Containment Verification Offers AI Safety Guarantees Independent of Model Alignment

A new paper introduces a technique called containment verification, designed to ensure AI safety without depending on how well the model aligns with our expectations. Instead of directly altering the model, this method looks for safety assurances within the framework of the AI itself. It uses havoc oracle semantics to treat the AI as an unrestricted oracle within its action space, which means the containment layer must enforce a boundary for all possible outputs. The authors provide a universal guarantee for properties that can be maintained at this boundary through a process called forward-simulation refinement, using Dafny for implementation. They also apply this approach to validate PocketFlow, a basic agentic LLM framework. You can find the paper on arXiv.