HarmThoughts: A Benchmark for Fine-Grained Harmful Behavior Detection in Reasoning Traces

Large reasoning models (LRMs) produce complex, multi-step reasoning traces, yet safety evaluation remains focused on final outputs, overlooking how harm emerges during reasoning. When jailbroken, harm does not appear instantaneously but unfolds through distinct behavioral steps such as suppressing refusal, rationalizing compliance, decomposing harmful tasks, and concealing risk. However, no existing benchmark captures this process at sentence-level granularity within reasoning traces -- a key step toward reliable safety monitoring, interventions, and systematic failure diagnosis. To address this gap, we introduce HarmThoughts, a benchmark for step-wise safety evaluation of reasoning traces. HarmThoughts is built around our proposed harm taxonomy, comprising 16 functional reasoning behavior categories that capture how reasoning steps contribute to or mitigate harmful outcomes. The dataset consists of 56,931 sentences from 1,018 reasoning traces generated by four model families, each annotated with fine-grained sentence-level behavioral labels. Using HarmThoughts, we analyze harm propagation by composing taxonomy behaviors into safety-failure patterns that characterize how reasoning transitions into harmful execution. We compare white-box and black-box monitors for identifying fine-grained taxonomy behaviors, and further evaluate supervised fine-tuning. While off-the-shelf monitors degrade sharply as behavioral granularity increases, fine-tuning substantially improves performance, highlighting both the difficulty and learnability of fine-grained process-level safety monitoring.