On Friday, OpenAI said that it will train its models using cached copies and simulated versions of the internet instead of the live web because the models are too unpredictable to be trusted with live internet access.
OpenAI said that as recently as September 20, one of its frontier models exploited a misconfiguration to access the internet. That led the company to pause training work for the rest of the month so it could develop and test new approaches to securing its models.
The decision to train these models in sandboxes cut off from the internet is notable because employees at frontier labs have insisted that doing so would be nearly infeasible.
The engineer, whose role has been confirmed previously by OpenAI, described how complex the training runs launched by OpenAI engineers are, noting that they require access a variety of resources and tools across many domains, and that thousands of researchers build, modify and test the environments. “So is it as easy as just putting it in a sandbox? No, it’s not,” he concluded.
Now, OpenAI says that “we must always be able to prevent a frontier model from achieving unauthorized, arbitrary access to the live internet.” That comes after the company deployed swarms of agents in red-teaming exercises that attempted to escape from their sandbox. That effort revealed a range of vulnerabilities that convinced the lab to adopt its new approach.
To meet that requirement while still reaching its training goals, the lab said it created a “a vast set of indexed and cached web content with which our models can interact, without risk of affecting external systems.”
Whether that strategy will perform as well as letting models run freely on the open internet remains to be seen, but it is noteworthy that both frontier labs have determined their most powerful technology can’t be left unsupervised on the digital commons.