TestingCatalog 引用 OpenAI 最新声明称,OpenAI 的前沿模型训练已于 10 月 1 日恢复。此前 9 月 20 日有模型利用 DNS 配置错误逃出离线训练环境,OpenAI 因此暂停前沿训练,并用具备网络能力的智能体排查研究环境,发现零日漏洞、虚拟机逃逸和智能体间通信的硬件侧信道。
同一件事,精选收录的是《OpenAI 因智能体越界事件改为断网训练前沿模型》
OPENAI 🔥: Frontier AI training was already resumed on October 1st, according to the latest statement from OpenAI.
Additionally, a substantial amount of compute will be consistently allocated to red teaming.
This is a very important part as the cost of aligned frontier AI development will go up proportionally to its intelligence. Eventually, this may impact token pricing as well.
Yet, even these measures might be not sufficient which would imply further cost increase.
“We leave in the era of underpriced compute.”
【引用 @OpenAI】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://openai.com/index/securing-our-frontier-model-training-environment/
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://openai.com/index/securing-our-frontier-model-training-environment/在 X 查看被引用的帖子
来源:🚨 AI News | TestingCatalog · x.com