DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learning

TL;DR

DeepCVA is a novel deep multi-task learning model that automatically assesses software vulnerabilities at the commit level, providing timely security insights with high accuracy and efficiency.

Contribution

It introduces the first effective and efficient deep learning approach for simultaneous commit-level vulnerability assessment based on CVSS metrics.

Findings

DeepCVA outperforms baseline models with 38% to 59.8% higher MCC.

It reduces training time by 6.3 times compared to multiple separate models.

The model effectively assesses vulnerabilities in large-scale real-world projects.

Abstract

It is increasingly suggested to identify Software Vulnerabilities (SVs) in code commits to give early warnings about potential security risks. However, there is a lack of effort to assess vulnerability-contributing commits right after they are detected to provide timely information about the exploitability, impact and severity of SVs. Such information is important to plan and prioritize the mitigation for the identified SVs. We propose a novel Deep multi-task learning model, DeepCVA, to automate seven Commit-level Vulnerability Assessment tasks simultaneously based on Common Vulnerability Scoring System (CVSS) metrics. We conduct large-scale experiments on 1,229 vulnerability-contributing commits containing 542 different SVs in 246 real-world software projects to evaluate the effectiveness and efficiency of our model. We show that DeepCVA is the best-performing model with 38% to 59.8% higher Matthews Correlation Coefficient than many supervised and unsupervised baseline models. DeepCVA also requires 6.3 times less training and validation time than seven cumulative assessment models, leading to significantly less model maintenance cost as well. Overall, DeepCVA presents the first effective and efficient solution to automatically assess SVs early in software systems.