OpenAI cuts live internet access from frontier model training runs

OpenAI has re-architected its research training environment to eliminate live internet access for all frontier models during training. Instead of allowing training jobs and reinforcement learning rollouts to query external web servers in real time, models in the training environment now interact with an offline, cached snapshot of the web hosted inside OpenAI’s isolated infrastructure.

Removing outbound network paths during training closes two distinct risk vectors: it prevents an experimental model with incomplete safety post-training from taking unsanctioned actions against external systems on the public internet, and it blocks external adversaries from serving poisoned payloads or prompt injections back into active training runs.

Alongside the containment changes, OpenAI conducted an internal agentic red-teaming assessment of its training stack. During that review, OpenAI security researchers identified an unauthenticated remote code execution vulnerability in the open-source Brotli compression library. Because Brotli decompression is embedded across widely deployed runtime and proxy software including Node.js, Bun and Envoy, a specially crafted compressed payload could allow a remote attacker to execute arbitrary code on unpatched systems.