Model containment uses technical and operational boundaries to reduce what a model can reach or do. Measures can include isolated environments, restricted tools and networks, permission controls, monitored execution, rate limits, and procedures that stop a risky run.
Containment is more difficult after model weights are widely distributed because independent operators control their own copies and environments. It should therefore be treated as one layer of risk reduction, not as proof that every possible behavior is under control.
ELI5
Model containment is like keeping an AI system inside a clearly fenced workspace. The system receives only the data, tools, and permissions needed for its task, while important boundaries are watched and enforced.
For example, researchers can test a model in an isolated computer environment with no access to real customer records or public systems. If the model attempts an unexpected action, the test can be stopped without giving it a path to affect those outside resources.
