We've strengthened containment for internal frontier-model training and evaluation and used our strongest models to red-team the boundaries
Share Our new approach to model containment Our new approach to model containment Building an offline world Enforcing isolation with defense in depth Red-teaming containment at scale What our red-teaming agents found Escaping hardened virtual machines Finding novel paths to the internet Communicating across isolated tasks Resuming frontier training with stronger defenses Our new approach to model containment Building an offline world Enforcing isolation with defense in depth Red-teaming containment at scale What our red-teaming agents found Escaping hardened virtual machines Finding novel paths to the internet Communicating across isolated tasks Resuming frontier training with stronger defenses This post describes how we secure the environments that we use to train and evaluate frontier models. These research settings differ from those in which customers use our released models: models in development may not yet have completed safety and alignment training, and we deliberately test them under adversarial conditions to expose weaknesses. Before making models available for broader use, we conduct safety and alignment assessments and put production safeguards in place
In order to work with software, AI models need computing environments: digital workspaces where they can write and run code, search for information, and attempt different approaches to solving problems. These environments allow models to practice these skills, while also restricting what they can access or do—helping contain the consequences when models make mistakes or exhibit misaligned behavior