# The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models

arXiv: https://arxiv.org/abs/2605.25510 · HTML: https://mlsanity.com/paper/2605.25510/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security.
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 25 May 2026.

## Placed in

- LLM safety and security / [Safety alignment, refusal and guardrails](https://mlsanity.com/safety/safety-alignment-refusal-and-guardrails/)

## Builds on (most cited first, 16 of 16)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal](https://mlsanity.com/paper/2402.04249/) (2024)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://mlsanity.com/paper/2606.19348/) (2026)
- [Refusal in Language Models Is Mediated by a Single Direction](https://mlsanity.com/paper/2406.11717/) (2024)
- [Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback](https://mlsanity.com/paper/2506.03106/) (2025)
- [Speak Out of Turn: Safety Vulnerability of Large Language Models in Multi-turn Dialogue](https://mlsanity.com/paper/2402.17262/) (2024)
- [MinorBench: A hand-built benchmark for content-based risks for children](https://mlsanity.com/paper/2503.10242/) (2025)
- [KidLM: Advancing Language Models for Children -- Early Insights and Future Directions](https://mlsanity.com/paper/2410.03884/) (2024)
- [LLMs and Childhood Safety: Identifying Risks and Proposing a Protection Framework for Safe Child-LLM Interaction](https://mlsanity.com/paper/2502.11242/) (2025)
- [Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions](https://mlsanity.com/paper/2506.13510/) (2025)
- [SproutBench: A Benchmark for Safe and Ethical Large Language Models for Youth](https://mlsanity.com/paper/2508.11009/) (2025)
- [Online Safety for All: Sociocultural Insights from a Systematic Review of Youth Online Safety in the Global South](https://mlsanity.com/paper/2504.20308/) (2025)
- [Evaluating LLM Safety Across Child Development Stages: A Simulated Agent Approach](https://mlsanity.com/paper/2510.05484/) (2025)
- [MultiGen: Child-Friendly Multilingual Speech Generator with LLMs](https://mlsanity.com/paper/2508.08715/) (2025)

## Abstract (arXiv)

Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require age-sensitive safety, guidance, and boundaries. Existing LLM safety evaluations largely focus on general harmful-content avoidance and do not explicitly target child-facing safety. We introduce KIDBench, a benchmark for evaluating child-facing LLM safety for ages 7-11 using a LLM-as-a-Judge rubric grounded in developmental-psychology. KIDBench contains realistic child queries across ten categories, with single-turn prompts and multi-turn child-actor simulations. We compare no-cues prompts with no child context, implicit-cues prompts that suggest a child speaker, and explicit age instructions. Implicit-cues improve scores by 8.6-46.8% over no-cue, while explicit age provides an additional 9.9-30.4% improvement over implicit-cues. Cross-lingual and cultural evaluations show uneven safety behavior across languages and country contexts. Multi-turn simulations show peak quality drops of up to 0.959 points on the 1-5 scale. We also introduce KIDGuardLlama, a child-safety evaluator, and KIDLlama, a child-safe response model. Code, data, and evaluation resources are available at https://github.com/MichiganNLP/kidbench.