Federated Learning (FL) systems face numerous challenges, including data heterogeneity, communication constraints, and vulnerability to adversarial attacks. A promising approach that has recently gained attention is prototype learning, which gives the possibility to share only local prototypes rather than entire models. However, the reliability of such systems is often overlooked, leading to insufficient attention to potential attack vectors. In this paper, we investigate the robustness of two Federated Prototypical methods under both random and revert poisoning attacks. We propose an extension of these methods using personalized aggregation in two different ways to enhance their resilience. Our approach provides valuable insights into the reliability of Federated Prototype Learning and contributes to the development of more robust FL systems. We validate our findings through an extensive experimental evaluation on Cifar-10 and EMNIST, with gains in performance up to 10% and 20%, respectively, with respect to the baselines.