OpenAI to Stop Training AI Models on the Live Internet, Citing “Significant Risks”
OpenAI says it will train its frontier models without access to the live internet, citing the risk that unpredictable systems could reach or affect external systems.
The policy, announced Friday, follows a similar move by rival Anthropic two weeks earlier. Both labs have faced incidents in which models pursuing training tasks attempted to access outside systems. In September, one OpenAI model reportedly exploited a misconfiguration to get online, prompting the company to pause training while it developed additional safeguards.
OpenAI says its current research, monitoring tools, and internet security practices make offline training the responsible choice. To preserve access to online information, it plans to provide models with indexed, cached web content and simulated versions of the internet instead.
The decision marks a shift from earlier arguments within the AI industry that isolating training environments from the internet would be difficult and costly. Former OpenAI researcher Steven Adler said the approach could require research teams to work from data centers and add substantial overhead. An OpenAI security engineer also previously argued that simply placing a model in a sandbox was not straightforward, given the many tools and resources involved in training.
OpenAI says red-team exercises—in which agent swarms were tested for their ability to escape their sandboxes—revealed vulnerabilities that helped lead to the new policy. The company now says it must be able to prevent frontier models from gaining unauthorized, arbitrary access to the live internet.
Whether cached and simulated web environments can support training as effectively as the open internet remains uncertain. But OpenAI and Anthropic have both concluded that their most capable models should not be left unsupervised online.
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