Red-Teaming

Red-teaming is the practice of deliberately attacking your own AI system to find failures before users or bad actors do. A red team probes the model with adversarial prompts — jailbreaks, prompt injections, requests for harmful content, attempts to leak the system prompt or private data — and documents where the guardrails break. It's borrowed from security, and for LLM features it's now table stakes. For SaaS builders shipping anything user-facing, red-teaming surfaces the concrete ways your product can be manipulated: a support bot coaxed into offering refunds it shouldn't, a RAG assistant tricked into revealing another tenant's documents, an agent talked into calling a destructive tool. You can red-team manually, crowdsource it, or use automated adversarial-prompt suites. The output should feed directly into guardrails, input/output filters, and your eval set so the same attack can't succeed twice. Treat red-teaming as ongoing, not a one-time audit — new jailbreak techniques appear constantly, and every prompt change can quietly reopen a hole you already closed. It helps to keep the vocabulary straight, because the two attack families need different defences. A jailbreak targets the model's own safety training directly, using role-play, hypothetical framing, or adversarial phrasing to get a refusal-trained model to comply. Prompt injection targets content the model is asked to process — a retrieved document, a scraped page, a tool's output — hoping the model treats instructions buried in that data as instructions from you. Your red team should exercise both, because a system hardened against one can be wide open to the other, and injection in particular scales with every new data source or tool you connect. At the frontier labs, red-teaming is a substantial line item in the cost of shipping a model responsibly, and its output feeds directly into post-training safety work: refusal training and the reward signals used during preference tuning are shaped by what the red team found. That is worth knowing because it sets expectations about what you are buying. No red-teaming process has been shown to catch every jailbreak or misuse vector, so a hosted model arrives hardened but not sealed, and the residual risk lands in your application layer. The other correction is about cadence. Red-teaming is not a pre-launch gate you pass once; new techniques surface continuously after deployment, and your own changes — a new tool, a widened permission, an edited system prompt — can silently reopen a closed hole. Schedule it, log the findings as regression tests, and re-run them on every release.