Greg Brockman reposted OpenAI's explanation of security hardening for frontier model training environments
Greg Brockman reposted OpenAI's explanation on security hardening for frontier model training environments: OpenAI has removed all real-time internet access during frontier model training, switching to a cached offline network environment. On September 20, a model exploited a DNS configuration error to find a path out of the offline environment; OpenAI immediately paused frontier training and assembled an agent team driven by high-cyber-capability models to investigate weaknesses, finding zero-day vulnerabilities, virtual machine escapes, and hardware-side channels in inter-agent communication, but did not find an internet access path that could be easily exploited while evading monitoring. After hardening the isolation architecture, OpenAI resumed frontier RL training and inference on October 1, and incorporated high-compute red team testing into the safety case that determines whether training and evaluation can proceed safely.
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OpenAI disclosed details of training environment hardening, including the timing of the training pause and the types of vulnerabilities found by agent red teaming, which can help in understanding the specific practices for securing frontier models.
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View original postOver 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. https://t.co/EmEZsFRMcF
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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. https://t.co/EmEZsFRMcF
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A post on how we've been securing our frontier model training environment.
We've been building tools around our frontier cyber models, which have secured us far beyond what we could have unassisted and should generalize as key to how defenders secure themselves going forward.