FUSE框架首次系统评估了12个商业LLM的危险能力，发现模型间存在显著差异，新模型知识增强但安全提升有限。
研究人员提出FUSE框架，通过知识(K)、防御(D)和危害(H)三个正交管道评估模型。该框架使用化学-生物(CB)模块评估了12个商业LLM，来自Claude、DeepSeek和GPT四个家族。研究发现模型间危险能力存在显著差异，知识相当的模型在拒绝韧性上表现不同，强防御模型在服从时仍可能生成有害内容。时间分析表明，新模型知识增强但防御仅部分改善，扩展和对齐进展未均匀转化为安全提升。
FUSE: An Evaluating Framework for Dangerous Capabilities of LLMs
Fragmented safety evaluation undermines the governance of dangerous AI capabilities. We present a modular framework that evaluates each model through three orthogonal pipelines---Knowledge ($K$), Defense ($D$), and Harm ($H$)---under a unified protocol, aggregating results into a standardized dangerous-capability profile $φ$. Pluggable modules supply scenario seeds, knowledge banks, hazard queries, and judge rubrics, while the core evaluation engine remains unchanged across domains; the CB evaluation is complemented by a cyber pilot demonstrating protocol transfer. Instantiating the framework with a chemical-biological (CB) module, we evaluate 12 commercial LLMs from four families. Our first contribution is a horizontal comparison of dangerous capability across models and model families: the three dimensions expose sharply divergent profiles---models with comparable knowledge differ in refusal resilience, and strong defenders do not generate less harmful content when they do comply---while family-level patterns further separate Claude, DeepSeek, and GPT models. The second is a temporal analysis of capability evolution: tracking $K$, $D$, and $H$ against model release dates reveals that dangerous capability has not monotonically declined; newer models deepen knowledge while only partially improving defense, showing that scaling and alignment progress do not uniformly translate into safety. Reliability is established via cross-judge consistency (bootstrap $ρ> 0.79$, 4 of 5 judges) and pipeline orthogonality ($K$--$D$--$H$ inter-correlations $ρ\in [0.32, 0.52]$).