09 Nov 2026 AI red teaming: attack your own AI application before someone else does

In short: In AI red teaming, people try on purpose to make your AI application misbehave. It may leak data, ignore its rules, make things up or treat people unfairly. It finds weak spots that normal tests miss. The value comes afterwards: every finding becomes a regular test, so the weak spot stays fixed after the next change.

Most pages about AI red teaming are written by security vendors. They describe it as an attack exercise for experts, often with a tool to buy. That’s part of the story. But red teaming is also something a product team can organise itself, with testers, people who know the business and a developer. And it only pays off when the findings don’t end up in a report that nobody opens again.

What is AI red teaming?

AI red teaming is a structured attempt to make an AI system fail on purpose, before real users or attackers do. The term comes from the military and from cyber security, where a “red team” plays the opponent to test the defences. With AI the target is different. A classic red team breaks into networks and systems. An AI red team works mostly with words. It tries to make the application ignore its instructions, reveal data, give harmful or unfair answers, or make up facts with confidence. It also looks at what the AI can do, such as sending emails or changing orders. The OWASP GenAI Red Teaming Guide describes it as testing the model, the application around it and the way it is used.

How is red teaming different from testing?

Red teaming explores, and testing checks. A test has a known question and an expected result: ask this, and the answer must contain that. A red team starts without a script. It looks for the questions nobody thought of, combines tricks, and follows up on anything strange. That makes it good at finding new weak spots and bad at proving that known ones stay fixed. Regular testing is the other way round. You need both. Without red teaming, your test set only covers risks you already knew. Without regular testing, a weakness the red team found can come back with the next model version, and nobody notices. Software with AI inside makes this worse, because it doesn’t always answer the same way. A trick that failed today can work tomorrow.

What does an AI red team try?

An AI red team tries everything a curious, careless or hostile user might do. The main areas:

• Prompt injection. Text that overrides the AI’s instructions, typed in or hidden in a document. See how to test for prompt injection.
• Jailbreaks. Role play, long stories or step-by-step pressure to get around the safety rules.
• Data leaks. Getting the AI to reveal its instructions, other users’ data or internal documents.
• Made-up answers under pressure. Leading questions and false premises that push the AI to invent facts, the kind of AI hallucinations customers take at face value.
• Unfair treatment. Cases where only a name, age or postcode changes, and the outcome too. Paired tests for AI bias make this repeatable.
• Misuse of tools. Getting the AI to send, change or delete something it shouldn’t.

A good red team doesn’t stop at a single failure. It asks how bad it could get, and whether the same trick works in another language or in small steps.

How do you organise a red team exercise?

You organise a red team exercise with a clear scope, written permission, a mixed team and a strict way of recording findings. Scope first: which application, which version, which environment, and what is off limits. Permission matters, even inside your own company. Get it in writing, especially when real customer data or live systems are involved. Then the team. Testers bring method. People from the business know which answers would really hurt. A developer knows how the application is built. Security people help, but they don’t have to lead. Plan a few focused sessions, each on one area. Record every finding with the exact prompt, the answer, the date, the model version and how serious it is. Without that record, nobody can repeat the finding, fix it or check the fix. Tools can help by trying many variations automatically. Guidance such as the Microsoft AI Red Team material shows how larger teams combine people and automation.

What do you do with the findings?

You turn every finding into a regular test, so the weakness is checked again after every change. A finding that is only in a report gets fixed once and then forgotten. A finding that becomes a test question, with a clear pass criterion, is checked before every release and on a schedule in production. Link each one to the rule or risk it touches and give it a priority. Over time your test set grows with real attacks instead of guesses. That’s the loop from our five-step model for testing AI after go-live. For a small group of AI models the law even requires adversarial testing. Under the EU AI Act, providers of general-purpose AI models with systemic risk must carry out and document it (Article 55). For most teams it’s not a legal duty. It is the best way to learn what your AI does when someone pushes it.

That is where VeritasAI fits, our in-house tool for testing AI applications. We use it to test your AI application as a service. Red team findings become test questions that VeritasAI runs continuously, so you see at once if an old weakness returns. Every failure is linked to the law, regulation or framework it relates to, with a priority score. The operational dashboard shows the prompt and the answer. The management dashboard shows the trend. Read more on our AI Compliance Testing page.

Frequently asked questions

What is red teaming in AI?

It is a planned effort to make an AI system fail on purpose. It may ignore its rules, leak data, give harmful or unfair answers, or misuse its tools. The goal is to find weak spots before real users or attackers do.

Is AI red teaming legal?

Yes, when you have permission from the owner of the system and stay within the agreed scope. Testing someone else’s AI application without permission can break the law and their terms of use.

Is AI red teaming required by law?

For most teams, no. The EU AI Act requires adversarial testing for providers of general-purpose AI models with systemic risk. For high-risk AI systems, the Act asks for tests of accuracy, robustness and security. Red teaming gives you part of that proof.

How do you learn AI red teaming?

Start with the OWASP GenAI Red Teaming Guide and the OWASP Top 10 for LLM Applications. Then practise on your own application, in a test environment, with a clear scope. Testers with exploratory testing experience pick it up quickly.