Large Language Models (LLMs) are rapidly entering children's lives - through parent-driven adoption, schools, and peer networks - yet current AI ethics and safety research do not adequately address content-related risks specific to minors. In this paper, we highlight these gaps with a real-world case study of an LLM-based chatbot deployed in a middle school setting, revealing how students used and sometimes misused the system. Building on these findings, we propose a new taxonomy of content-based risks for minors and introduce MinorBench, an open-source benchmark designed to evaluate LLMs on their ability to refuse unsafe or inappropriate queries from children. We evaluate six prominent LLMs under different system prompts, demonstrating substantial variability in their child-safety compliance. Our results inform practical steps for more robust, child-focused safety mechanisms and underscore the urgency of tailoring AI systems to safeguard young users.

本研究针对当前人工智能伦理和安全研究中未能充分解决的儿童内容风险问题，提出了一种新的分类法，并引入了一个开源基准测试MinorBench，以评估大型语言模型对儿童不安全或不当请求的拒绝能力。我们的评估结果展示了不同系统提示下六个重要大型语言模型在儿童安全合规性方面的显著差异，为改进专注于儿童的安全机制提供了切实建议。

MinorBench：一个专为儿童内容风险构建的基准测试