Multi-label classification represents the learning paradigm that categorizes a given instance with an undetermined number of labels. While this framework has progressively gained attention as its formulation naturally suits many real-life tasks, numerous limitations still hinder its practical application. This work addresses one such limitation by analyzing the impact of label-level noise on k-Nearest Neighbor (kNN)-based multi-label classifiers. Specifically, it examines six different noise induction mechanisms at the label level and assesses their effects on six representative multi-label datasets and three well-known kNN-based classifiers, along with three mechanisms for improving their efficiency. The results show that while some recognition strategies are severely affected by label-level noise, others naturally exhibit greater robustness. Moreover, the effectiveness of each approach varies depending on the specific type of noise, highlighting the need for tailored mitigation strategies.