Prototype-based networks such as ProtoPNet offer intrinsically interpretable predictions by matching regions of the inferenced image to learned prototypical patches. However, existing stability metrics rely on expensive manual part annotations and are limited to narrow perturbation types. In this work, we introduce the Structural Stability Score (Sss), a scalable, annotation-free metric that quantifies prototype stability under a diverse set of visual transformations by comparing prototype activation maps. We evaluate Sss on two ProtoPNet variants (using VGG19 and Resnet34 backbones) trained on the CUB-200-2011 dataset, and assess stability across six distinct perturbations. Our results reveal clear differences in robustness both between models and among different transformations. These findings demonstrate that Sss is a practical tool for highlighting stability variations within and across prototype-based networks, guiding model selection and interpretability analysis.