Prototype-based networks such as ProtoPNet offer intrinsically inter-pretable predictions by matching regions of the inferenced image to learned pro-totypical patches. However, existing stability metrics rely on expensive manualpart annotations and are limited to narrow perturbation types. In this work, weintroduce the Structural Stability Score (Sss), a scalable, annotation-free metricthat quantifies prototype stability under a diverse set of visual transformations bycomparing 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 dif-ferences in robustness both between models and among different transformations.These findings demonstrate that Sss is a practical tool for highlighting stabilityvariations within and across prototype-based networks, guiding model selectionand interpretability analysis.