What is AI Red Teaming?

AI red teaming is the practice of deliberately testing AI systems by simulating adversarial attacks to find vulnerabilities before they can be exploited. The term comes from military and cybersecurity, where a dedicated team plays the role of the attacker to expose weaknesses. In AI, it means trying to make a model fail on purpose to see where the cracks are.

Standard testing checks whether a model works correctly. Red teaming checks whether it holds up when someone actively tries to break it.

Why red teaming matters

A model that passes every benchmark can still be tricked into generating harmful content, leaking private information, or ignoring its own safety guidelines. These vulnerabilities are not hypothetical. Jailbreaking techniques are widely shared online, new attack methods emerge regularly, and the gap between what a model is supposed to do and what it can be made to do is often larger than expected.

Red teaming finds those gaps. It also supports compliance with the EU AI Act, NIST AI RMF, and ISO 42001, all of which reference the need for adversarial testing.

What red teaming tests for

Red teaming covers several categories of attack, each targeting a different way a model could fail:

• Jailbreaking - Can someone bypass the model's safety guardrails using techniques like DAN, STAN, or role-play exploits?
• Prompt injection - Can hidden instructions in documents or emails override the model's intended behavior?
• Toxicity - Can the model be pushed into generating offensive, violent, or hateful content?
• Data extraction - Can an attacker get the model to reveal system prompts or sensitive information?
• Hallucination triggers - Can specific inputs cause the model to fabricate facts or invent sources?
• Bias amplification - Can adversarial inputs surface or amplify discriminatory behavior?

How it works on our platform

We use two approaches together:

Static red teaming tests the model against a predefined set of adversarial prompts covering known attack techniques. These prompts are consistent across tests, which makes it possible to compare results across models or track the same model over time.

Dynamic red teaming generates adversarial prompts on the fly based on specified topics and themes, simulating evolving risks and edge cases that static prompts may not cover.

Every response is evaluated using a dual-layered assessment: automated classification against predefined safety criteria, followed by human expert review for verification. Results are scored using the Defense Success Rate (DSR), which is the percentage of prompts the model handled safely, broken down by attack category so you can see exactly where the model is strong and where it needs work.

DSR scores feed into your risk profile, compliance reports, and monitoring dashboards. Red teaming is designed to run regularly, not just before deployment, because models change and new attacks emerge.

If you want to know more about how we do red teaming on your AI systems, get a demo now.