting and cloaking, and discusses the profound security implications for the future of agentic AI.
arXiv Detail & Related papers (2025-08-29T08:14:52Z) - Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflows [60.04362496037186]
We present the first controlled study of developer interactions with coding agents.<n>We evaluate two leading copilot and agentic coding assistants.<n>Our results show agents can assist developers in ways that surpass copilots.
arXiv Detail & Related papers (2025-07-10T20:12:54Z) - Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions [9.495142718502072]
Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising.<n>Prior research heavily relied on automated web crawls, which inherently struggle to replicate the nuances of human-computer interactions.<n>This paper presents a user study involving 30 participants over 10 weeks, capturing telemetry data from real browsing sessions across 3,000 top-ranked websites.
arXiv Detail & Related papers (2025-02-03T18:43:34Z) - Beyond Browsing: API-Based Web Agents [58.39129004543844]
API-Based Agents outperform web Browsing Agents in experiments on WebArena.<n>Hybrid Agents out-perform both others nearly uniformly across tasks.<n>Results strongly suggest that when APIs are available, they present an attractive alternative to relying on web browsing alone.
arXiv Detail & Related papers (2024-10-21T19:46:06Z) - Navigates Like Me: Understanding How People Evaluate Human-Like AI in
Video Games [36.96985093527702]
We collect hundreds of crowd-sourced assessments comparing the human-likeness of navigation behavior generated by our agent and baseline AI agents.
Our proposed agent passes a Turing Test, while the baseline agents do not.
This work provides insights into the characteristics that people consider human-like in the context of goal-directed video game navigation.
arXiv Detail & Related papers (2023-03-02T18:59:04Z) - Can Machines Imitate Humans? Integrative Turing-like tests for Language and Vision Demonstrate a Narrowing Gap [56.611702960809644]
We benchmark AI's ability to imitate humans in three language tasks and three vision tasks.<n>Next, we conducted 72,191 Turing-like tests with 1,916 human judges and 10 AI judges.<n>Imitation ability showed minimal correlation with conventional AI performance metrics.
arXiv Detail & Related papers (2022-11-23T16:16:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.