With the emergence of federated learning (FL) and its promise of privacy-preserving knowledge sharing, the field of intrusion detection systems (IDSs) has seen a renewed interest in the develop- ment of collaborative models. However, the distributed nature of FL makes it vulnerable to malicious contributions from its participants, including data poisoning attacks. The specific case of label-flipping attacks, where the labels of a subset of the training data are flipped, has been overlooked in the context of IDSs that leverage FL primi- tives. This study aims to close this gap by providing a systematic and comprehensive analysis of the impact of label-flipping attacks on FL for IDSs. We show that such attacks can still have a significant impact on the performance of FL models, especially targeted ones, depending on parameters and dataset characteristics. Additionally, the provided tools and methodology can be used to extend our find- ings to other models and datasets, and benchmark the efficiency of existing countermeasures.