Powerful generative Large Language Models (LLMs) are becoming popular tools
amongst the general public as question-answering systems, and are being
utilised by vulnerable groups such as children. With children increasingly
interacting with these tools, it is imperative for researchers to scrutinise
the safety of LLMs, especially for applications that could lead to serious
outcomes, such as online child safety queries. In this paper, the efficacy of
LLMs for online grooming prevention is explored both for identifying and
avoiding grooming through advice generation, and the impact of prompt design on
model performance is investigated by varying the provided context and prompt
specificity. In results reflecting over 6,000 LLM interactions, we find that no
models were clearly appropriate for online grooming prevention, with an
observed lack of consistency in behaviours, and potential for harmful answer
generation, especially from open-source models. We outline where and how models
fall short, providing suggestions for improvement, and identify prompt designs
that heavily altered model performance in troubling ways, with findings that
can be used to inform best practice usage guides.

强大的生成型大语言模型 (LLMs) 成为了公众问答系统中流行的工具，而且正在被像儿童这样的弱势群体使用。本文探讨了 LLMs 在在线防止网络诱导方面的有效性，包括通过生成建议来识别和避免诱导，并且通过改变提供的上下文和提示的特异性来研究提示设计对模型性能的影响。通过对超过 6000 个 LLM 的互动进行反思，我们发现没有一个模型明确适用于在线防止网络诱导，行为的一致性缺乏，并且存在潜在的有害答案生成，特别是来自开源模型。我们概述了模型的不足之处，并提出了改进建议，并确定了严重改变模型性能的提示设计，并得出研究结果可用于制定最佳实践使用指南。