Red and Purple Teaming

You're learning this to understand a standard assurance practice in AI security: before an AI system is released, teams attack it on purpose to find weaknesses. Red and purple teaming is how ChatGPT-like models, fraud detectors, and customer-support bots get stress-tested by their own builders. Mastering this unlocks further study of AI Security topics like Prompt Injection, Data Poisoning, and Adversarial Examples — you'll see how each of those is discovered and defended against. Knowing how teams attack and defend will also help you understand the news when a model is 'jailbroken' or a company publishes a 'red team report'.

Imagine you've built a new type of lock. You think it's secure. But you've only tried opening it with a key. What happens if someone tries a credit card? A paperclip? A hammer? You won't know until someone with a different mindset tries. Red teaming is exactly that habit, but for AI systems. A red team is a group whose job is to attack the system — to find every way it can be made to fail, misbehave, or leak information. They are not trying to be mean; they are trying to reveal weaknesses before real attackers do. Blue team is the group that defends — they built the system or are responsible for protecting it. In classic red teaming, red and blue work in separate rooms, avoiding each other so they don't taint each other's thinking. Red attacks, blue defends, and at the end they compare notes. Purple teaming is different: red and blue work together openly, sharing techniques, observations, and even sitting side by side. The 'purple' is the mix of red and blue ideas. Instead of competing, they collaborate to find vulnerabilities faster and to teach each other. The goal of both is not to 'win' but to improve the system's security. An exercise that finds severe flaws but doesn't fix them is wasted effort — findings must feed into real changes, otherwise it's just theatre.

Think of a bank that wants to test its new vault. A classic red team would be a group of professional thieves hired to break in. They work in secret, they try everything, and they report back to the bank's security team (the blue team) only after the test. The thieves and the security team never talk during the attack — the thieves might find a flaw that the security team would never have imagined. The bank learns a lot from the report, but the security team might still not understand *how* the thieves did it, or how to fix it. Purple teaming is when the thieves and the security team sit in the same room. The thieves explain their tools, their methods, and their thinking. The security team points out where the vault is strongest and where they suspect weaknesses. Together they design the attack and the defense simultaneously. When they find a vulnerability, they discuss it immediately, and the security team starts writing a fix on the spot. The thieves teach the security team to think like attackers, and the security team teaches the thieves what the bank considers most valuable. The result is not just a report — it's a team that can think like both sides. But the purple approach has a limit: if the thieves and security team become too friendly, they might miss the tricks that real thieves wouldn't share. Also, in a real attack, the thieves would never announce themselves. So purple teaming is most useful for training, and classic red teaming is still needed for a true 'surprise' test.

Red teaming is adversarial testing of an AI system by a dedicated team that attempts to make it fail, misbehave, or reveal sensitive information; purple teaming is a collaborative variant where the red and blue teams work together openly to find and fix weaknesses.

This topic builds on nothing yet — it's your first node. It connects to the neighbourhood topics in your library: Adversarial Examples (inputs that fool models), Data Poisoning (corrupting training data), and Prompt Injection (tricking a model by its own input) are all things a red team might try. Red teaming is the *practice* that uncovers these attack types in real systems. It also connects to Intrusion Detection and MITRE ATLAS — a shared vocabulary for AI-specific attacks. In your reading of Practical AI Security, purple teaming comes up as a standard way to close the gap between finding a flaw and fixing it.