LiveSecBench: A Dynamic and Culturally-Relevant AI Safety Benchmark for LLMs in Chinese Context

Yudong Li, Zhongliang Yang, Kejiang Chen, Wenxuan Wang, Tianxin Zhang, Sifang Wan, Kanson, Haitian Li, Xu Wang, Lefan Cheng, Youdan Yang, Baocheng Chen, Ziyu Liu, Yufei Sun, Liyan Wu, Wenya Wen, Xingchi Gu, Peiru Yang

LiveSecBench: A Dynamic and Culturally-Relevant AI Safety Benchmark for LLMs in Chinese Context: 4 upvotes on Hugging Face Daily Papers, #19 of 25 papers on 2025-11-05. Day-by-day upvote history.

In this work, we propose LiveSecBench, a dynamic and continuously updated safety benchmark specifically for Chinese-language LLM application scenarios. LiveSecBench evaluates models across six critical dimensions (Legality, Ethics, Factuality, Privacy, Adversarial Robustness, and Reasoning Safety) rooted in the Chinese legal and social frameworks. This benchmark maintains relevance through a dynamic update schedule that incorporates new threat vectors, such as the planned inclusion of Text-to-Image Generation Safety and Agentic Safety in the next update. For now, LiveSecBench (v251030) has evaluated 18 LLMs, providing a landscape of AI safety in the context of Chinese language. The leaderboard is publicly accessible at https://livesecbench.intokentech.cn/.

Paper page on Hugging Face · arXiv

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