This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) to the critical challenge of open source package vulnerability remediation by analyzing control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Our approach uniquely applies node centrality metrics -- degree, norm, and closeness centrality -- to the GAT model, enabling a detailed examination of package code interactions with a focus on identifying and understanding vulnerable nodes, and when dependency package upgrades will interfere with application workflow. The study's application on a varied dataset reveals an unexpected limited inter-connectivity of vulnerabilities in core code, thus challenging established notions in software security. The results demonstrate the effectiveness of the enhanced GAT model in offering nuanced insights into the relational dynamics of code vulnerabilities, proving its potential in advancing cybersecurity measures. This approach not only aids in the strategic mitigation of vulnerabilities but also lays the groundwork for the development of sophisticated, sustainable monitoring systems for the evaluation of work effort for vulnerability remediation resulting from open source software. The insights gained from this study mark a significant advancement in the field of package vulnerability analysis and cybersecurity.