BrowseSafe: Understanding and Preventing Prompt Injection Within AI Browser Agents

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.

Key Contributions

• Benchmark of prompt injection attacks embedded in realistic HTML payloads, emphasizing injections that trigger real-world actions with distractor frequency matching production environments
• Comprehensive empirical evaluation of existing defenses against prompt injection across multiple frontier AI models
• Multi-layered BrowseSafe defense strategy combining architectural and model-based defenses for a defense-in-depth approach to secure web agents

🛡️ Threat Analysis

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