  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.
