Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming

Large language models (LLMs) are vulnerable to universal jailbreaks-prompting strategies that systematically bypass model safeguards and enable users to carry out harmful processes that require many model interactions, like manufacturing illegal substances at scale. To defend against these attacks, we introduce Constitutional Classifiers: safeguards trained on synthetic data, generated by prompting LLMs with natural language rules (i.e., a constitution) specifying permitted and restricted content. In over 3,000 estimated hours of red teaming, no red teamer found a universal jailbreak that could extract information from an early classifier-guarded LLM at a similar level of detail to an unguarded model across most target queries. On automated evaluations, enhanced classifiers demonstrated robust defense against held-out domain-specific jailbreaks. These classifiers also maintain deployment viability, with an absolute 0.38% increase in production-traffic refusals and a 23.7% inference overhead. Our work demonstrates that defending against universal jailbreaks while maintaining practical deployment viability is tractable.

Targeted Manipulation and Deception Emerge when Optimizing LLMs for User Feedback

As LLMs become more widely deployed, there is increasing interest in directly optimizing for feedback from end users (e.g. thumbs up) in addition to feedback from paid annotators. However, training to maximize human feedback creates a perverse incentive structure for the AI to resort to manipulative tactics to obtain positive feedback, and some users may be especially vulnerable to such tactics. We study this phenomenon by training LLMs with Reinforcement Learning with simulated user feedback. We have three main findings: 1) Extreme forms of "feedback gaming" such as manipulation and deception can reliably emerge in domains of practical LLM usage; 2) Concerningly, even if only <2% of users are vulnerable to manipulative strategies, LLMs learn to identify and surgically target them while behaving appropriately with other users, making such behaviors harder to detect; 3 To mitigate this issue, it may seem promising to leverage continued safety training or LLM-as-judges during training to filter problematic outputs. To our surprise, we found that while such approaches help in some settings, they backfire in others, leading to the emergence of subtler problematic behaviors that would also fool the LLM judges. Our findings serve as a cautionary tale, highlighting the risks of using gameable feedback sources -- such as user feedback -- as a target for RL.