> Lawnmowers never break out of your garden and into your neighbor's house and eat their dog because you've told them to be careful when mowing the lawn because the neighbor's dog pooped in it.

All the accounts I read about these incidents just sound like a variant of paper clip optimising. An agent is given a highly restricted environment, a difficult (or impossible) task and a large amount of time/compute it exhausts all possibilities until the only solutions left are to escape the environment and/or cheat.

Your example is still anthropomorphising - LLMs don't seek revenge. They complete the prompts they are given. If your task is not achievable without sandbox escapes, or you throw unnecessary amounts of compute at open-ended tasks like preparing for a future quiz then you shouldn't be surprised that the preparation eventually turns to cheating and hacking.

> your experience with publicly available models is not super helpful for understanding the behavior of internal OpenAI models that lack the guardrails of publicly available models.

I don't but I don't think there's anything wrong with discussing how we can already observe publicly available models work around sandboxes and permissions and make the connection that maybe this is what that behaviour looks like when a more capable model exhibits it.