HarmProfile: Characterizing Harmful Distributions in Frontier LLMs
Frontier large language models (LLMs) safety evaluation has largely treated harmful generation as an attack outcome rather than as an object of analysis. Consequently, little is known about the harmful outputs produced during model misbehavior, partly because large-scale, high-quality collections of frontier-LLM misbehavior are difficult to obtain. To address this gap, we introduce HarmProfile, a content-centric benchmark dataset that collects model misbehavior across diverse harm categories and model families, and defines the resulting harmful-output distribution as a model-level risk profile. The premise is that, just as linguistic behavior can be characterized from an utterance corpus,...
| Authors | Zhouyuan Ma, Yutao Wu, Hanxun Huang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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