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

arXiv: https://arxiv.org/abs/2506.13510 · HTML: https://mlsanity.com/paper/2506.13510/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Safety alignment, refusal and guardrails, one of 12 directions in LLM safety and security.
- Builds on 9 of its 86 references that are in the map.
- First posted on arXiv 16 Jun 2025.

## 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, 9 of 9)

- [HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal](https://mlsanity.com/paper/2402.04249/) (2024)
- [JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models](https://mlsanity.com/paper/2404.01318/) (2024)
- [Toxicity in ChatGPT: Analyzing Persona-assigned Language Models](https://mlsanity.com/paper/2304.05335/) (2023)
- [PromptBench: A Unified Library for Evaluation of Large Language Models](https://mlsanity.com/paper/2312.07910/) (2023)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [A Piece of Theatre: Investigating How Teachers Design LLM Chatbots to Assist Adolescent Cyberbullying Education](https://mlsanity.com/paper/2402.17456/) (2024)
- [MinorBench: A hand-built benchmark for content-based risks for children](https://mlsanity.com/paper/2503.10242/) (2025)
- [LLM Safety for Children](https://mlsanity.com/paper/2502.12552/) (2025)
- [Distributed agency in second language learning and teaching through generative AI](https://mlsanity.com/paper/2403.20216/) (2024)

## Built on by (most cited first, 7 of 7)

- [Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberation](https://mlsanity.com/paper/2509.14760/) (2025)
- [Evaluating Cognitive Age Alignment in Interactive AI Agents](https://mlsanity.com/paper/2605.17894/) (2026)
- [GrandGuard: Taxonomy, Benchmark, and Safeguards for Elderly-Chatbot Interaction Safety](https://mlsanity.com/paper/2605.20203/) (2026)
- [CAREBench: A Child-Safety Risk Benchmark for Language Models](https://mlsanity.com/paper/2606.29685/) (2026)
- [A Privacy by Design Framework for Large Language Model-Based Applications for Children](https://mlsanity.com/paper/2602.17418/) (2026)
- [CR4T: Rewrite-Based Guardrails for Adolescent LLM Safety](https://mlsanity.com/paper/2605.21609/) (2026)
- [The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models](https://mlsanity.com/paper/2605.25510/) (2026)

## Abstract (arXiv)

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) and adolescents (13-17). Our framework includes a novel multi-part dataset of 200 adversarial prompts, curated from red-teaming corpora (e.g., SG-Bench, HarmBench), with human-annotated labels for jailbreak success and a standardized 0-5 ethical refusal scale. Evaluating leading LLMs -- including ChatGPT, Claude, Gemini, LLaMA, DeepSeek, Grok, Vicuna, and Mistral -- we uncover critical safety deficiencies in child-facing scenarios. This work highlights the need for community-driven benchmarks to protect young users in LLM interactions. To promote transparency and collaborative advancement in ethical AI development, we are publicly releasing both our benchmark datasets and evaluation codebase at https://github.com/The-Responsible-AI-Initiative/Safe_Child_LLM_Benchmark.git