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In-Browser LLM-Guided Fuzzing for Real-Time Prompt Injection Testing in Agentic AI Browsers

Authors: Avihay Cohen

Published: 2025-10-15

arXiv ID: 2510.13543v1

Added to Library: 2025-11-11 14:21 UTC

Red Teaming

📄 Abstract

Large Language Model (LLM) based agents integrated into web browsers (often called agentic AI browsers) offer powerful automation of web tasks. However, they are vulnerable to indirect prompt injection attacks, where malicious instructions hidden in a webpage deceive the agent into unwanted actions. These attacks can bypass traditional web security boundaries, as the AI agent operates with the user privileges across sites. In this paper, we present a novel fuzzing framework that runs entirely in the browser and is guided by an LLM to automatically discover such prompt injection vulnerabilities in real time.

🔍 Key Points

  • Introduction of an in-browser fuzzing framework designed to uncover prompt injection vulnerabilities in AI browsers, allowing real-time security testing directly within the browser environment.
  • Utilization of large language models (LLMs) to generate diverse and evolving attack vectors by mutating existing templates, resulting in a more sophisticated and effective fuzzing process.
  • Zero false positive guarantee achieved through a robust action-based detection mechanism that records actual unwanted actions taken by AI agents when faced with malicious content.
  • Identification of specific high-risk features in AI-based browsing assistants, such as summarization and question answering, that are vulnerable to advanced prompt injection attacks.
  • Demonstration of the progressive evasion phenomenon, where initial defenses against prompt injection attacks degrade significantly over iterative testing against adaptive approaches.

💡 Why This Paper Matters

The paper presents a substantial advancement in the security testing of AI-assisted browsers by introducing a novel in-browser, LLM-guided fuzzing framework. This approach allows for real-time, high-fidelity testing of AI agents' vulnerabilities to indirect prompt injections, uncovering weaknesses that traditional defenses might miss. With the rapid integration of AI into everyday tools, understanding and mitigating these vulnerabilities is crucial to enhancing user safety and maintaining trust in automated systems.

🎯 Why It's Interesting for AI Security Researchers

This paper is particularly relevant to AI security researchers due to its novel approach to fuzz testing AI-powered systems. The detailed exploration of prompt injection vulnerabilities in AI agents highlights the pressing need for adaptive security measures. This research not only offers practical implications for improving security frameworks in AI applications but also provides insights into the evolving tactics used by malicious actors. Additionally, the open-source nature of the framework enables collaboration and further research in this crucial area of AI safety.

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