A small way to keep AI-agent security tests honest

I build RedThread, an open-source CLI for replayable LLM-agent red-team cases.

The security distinction I am trying to learn is between a strange model output and a case where untrusted text changed a proposed tool action. I do not want a denied call to become a dramatic incident report, but I also do not want the causal path to disappear.

RedThread keeps the context slice, tool schema, proposed call, response, and score together so I can replay the case after a prompt, model, or adapter change. It is an early eval tool, not a runtime security product.

https://github.com/matheusht/redthread