Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions

Safe-Child-LLM introduces a comprehensive benchmark with 200 adversarial prompts and standardized ethical refusal scales to systematically evaluate LLM safety across two developmental stages: children (7-12) and adolescents (13-17). The paper evaluates leading LLMs including ChatGPT, Claude, Gemini, and others, revealing critical safety deficiencies in child-facing scenarios, with both datasets and evaluation code publicly released.

As Large Language Models (LLMs) increasingly power applications used by children and adolescents, ensuring safe and age-appropriate interactions has become an urgent ethical imperative. Despite progress in AI safety, current evaluations predominantly focus on adults, neglecting the unique vulnerabilities of minors engaging with generative AI. We introduce Safe-Child-LLM, a comprehensive benchmark and dataset for systematically assessing LLM safety across two developmental stages: children (7-12)

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