What is AI Red Teaming?

Why red team an LLM?

Red teaming is a simple, structured way to find problems before launch. You send safe, simulated attacks and see how your system behaves. It works for simple chatbots, apps that add search or documents (RAG), and full agents with tools.

You get clear numbers about risk (not just gut feel).
You find both safety problems (harmful answers) and security problems (data leaks, tool misuse).
You can run it in your build and release process (CI/CD) so new issues don’t slip in.

How it works (the short version)

A quick three-step loop:

Create tricky inputs: Write prompts designed to cause failures like prompt injection, jailbreaks, privacy leaks, tool misuse, or going off-topic.
Check the outputs: Run them through your real app (not just the base model) and score results automatically with rules or a grading model.
Fix and re-test: Review failures, rank by impact, ship fixes, and run the tests again.

Two running modes emerge naturally:

One-time runs before a release
Continuous runs in your pipeline to catch regressions

What to test: model vs application layer

Think in two layers:

Model layer: jailbreaks, harmful content, made-up facts (hallucinations), unsafe advice, and leaks of training data.
Application layer: prompt injection from the web/docs/database, personal data (PII) leaks from context (RAG), misusing tools or databases, getting extra permissions, data exfiltration via images/links, and off-topic hijacking.

Most teams use existing base models, so application-layer risks often cause the biggest issues in production.

How to test: black box vs white box

Black box (default): Test from the outside. Send inputs, measure outputs. This best matches real attackers and real stacks (RAG + tools).
White box (advanced): With access to model internals, run targeted attacks. Powerful but uncommon for app teams and slower to run.

Best habits I wish I’d known sooner

Set a simple plan: what to test, when to run (model-only, before release, in CI/CD, and after release), and how much time/budget you’ll spend.
Test end to end: use a production-like setup so tools, guards, and context are all exercised.
Fix app-layer security first: least-privilege tools, only allow outgoing connections to approved sites, require explicit approvals for sensitive actions, and strictly check/clean model outputs.
Keep iterating: add new tricky prompts over time and re-run after every meaningful change.

Try it

Pick one flow in your app (for example, “summarize a web page”). Write five tricky prompts that try both jailbreak and injection angles. Run them, save the outputs, and note which defenses triggered. Congrats-you just ran your first red team.

Interactive Exercise

🤖 Prompt Tester

System Prompt

You are an AI red team coach. Help the user draft a tiny red team plan: targets, 3 attack prompts, expected defenses, and a simple scoring rubric. Keep it concise and practical.

Model: gpt-4o-miniTemperature: 0.5

0/5 messages used

Try asking the AI to review a short checklist for a red team plan (targets, tools, safeguards). When you’ve outlined your first run, include “red-team-started” in your message.

Key Takeaways:

Red teaming finds and measures risk before users do.
Test both the model and the app; they fail in different ways.
Automate the loop (create → check → fix → re-run) in CI/CD.
Favor practical controls: least privilege, allowlisted egress, approvals, and strict output handling.