What is prompt injection? How hidden text hijacks AI agents

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Prompt injection exploits the fact that language models cannot reliably separate instructions from data, since system prompts, user requests, and fetched content all arrive as text in one channel. Indirect attacks are especially concerning because malicious text hidden in READMEs, issue comments, or tool descriptions can be acted on by an agent without the user ever seeing it. The stakes are higher for agents, which read large amounts of text and can run shell commands, access files, and use the network. Telling a model to ignore malicious content is not a dependable defense, so the article argues for structural safeguards such as least privilege, approval gates for sensitive actions, and scanning content before ingestion. The author concludes that prompt injection is a boundary problem that developers must solve rather than expecting smarter models to resolve it.

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