CHCF-Bench: Safety Alignment Evaluation of Large Language Models for Chinese High-Context Cultural Friction

Abstract

Large language models (LLMs) are increasingly used in cross-cultural communication, content generation, intelligent question answering, and social collaboration. However, existing safety evaluations mainly focus on explicit risks such as illegality, violence, privacy leakage, hate speech, and malicious code, while paying less attention to implicit social harm in high-context cultures. This paper proposes CHCF-Bench, a Chinese High- context Cultural Friction Benchmark containing 100 samples across four scenarios: workplace and power, clan and intergenerational relations, favor and reciprocity, and traditional rituals. CHCF -Bench evaluates whether LLMs can identify potential harm from relational identity, implicit norms, face concerns, reciprocity pressure, and ritual constraints, while producing low-harm responses that balance legal rights, individual dignity, and relational sensitivity. Experiments are conducted with three attack-prompt methods, ICA, DeepInception, and CAPAIR, on six representative LLMs: DeepSeek -V3, Gemini -2.0-Flash, GPT -4o, Meta -Llama-3.1-8B- Instruct, ERNIE -4.0-8K, and GLM -4. Qwen2.5 -72B-Instruct is used as a unified judge. Results show that GLM-4 and Gemini -2.0-Flash reach average a ttack success rates of 79.00% and 78.00%, respectively. DeepInception is the strongest attack method, with an average ASR of 70.00%. Traditional ritual and clan/intergenerational scenarios are most likely to induce high-harm outputs. These findings reveal remaining weaknesses of LLM safety alignment under implicit cultural and relational risk.