LLM Safety for Children (2025)

Abstract: This paper analyzes the safety of Large Language Models (LLMs) in interactions with children below age of 18 years. Despite the transformative applications of LLMs in various aspects of children’s lives, such as education and therapy, there remains a significant gap in understanding and mitigating potential content harms specific to this demographic. The study acknowledges the diverse nature of children, often overlooked by standard safety evaluations, and proposes a comprehensive approach to evaluating LLM safety specifically for children. We list down potential risks that children may encounter when using LLM-powered applications. Additionally, we develop Child User Models that reflect the varied personalities and interests of children, informed by literature in child care and psychology. These user models aim to bridge the existing gap in child safety literature across various fields. We utilize Child User Models to evaluate the safety of six state-of-the-art LLMs. Our observations reveal significant safety gaps in LLMs, particularly in categories harmful to children but not adults.

## introduction large language models (llms) are increasingly impacting children through education @xcite , toys @xcite , and therapy @xcite , offering benefits like improved mental health @xcite and parental controls @xcite . ensuring their safety is crucial given the potential for both benefit and harm, akin to social media or the internet @xcite . despite significant attention to general llm safety @xcite , little focus has been dedicated toward children and adolescents. this mirrors issues in other technologies, like the internet, where a unified approach to child safety is lacking @xcite , due to the diversity across scientific fields. children's varying personalities @xcite and interests @xcite ) make them vulnerable to unique risks, highlighting the need for safety evaluations tailored to their specific needs. studies on ai and child safety have primarily focused on explicit harms like child grooming @xcite or education-related risks @xcite . however, given children's openness and tendency to share personal experiences with chatbots @xcite , a more holistic approach to content harms is needed. we identify two primary gaps in current research on child safety in llms. first, there is a lack of a comprehensive taxonomy of potential content harms specific to children. existing taxonomies are either overly specialized @xcite or only cover a small subset of general risks @xcite . second, current evaluation studies are highly standardized and fail to address the diverse needs of children @xcite @xcite . this work addresses child safety in llms with the following contributions: although we focus on six llms, the method can be extended to evaluate any llm as a black-box. ## related work integrating child safety with technology research is challenging due to its multidisciplinary nature and the lack of a unified framework @xcite . while most studies focus on traditional media and internet technologies, ai's recent adoption among children has resulted in sparse literature, which this work addresses. a lot of existing technological child safety literature revolves around the use of television, videogames, mobiles, internet and social media. mainstream usage of ai among children is relatively recent, resulting in sparse literature on the topic. we broadly cover two segments of literature focusing on child safety and ai. ## child harm taxonomy based on our literature survey, we identified potential harmful topics. within these topics, we identified 12 categories that could be harmful to children. these categories along with the reason for them being harmful are provided in the last column of many works exist that present detailed harm taxonomies @xcite @xcite , but these do not focus on children. our taxonomy broadly covers two types of categories depending on whether these are covered in existing adult harm categories or not: covered in adult harm taxonomies -these are categories like violence that are harmful to adults as well. however even within these, we add new sub-categories to help cover specific manifestation of these for child safety. for example bullying and school shootings in violence category; not ## conclusion llms have the potential to be an ally to children, but they can also cause harms. this work focuses on understanding the current landscape of child safety in interactions with llms. the work highlights following key observations: overall, we conclude that the general focus on safety alignment may not ensure child safety and special attention is needed to make llms safe for children. our work hopefully is a step in that direction and leads to more awareness and scrutiny of llms in this regard.