The Hidden Dangers of Browsing AI Agents

2025-05-19

AI Summary

This work systematically exposes critical security risks in large language model (LLM)-based autonomous web browsing agents, identifying multi-layered systemic vulnerabilities in dynamic content parsing, tool invocation, and user input handling. We propose the first end-to-end, full-stack threat model encompassing planner, executor, and environment interaction layers. To mitigate these risks, we design a defense-in-depth framework comprising input sanitization, strict planner-executor isolation, formal analyzers for action validation, and session-level protection mechanisms. Leveraging white-box analysis, prompt injection detection, strengthened domain validation, and formal verification, we reproduce and validate high-severity vulnerabilities—including prompt injection, domain validation bypass, and credential exfiltration—leading to one CVE disclosure. Our framework delivers practical, deployable countermeasures validated across real-world agent deployments, advancing the security foundations of LLM-based autonomous agents.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Application Domains: Security

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Autonomous browsing agents powered by large language models (LLMs) are increasingly used to automate web-based tasks. However, their reliance on dynamic content, tool execution, and user-provided data exposes them to a broad attack surface. This paper presents a comprehensive security evaluation of such agents, focusing on systemic vulnerabilities across multiple architectural layers. Our work outlines the first end-to-end threat model for browsing agents and provides actionable guidance for securing their deployment in real-world environments. To address discovered threats, we propose a defense in depth strategy incorporating input sanitization, planner executor isolation, formal analyzers, and session safeguards. These measures protect against both initial access and post exploitation attack vectors. Through a white box analysis of a popular open source project, Browser Use, we demonstrate how untrusted web content can hijack agent behavior and lead to critical security breaches. Our findings include prompt injection, domain validation bypass, and credential exfiltration, evidenced by a disclosed CVE and a working proof of concept exploit.

Problem

• Evaluates security risks in LLM-powered autonomous browsing agents
• Proposes defense strategies against web-based attacks on browsing agents
• Demonstrates vulnerabilities like prompt injection and credential exfiltration

Innovation

• Defense in depth strategy with multiple safeguards
• Input sanitization and planner executor isolation
• Formal analyzers and session safeguards integration