A Large-scale Empirical Study on the Generalizability of Disclosed Java Library Vulnerability Exploits

TL;DR

This large-scale empirical study evaluates the applicability of Java library vulnerability exploits across versions, demonstrating high accuracy and proposing strategies for exploit migration to improve vulnerability assessment.

Contribution

It provides the first comprehensive analysis of exploit applicability across Java library versions, including a taxonomy of migration strategies to enhance vulnerability detection.

Findings

Exploits achieve 83.0% recall and 99.3% precision without migration.

Migration of exploits increases overall recall to 96.1%.

Identified compatibility issues as main cause of exploit failures.

Abstract

Open-source software supply chain security relies heavily on assessing affected versions of library vulnerabilities. While prior studies have leveraged exploits for verifying vulnerability affected versions, they point out a key limitation that exploits are version-specific and cannot be directly applied across library versions. Despite being widely acknowledged, this limitation has not been systematically validated at scale, leaving the actual applicability of exploits across versions unexplored. To fill this gap, we conduct the first large-scale empirical study on exploit applicability across library versions. We construct a comprehensive dataset consisting of 259 exploits spanning 128 Java libraries and 28,150 historical versions, covering 61 CWEs that account for 76.33% of vulnerabilities in Maven. Leveraging this dataset, we execute each exploit against the library version history and compare the execution outcomes with our manually annotated ground-truth affected versions. We further investigate the root causes of inconsistencies between exploit execution and ground truth, and explore strategies for exploit migration. Our results (RQ1) show that, even without migration, exploits achieve 83.0% recall and 99.3% precision in identifying affected versions in Java, outperforming most widely used vulnerability databases and assessment tools. Notably, this capability enables us to contribute 796 confirmed missing affected versions to the CPE dictionary. We investigate the remaining exploit failures (RQ2) and find that they mainly stem from compatibility issues introduced by library evolution and changing environmental constraints. Based on these observations, we manually migrate exploits for 1,885 versions and distill a taxonomy of 10 strategies from these successful adaptation cases (RQ3), thereby increasing the overall recall to 96.1%.