Neural networks are ubiquitous in many areas of computer vision, and have gained popularity in the field of optical flow estimation in recent years. While they manage to outperform classical methods in terms of accuracy, they are also known to be vulnerable to adversarial attacks. Since optical flow estimation is a critical component of many computer vision applications, some of which are safety-critical, such as autonomous driving, it is crucial that the vulnerabilities of the methods used are well understood. Further, a modular robustness understanding of a network’s components can help with identifying and fixing vulnerabilities in other networks as well. In this thesis, we perform a robustness analysis of the components of RAFT, a state-of-the-art network for flow estimation, under two different kinds of attacks: The perturbation-constrained flow attack (PCFA), which adds a global perturbation to the whole input image, and an attack based on realistic snow particles, which are added to a three-dimensional scene and then projected onto the input images. We identify different sets of components vulnerable to each attack, and provide some presumptions about the reasons for their vulnerability.