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

The original responsible scaling policy committed to ensuring models reach a sufficient level of robustness to jailbreaks to mitigate risks effectively.

We focused on universal jailbreaks because they can empower non-experts to bypass safeguards easily, which is particularly concerning.

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

The overall summary on whether we're making progress is how hard it is to find a universal jailbreak for a system without increasing refusals too much or compute costs.

The goal is to minimize the time an open universal jailbreak is available, ideally reducing it to a small fraction of the time to prevent misuse in dangerous scenarios.

Before the project, it was relatively easy to jailbreak models, with examples of jailbreaks being shared on social media during live demos.

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.

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