The Inspect Sandboxing Toolkit: Scalable and secure AI agent evaluations

How do we test AI systems for dangerous capabilities without risking real-world harm?

The more capable models become, the harder it is to safely evaluate them. When an agent can execute arbitrary code and interact with critical systems to gain sensitive information, running evaluations without adequate safeguards could put critical systems at risk.

Sandboxes are isolated environments for testing and monitoring AI behaviour. When we give a model access to use tools (such as for writing code), we execute that action in a sandbox to limit the model’s access to external systems and data. This lets us evaluate its capabilities without exposing sensitive resources. Today, we’re releasing our toolkit for safely running agentic AI evaluations.

Sandboxing is a common practice in software development for limiting the resources and impact of potentially untrusted processes. When evaluating AI agents, it is critical that instructions are appropriately sandboxed because they come directly from the agent being evaluated.

Running untrusted processes is a mature problem in software engineering – but applying sandboxing to AI agent evaluations in particular presents unique challenges. We need to assess AI agents' capability to interact with arbitrary systems and services to complete a complex, multi-step tasks. But it’s hard to predict ahead of time what tools the model may need to access during this process, how proficient it will be, and whether it will attempt to pursue misaligned goals.

While several sandboxes are available for AI evaluations, we found that existing solutions have important limitations. For example, they may allow secure execution of Python code but prevent the agent from accessing the internet or filesystems.