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 fi...
| Authors | Zhengyi Jin, Ru Zhang, Xiao Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 2 September 2026 |
| Last updated at source | 2 September 2026 |
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