A Safety and Security-Centered Evaluation Framework for Large Language Models via Multi-Model Judgment

Abstract

The pervasive deployment of large language models (LLMs) has given rise to mounting concerns regarding the safety and security of the content generated by these models. Nevertheless, the absence of comprehensive evaluation methods constitutes a substantial obstacle to the effective assessment and enhancement of the safety and security of LLMs. In this paper, we develop the Safety and Security (S&S) Benchmark, integrating multi-source data to ensure comprehensive evaluation. The benchmark compris

Categories

@article{zhang2025safety,
  title = {{A Safety and Security-Centered Evaluation Framework for Large Language Models via Multi-Model Judgment}},
  author = {Jinxin Zhang and Yunhao Xia and Hong Zhong and Weichen Lu and Qingwei Deng and Changsheng Wan},
  year = {2025},
  month = dec,
  journal = {Mathematics},
  doi = {10.3390/math14010090},
  url = {https://doi.org/10.3390/math14010090}
}