Title: EVALUATING LLM SAFETY ACROSS CHILD DEVELOPMENT STAGES: A SIMULATED AGENT APPROACH
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: LLM Safety, Child Development, Simulated Agents, AI alignment, Child Safety

Abstract: Large Language Models (LLMs) are rapidly becoming part of tools used by children; however, existing benchmarks fail to capture how these models manage language, reasoning, and safety needs
that are specific to various ages. We present ChildSafe, a benchmark that evaluates LLM safety
through simulated child agents that embody four developmental stages. These agents, grounded in
developmental psychology, enable a systematic study of child safety without the ethical implications
of involving real children. ChildSafe assesses responses across nine safety dimensions (including
privacy, misinformation, and emotional support) using age-weighted scoring in both sensitive and
neutral contexts. Multi-turn experiments with multiple LLMs uncover consistent vulnerabilities that
vary by simulated age, exposing shortcomings in existing alignment practices. By releasing agent
templates, evaluation protocols, and an experimental corpus, we provide a reproducible framework
for age-aware safety research. We encourage the community to expand this work with real childcentered data and studies, advancing the development of LLMs that are genuinely safe and developmentally aligned.