Deep learning (DL) based techniques have been a common thread of several recent studies in vulnerability detection. The rise of large, publicly available datasets of vulnerabilities has fueled the learning process behind these techniques. While these datasets help the DL-based vulnerability detectors, they also constrain these detectors’ predicting abilities. Vulnerabilities in these datasets have to be represented in a certain way, e.g., code lines, functions, or Program Dependence Graph (PDG) slices within which the vulnerabilities exist. We refer to this representation as a base unit. This means the detectors learn how base units can be vulnerable and then predict whether other base units are vulnerable. We hypothesized that this focus on individual base units harms the ability of the detectors to properly detect those vulnerabilities that span multiple base units (or MBU vulnerabilities). For vulnerabilities like these, a correct detection occurs when all comprising base units are detected as vulnerable. Verifying how these detectors perform in detecting all parts of a vulnerability is important to establish their effectiveness for other downstream tasks. To check our hypothesis we ran a study focusing on three prominent DL-based detectors, ReVeal, DeepWuKong, and LineVul. Our study shows that all these three DL-based vulnerability detectors contain MBU vulnerabilities in their respective datasets. Further, we observed significant drops of accuracies in detecting these types of vulnerabilities. We present our study and a framework that can be used to help DL-based detectors towards proper inclusion of MBU vulnerabilities.