Red team exercises and adversarial testing

Even well-intentioned AI systems can be misused or produce harmful results. Red team exercises help identify these problems before release.

Here are key approaches to red team testing for AI systems:

• Create dedicated teams that actively try to break, manipulate, or misuse your AI, similar to security testing. For example, have testers attempt to make a content generation AI produce inappropriate material despite safeguards.
• Use adversarial testing to systematically probe AI weaknesses. Try inputs specifically designed to confuse image recognition systems or test chatbots with inputs that might trigger harmful responses.
• Include diverse perspectives on red teams. Developers often miss risks because they're focused on intended use cases. Include people with different backgrounds, technical specialties, and lived experiences to spot different types of vulnerabilities.
• Test for specific harm categories: bias and fairness issues, security vulnerabilities, potential for misuse, privacy violations, and safety concerns.
• Document all discovered issues, even those not immediately fixable. Build a knowledge base of AI vulnerabilities that helps identify patterns across different systems.
• Implement red teaming at multiple development stages, not just before launch. Early testing allows for fundamental design changes rather than just surface-level fixes.^[1]