There are many reasons one may want to edit a model’s chain of thought (CoT)—e.g., to prefill it with reasoning from a stronger model or to remove steps that may yield unsafe outputs. The success of these edits plausibly depends on a model’s inability to notice them, as the model may alter its behavior if it suspects tampering. In this work, we study whether recent reasoning models are able to detect such edits on their CoTs under a variety of conditions: during reasoning and after it; when alerted to the possibility of tampering and when not alerted; and when prefilled both with their own CoTs and with those of other models. Broadly, we find that (i) models exhibit only very modest detection accuracy; (ii) models struggle to identify *where* their CoT was modified; and (iii) models are about as good at detecting changes to their own CoTs as to those of other models.