The rapid proliferation of large language models (LLMs) in applications targeting children and adolescents necessitates a fundamental reassessment of prevailing AI safety frameworks, which are largely tailored to adult users and neglect the distinct developmental vulnerabilities of minors. This paper highlights key deficiencies in existing LLM safety benchmarks, including their inadequate coverage of age-specific cognitive, emotional, and social risks spanning early childhood (ages 0--6), middle childhood (7--12), and adolescence (13--18). To bridge these gaps, we introduce SproutBench, an innovative evaluation suite comprising 1,283 developmentally grounded adversarial prompts designed to probe risks such as emotional dependency, privacy violations, and imitation of hazardous behaviors. Through rigorous empirical evaluation of 47 diverse LLMs, we uncover substantial safety vulnerabilities, corroborated by robust inter-dimensional correlations (e.g., between Safety and Risk Prevention) and a notable inverse relationship between Interactivity and Age Appropriateness. These insights yield practical guidelines for advancing child-centric AI design and deployment.

翻译：大型语言模型（LLMs）在面向儿童和青少年的应用中的快速普及，亟需对现有主要针对成年用户、忽视未成年人独特发展脆弱性的AI安全框架进行根本性重新评估。本文揭示了现有LLM安全基准的关键不足，包括其对涵盖幼儿期（0-6岁）、童年中期（7-12岁）和青春期（13-18岁）的年龄特异性认知、情感及社会风险覆盖不足。为弥合这些差距，我们引入了SproutBench，这是一个创新的评估套件，包含1,283个基于发展心理学的对抗性提示，旨在探测如情感依赖、隐私侵犯及危险行为模仿等风险。通过对47个多样化LLMs的严格实证评估，我们揭示了显著的安全漏洞，这些发现得到了稳健的维度间相关性（例如安全性与风险预防之间）以及交互性与年龄适宜性之间显著负相关关系的佐证。这些洞见为推进以儿童为中心的AI设计与部署提供了实用指南。