Neural networks are currently used successfully in many areas. In the context of optical flow estimation, RAFT [1] is a very effective network because it provides fast and also accurate predictions. So far, the focus in the analysis of such networks is primarily on the accuracy of the prediction, the complexity of the architecture, or the computation time. The stability with regard to disturbed input data however, is not yet or only insufficiently investigated for many networks, such as RAFT. The fact that potential weaknesses of a network can be exploited deliberately leads to a significant security risk, for example in the context of autonomous driving. Therefore, we examine two key aspects in this bachelor thesis: On the one hand, we analyze the stability of RAFT when the input images are sabotaged using optimized patches. On the other hand, we modify the existing architecture to make RAFT more robust and less vulnerable to these perturbations. We first transfer the framework developed by Ranjan et al. [2] to perform targeted attacks on RAFT. This shows that by cleverly placing small patches near the the image boundaries, RAFT falsely predicts large areas of zero flow that exceed the dimensions of the actual patch. Therefore, in the next step, we establish different architectural layers of the network that compute the optical flow alternately or successively and compose it from different resolutions. Using our pyramid approach pyRAFTmid, we thereby increase the accuracy of the predicted optical flow by up to 12% on Sintel [3], while decreasing the vulnerability to attacks with optimized patches by up to 30%. Our results help to identify such components that make existing networks more robust and thus contribute to making new methods more robust in this regard.