In a recent write-up, Eugene Yan outlines reusable patterns for constructing cybersecurity evaluations of AI agents. The approach is built around four core ideas: a sandboxed target environment, inputs that tune task difficulty, tools the agent can use, and a grader that determines success or failure.
Yan suggests that the sandboxed target is key to keeping tests contained, while deliberately varying inputs lets teams stress-test performance across different levels of challenge. Meanwhile, giving the agent tools mirrors real-world conditions, and a separate grader provides an objective way to measure results.
Because this is the sole source, there are no conflicting views to compare. The framework is presented as a starting point rather than a complete methodology, and it leaves room for teams to adapt the components to their own evaluation needs.