If you don't red-team your LLM app, your users will

Security Eval and Red-Teaming: Prompt Injection, Data Exfiltration, Jailbreaks, and Agent Abuse The lifecycle of an AI application usually starts with magic and ends in a mild panic. You build a sleek Retrieval-Augmented Generation (RAG) agent, test it on a dozen standard queries, and marvel at its fluid responses. But the moment you deploy it to production, the real testing begins. Within hours, a user will inevitably try to make your customer support bot write a pirate-themed poem, leak its system instructions, or worse, offer a 99% discount on your flagship product. Deploying an LLM application is remarkably easy, but securing it is notoriously hard. Because large language models process inputs in which instructions and data are fundamentally intertwined, traditional security paradigms—such as strict input sanitization—fall short. If your security evaluation strategy relies solely on asking the model to "be helpful and harmless," you are leaving your application wide open. This arti