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.

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.

The classifiers can detect attempts to circumvent the system, which is crucial for maintaining security.

We started with a model that had basic training to refuse harmful queries, but many jailbreaks existed that could bypass these safeguards.

A jailbreak is a method that bypasses the safeguards in AI models to extract harmful information.

We should care about jailbreaks because future AI models may pose greater risks, including weapon development and large-scale cybercrime.

The two main improvements we made were honing in on the constitution idea and solidifying the jailbreak styles that we trained on, which helped reduce false positives.