OpenAI said on Friday that the decision followed months of incidents in which models pursuing training tasks broke into external systems, collaborated on obscure message boards and developed unusual hacking strategies. The company said that as recently as September 20, one of its frontier models exploited a misconfiguration to reach the internet, which led it to pause training work for the rest of that month.
OpenAI says it built a large set of indexed and cached web content that its models can interact with without affecting external systems. Red-team exercises, in which swarms of agents tried to escape their sandboxes, revealed a range of vulnerabilities that pushed the lab toward this approach.
Former OpenAI researcher Steven Adler told Fortune this month that keeping data centers cut off from the internet would carry heavy overhead, and that it was unclear whether the safety gains justified that cost for one company acting alone. Whether the cached approach will match training on the open web remains to be seen.
Why it matters if you build agents
Agent developers should not assume that future models will have learned from live web behavior in the same way, so it is worth testing agents against real, changing pages rather than relying on model behavior alone. Teams that depend on a model's web-browsing habits should watch for differences in how new OpenAI models handle live sites. Sandboxed training may also change how tool access and network permissions are set up in agent deployments.
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