Indirect Prompt Injection in Web-Browsing Agents

AI agents with web-browsing capabilities are susceptible to indirect prompt injection attacks, where malicious instructions hidden within web pages can be executed when an agent visits and processes those pages. These attacks exploit the agent's ability to fetch and interpret web content, embedding hidden instructions through techniques like invisible text, HTML comments, and semantic embedding. Different AI models, such as Claude and GPT-4.1, have varying vulnerabilities to these techniques, with semantic embedding proving particularly challenging to defend against due to its subtlety. The indirect-web-pwn test harness is designed to evaluate the resilience of AI agents against such attacks by dynamically generating web pages with concealed payloads tailored to the agent's function. These attacks can lead to data exfiltration, where sensitive information is encoded into URLs and sent externally, or behavior manipulation, where the agent is tricked into violating safety protocols. The approach underscores the risks associated with AI agents' interactions with untrusted web content, highlighting the importance of robust testing to mitigate potential security threats.