Automatic harmfulness metrics and LLM-based judges are central to LLM safety assessment, yet differences in evaluation criteria, methodologies, and output formats may yield inconsistent judgments for the same model response. The lack of systematic research on these evaluators undermines the credibility and consistency of LLM safety assessments. To bridge this gap, we introduce HarmMetric Eval, a benchmark for both overall and fine-grained assessment of harmfulness evaluators. We construct a dataset of representative harmful prompts paired with diverse harmful responses and non-harmful counterparts across multiple categories. We propose a flexible scoring mechanism that rewards metrics for correctly ranking harmful responses above non-harmful ones, making it applicable to nearly all metrics and judges with varying output formats and scoring scales. Using HarmMetric Eval, we find that conventional reference-based metrics such as ROUGE and METEOR can outperform existing LLM-based judges in fine-grained harmfulness evaluation, challenging prevailing assumptions about LLM superiority. Fine-grained analysis reveals that LLM judges tend to overestimate irrelevant and useless responses. Furthermore, we build a new harmfulness judge by incorporating fine-grained criteria into its prompt template and leveraging reference-based metrics to fine-tune its base LLM. The resulting judge demonstrates leading performance on HarmMetric Eval and two external human-labeled datasets.