BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents

• URL: http://arxiv.org/abs/2511.20597v1
• Date: Tue, 25 Nov 2025 18:28:35 GMT
• Title: BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents
• Authors: Kaiyuan Zhang, Mark Tenenholtz, Kyle Polley, Jerry Ma, Denis Yarats, Ninghui Li,
• Abstract summary: We examine the landscape of prompt injection attacks and synthesize a benchmark of attacks embedded in realistic HTML payloads.<n>Our benchmark goes beyond prior work by emphasizing injections that can influence real-world actions rather than mere text outputs.<n>We propose a multi-layered defense strategy comprising both architectural and model-based defenses.
• Score: 8.923854146974783
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The integration of artificial intelligence (AI) agents into web browsers introduces security challenges that go beyond traditional web application threat models. Prior work has identified prompt injection as a new attack vector for web agents, yet the resulting impact within real-world environments remains insufficiently understood. In this work, we examine the landscape of prompt injection attacks and synthesize a benchmark of attacks embedded in realistic HTML payloads. Our benchmark goes beyond prior work by emphasizing injections that can influence real-world actions rather than mere text outputs, and by presenting attack payloads with complexity and distractor frequency similar to what real-world agents encounter. We leverage this benchmark to conduct a comprehensive empirical evaluation of existing defenses, assessing their effectiveness across a suite of frontier AI models. We propose a multi-layered defense strategy comprising both architectural and model-based defenses to protect against evolving prompt injection attacks. Our work offers a blueprint for designing practical, secure web agents through a defense-in-depth approach.

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