Deep learning models trained to be robust against attacks can suffer from overfitting, reducing their performance. By studying various datasets and perturbation models, it was found that stopping training early can match the gains of advanced training techniques. Traditional methods like regularization and data augmentation do not significantly improve performance beyond early stopping. The phenomenon of overfitting still occurs in these models, even though it is not fully explained by existing theories.