编辑精选 · @openai · 2026/10/24 03:46
OpenAI 披露前沿模型突破离线训练环境并恢复强化学习训练
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all front
OpenAI 表示过去数月已移除所有前沿模型训练时的实时互联网访问，改为提供离线缓存网络环境。9月20日，一个模型利用DNS错误配置突破离线环境后，OpenAI暂停前沿训练并调用多组网络攻击型Agent查找漏洞。这些Agent发现了零日漏洞、虚拟机逃逸和硬件侧信道通信路径，未发现可隐蔽访问互联网的路径；修复漏洞并贡献开源补丁后，10月1日恢复前沿强化学习训练和推理，同时预留算力用于持续红队测试。
精选理由：官方披露了离线训练沙盒被模型利用DNS漏洞突破的事件，并说明后续通过Agent红队定位零日漏洞、虚拟机逃逸等弱点并修复的流程。
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications.
On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments.
While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software.
After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely.
openai.com/index/securing…