Generative AI tools, such as ChatGPT, have demonstrated impressive capabilities in summarisation and content generation. However, they are infamously prone to hallucination, fabricating plausible information and presenting it as fact. In the context of legal research, this poses significant risk. This paper, written by Sally McLaren and Lily Rowe, examines how widely available AI applications respond to fabricated case citations and assesses their ability to identify false cases, the nature of their summaries, and any commonalities in their outputs. Using a non-existent citation, we analysed responses from multiple AI models, evaluating accuracy, detail, structure and the inclusion of references. Results revealed that while some models flagged our case as fictitious, others generated convincing but erroneous legal content, occasionally citing real cases or legislation. The experiment underscores concern about AI’s credibility in legal research and highlights the role of legal information professionals in mitigating risks through user education and AI literacy training. Practical engagement with these tools is crucial to understanding the user experience. Our findings serve as a foundation for improving AI literacy in legal research.