Defending against AI jailbreaks

The podcast explores constitutional classifiers as a novel method for mitigating jailbreaks in AI models, particularly concerning universal jailbreaks that could enable non-experts to extract harmful information. The panel defines jailbreaks as bypassing safeguards to elicit harmful responses from AI models, emphasizing the need to prevent models from aiding in weapon development or cybercrime. They detail a defense strategy using input, Claude's refusal, and output classifiers, which are guided by a natural language "constitution" defining harmful and harmless topics. The discussion highlights the flexibility of constitutional classifiers, allowing for quick adaptation to new threats by simply rewriting the constitution. Red teaming efforts showed an increase in robustness from minutes to thousands of hours before a universal jailbreak was found, demonstrating significant progress.