How does K-Means behave under adversarial data injection?

K-Means is highly vulnerable to adversarial points because centroids are mean-based and easily shifted.

A small number of adversarially placed points can drastically move centroids, especially in small clusters. This makes K-Means unsuitable for adversarial environments like fraud-heavy or security-sensitive systems without preprocessing.