An AI agent can answer a normal customer question correctly and still fail when a hostile instruction is hidden in a document, web page, email, or tool response it reads. That makes prompt injection testing different from checking whether a chatbot gives the right answer. You need to test what the agent trusts, what data it can reach, and what actions it may take after reading untrusted content.<\/p>\r\n\r\n\r\n\r\n
This guide gives you a practical audit you can run before launch and repeat after meaningful changes. It focuses on observable outcomes: whether the agent follows the user\u2019s legitimate task, keeps data within its intended boundary, and stops before an unauthorized or irreversible action.<\/p>\r\n\r\n\r\n\r\n
\r\nCore idea:<\/strong> Treat every instruction found inside retrieved content as untrusted data unless the application explicitly authorizes it. Test the whole path from input to tool action, not just the model\u2019s final wording.<\/p>\r\n<\/blockquote>\r\n\r\n\r\n\r\n