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

A universal jailbreak is a prompting strategy that can be easily replaced with various harmful questions, consistently bypassing model safeguards.

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

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

Universal jailbreaks speed up the process for bad actors, allowing them to use one jailbreak for multiple harmful queries instead of needing to jailbreak for each specific question.

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

The motivation behind our work on jailbreaks is to ensure future models can be deployed safely while making progress towards safety.

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

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