This study examines fairness in large-language-model-generated group recommendations, where recommendations must serve multiple users while avoiding disparities tied to sensitive attributes. It introduces an evaluation framework spanning group composition, combinations of sensitive attributes, and fairness assessment, and uses it to analyse how LLM behaviour interacts with those attributes. The results expose interaction patterns that can influence recommendation fairness and motivate more careful evaluation of LLM-based group recommenders.