The techniques used to bypass classifiers often involve encoding mechanisms that obscure harmful outputs, making them less detectable.

We need mechanisms to detect and respond to jailbreaks that may still bypass our classifiers, including a bug bounty program and incident detection, while ensuring the classifiers are efficient, small, and support token-by-token streaming to reduce latency and provide immediate responses.

The classifiers approach aims to improve user experience for non-dangerous tasks while effectively blocking harmful content.

The constitutional classifiers approach is extremely flexible and can be easily updated to respond to various novel threats.

The first layer of our defense against jailbreaks is the input classifier, which analyzes the entire conversation before it reaches the model.

With the constitutional classifiers, we achieved thousands of hours of robustness to Red Teaming, significantly improving our defenses against jailbreaks.

Rapid response monitoring for new jailbreaks allows for quick adaptation and retraining of classifiers to improve safety.

The output classifier monitors the model's responses in real-time and can block dangerous outputs that violate our safety values.

Constitutional classifiers are a promising development in AI monitoring, demonstrating that smaller models can effectively check larger ones.