Recently, growing public concern over data privacy has led to the emergence of the ‘right to be forgotten’ under the European Union’s General Data Protection Regulation (GDPR), which allows individuals to request the deletion of personal data. In response, researchers have developed methods to selectively remove or mitigate the influence of specific data on machine learning models, a process known as machine unlearning (MUL). However, while most research on MUL has focused exclusively on maintaining model accuracy after unlearning, the robustness of these models has been largely overlooked, especially under adversarial attacks. In this study, we examine whether unlearning degrades robustness in the visual classification task by performing adversarial attacks on various unlearned methods and compare performance against a retrained-from-scratch baseline, including evaluations using a standard corruption dataset. Our comprehensive evaluation across seven unlearning methods reveals consistent degradation in robustness under perturbation, supporting our hypothesis that machine unlearning degrades adversarial robustness compared to retraining from scratch, and highlighting the need for methods that preserve robustness without compromising the effectiveness and efficiency.