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

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

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

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.

I believe there will be powerful AI models that can be misused in significant ways, including new forms of bioterrorism and cybersecurity threats.

We need to be aware that bad actors are already using AI against us, particularly in cybercrime.

The misuse of AI technology poses a real risk, especially in the context of increasing global terrorism and potential cyber and bio attacks, and we need to think about how to mitigate these risks, which is a significant concern for society.

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

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