We are concerned about threat models where AI systems can copy their weights and perform multi-step tasks in a coding environment.

In a scenario where an AI model might attempt to copy its weights from a data center to run unmonitored, AI control would involve security measures to prevent such actions, including restricting access to model weights and using other models to monitor behavior.

If AI models escape and start creating unmonitored copies, it becomes challenging to control or shut them down.

We have implemented security measures to protect our AI models, but if a state actor is determined enough, they could still succeed in stealing our model weights.

Control measures should treat AI models as potential insider threats, implementing restrictions on access to model weights and algorithmic secrets.

Monitoring for external influences on AI research is crucial, as models might assist unauthorized entities in conducting AI research.

It's difficult to find interesting and realistic threat models for AI systems that can effectively demonstrate their capabilities.

Releasing the weights of AI models is a dangerous move, as it removes the main barrier to misuse and could lead to significant risks.

The rapid replication of both beneficial and harmful information in AI systems poses a significant risk.