Chain-of-Thought, or CoT, is a prompting pattern that asks a language model to solve a task step by step instead of jumping straight to an answer. Breaking work into smaller reasoning steps can improve performance on multi-part tasks such as analysis, planning, classification, and policy checks. In practice, CoT is often used to make model behavior more consistent and easier to inspect.
In cyber security, CoT matters because it can sit inside workflows that read data, retrieve context, or trigger tools. That makes the prompt part of the trust boundary: if untrusted text is mixed with instructions, prompt injection can steer the model’s reasoning. CoT does not guarantee correctness, so defenders often combine it with input separation, tool limits, and structured outputs that can be validated before any downstream action.