  AI systems can take harmful actions and are highly vulnerable to adversarial
attacks. We present an approach, inspired by recent advances in representation
engineering, that interrupts the models as they respond with harmful outputs
with "circuit breakers." Existing techniques aimed at improving alignment, such
as refusal training, are often bypassed. Techniques such as adversarial
training try to plug these holes by countering specific attacks. As an
alternative to refusal training and adversarial training, circuit-breaking
directly controls the representations that are responsible for harmful outputs
in the first place. Our technique can be applied to both text-only and
multimodal language models to prevent the generation of harmful outputs without
sacrificing utility -- even in the presence of powerful unseen attacks.
Notably, while adversarial robustness in standalone image recognition remains
an open challenge, circuit breakers allow the larger multimodal system to
reliably withstand image "hijacks" that aim to produce harmful content.
Finally, we extend our approach to AI agents, demonstrating considerable
reductions in the rate of harmful actions when they are under attack. Our
approach represents a significant step forward in the development of reliable
safeguards to harmful behavior and adversarial attacks.
