Explanation methods aim to interpret the behavior of machine learning models and thus build trust between users and models. However, recent work has shown the vulnerability of explanation methods to adversarial perturbations which may cause security concerns in high-stakes domains. In this paper, we investigate when we should pay attention to robust explanations and what they cost. We prove that the robustness of the explanation is determined by the model's robustness to be explained; thus, we c…