How Indirect Prompt Injection Attacks Hijack AI Agents via Web Content

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Indirect Prompt Injection (IPI) is an emerging cybersecurity threat targeting autonomous AI agents powered by Large Language Models and tools like the Model Context Protocol (MCP). Unlike direct prompt injection, IPI occurs when an agent ingests malicious instructions hidden within external data sources such as scraped webpages, API responses, or document feeds. Because LLMs share the same context space for both instructions and data, there is no built-in separation to distinguish trusted commands from hostile content embedded in the environment. This mirrors the classic Cross-Site Scripting (XSS) vulnerability in web development, where a browser cannot distinguish a trusted script from a malicious one injected via user content. Defending against IPI requires a layered, defense-in-depth approach that addresses how agentic systems consume, trust, and act upon unvetted external data.

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