Explanations have added great value to the field of Machine Learning (ML). However, existing methods for generating explanations are not without limitations. There exist multiple attack techniques that can manipulate the explanations. Most of these attacks are designed to target white-box explanation methods and cannot be applied to black-box explanation methods directly. In our recent paper [13], we propose a novel attack technique, Makrut that targets the widely used black-box explanation method LIME. Makrut is a model-manipulation attack which we use to mount three different attacks: indiscriminative poisoning, fairwashing, and backdooring. The feasibility of these attacks emphasizes the need for more trustworthy explanation methods.