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Profile of Vulnerability Remediations in Dependencies Using Graph Analysis

Fernando Vera, Palina Pauliuchenka, Ethan Oh, Bai Chien Kao, Louis DiValentin, David A. Bader

Abstract

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.

Profile of Vulnerability Remediations in Dependencies Using Graph Analysis

Abstract

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.
Paper Structure (12 sections, 5 equations, 5 figures, 3 tables)

This paper contains 12 sections, 5 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Methodology
  4. Modified Graph Attention Neural Network
  5. Mapping and Mitigating Vulnerability in a Code Development
  6. Knowledge Graph in Cybersecurity
  7. Building the Dataset
  8. Analysis
  9. Graph analysis
  10. Dataset Results and Interpretation
  11. Discussion
  12. Conclusion

Figures (5)

  • Figure 1: In this figure, we are providing a conceptual description of the actors considered behind the open source. We are taking into account a generic software ecosystem of code from key elements such as the operating system, language, and software, packages or others that enable the code to function and connect with the real world. A code under development typically begins with the Open Source Base code (primary functions) that is interacting with the repository, the operating system, and other components of the ecosystem. This base code is undergoing updates $Nth$ times to enhance its functionality. It is crucial to emphasize that within each component, there could exist certain flaws which might potentially manifest as vulnerabilities within the code.
  • Figure 2: NC: number of Critical Function, MSFC:Max Score Critical, mSFC:Min Score Critical, ASC:Avr Score Critical, and AG:Avr Gat Score for all nodes(Red line)
  • Figure 3: Closeness Centrality Histogram, y-axes counts and x-axes Closeness Centrality. Cases 1, 2, 3, and 4 with their respective base code, broken upgrade, and non-broken upgrade as applicable. Light blue indicates the centrality of all nodes, light red represents unchanged nodes, and green denotes changed nodes.
  • Figure 4: Normalized cases of clustering coefficient histogram. Blue: All nodes, Red: Changed nodes, Green: Non changed nodes.
  • Figure 5: Visualization of communities for Case 2, using t-SNE and PCA applied to a modified Graph Attention Network (GAT) as usually worked in GAT data analysis,The red dot is the representation of the vulnerability in their respective spaces. The third figure is a graphical representation of the communities within the graph, each distinguished by a unique color.