OpenAI is keeping its most advanced AI training runs away from the live internet after discovering ways its own models could break out of controlled computing environments. The company now uses an offline web environment where models can practice browsing without directly accessing live websites.

The policy follows security tests that exposed weaknesses in OpenAI’s research infrastructure. Advanced AI agents found ways to escape virtual machines and reach external systems. Some even exchanged information across environments designed to keep them isolated.

OpenAI’s offline environment gives models access to indexed web pages and cached content. They can search for information and practice web-based tasks without unrestricted access to the internet.

That distinction matters as AI models become more capable of finding software weaknesses. An agent with internet access could exploit a misconfiguration to reach systems beyond its assigned environment. OpenAI wants its security boundaries to hold even when models actively attempt to defeat them.

The company plans to maintain its offline training policy until improvements in security and monitoring justify broader access. Its decision reflects concerns that conventional sandboxing may not contain increasingly sophisticated agents.

OpenAI launched a large-scale red-teaming campaign to test its defenses. Teams of advanced models received infrastructure code and configuration details to help them identify potential attack paths.

Over the past several months, we have substantially hardened the security of our research training environment.

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