Instant messaging is an integral part of daily communication, and users increasingly seek anonymous options to protect their privacy. Mix networks are a well-established approach for metadata protection, but current designs largely overlook the unique challenges of instant messaging, such as low-latency expectations, bursty interaction patterns, and group chats. In this work, we present the first systematic analysis of mix networks, and anonymization systems in general, for group messaging under realistic conditions. We introduce a novel group-identification attack that exploits co-occurrence patterns of recipients to infer group membership with high accuracy, even against a significantly weaker attacker model than these systems typically assume, a local observer. Our evaluation leverages real-world WhatsApp data and adapts it to better reflect realistic usage scenarios. Finally, we evaluate how defense strategies such as relaxing low latency requirements to allow for more delay options and cover traffic perform against this attack. Our findings reveal critical weaknesses in current designs and provide insights for improving anonymity in instant group messaging.