FixV2W: Correcting Invalid CVE-CWE Mappings with Knowledge Graph Embeddings

arXiv cs.LG / 4/27/2026

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Key Points

The paper highlights that CVE-to-CWE mappings in public sources like the NVD can be inconsistent or incomplete, which undermines automated vulnerability analysis and remediation.
It introduces FixV2W, a lightweight method that uses knowledge graph embeddings plus historical remapping trends and hierarchical relationships in NVD/CWE data to predict more accurate CWE mappings.
FixV2W targets vulnerabilities whose CWE links fall under Prohibited or Discouraged categories, using longitudinal patterns to correct previously invalid mappings.
In experiments on data collected from Aug 2021 to Dec 2024, FixV2W correctly predicts the right CWE for 69% of exploited vulnerabilities that had invalid CWE assignments before exploitation.
The approach also boosts downstream ML performance, improving Mean Reciprocal Rank (MRR) for an ML model focused on finding unknown CVE-CWE mappings from 0.174 to 0.608.

Abstract

Accurate mapping between Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) entries is critical for effective vulnerability management and risk assessment. However, public databases, such as the National Vulnerability Database (NVD), suffer from inconsistent and incomplete CVE to CWE mappings, complicating automated analysis and remediation. We introduce FixV2W, a lightweight approach that leverages knowledge graph embeddings and longitudinal trends to improve mapping accuracy of the NVD. FixV2W systematically analyzes historical remapping patterns and leverages hierarchical relationships within NVD and CWE data to predict more precise CWE mappings for vulnerabilities linked to Prohibited or Discouraged categories. We run extensive experimental evaluation of FixV2W, based on test data set collected between August 2021 and December 2024. Considering the Top 10 ranked predictions, the results show that FixV2W predicts the correct CWE mappings for 69% of exploited vulnerabilities that had invalid CWEs before they were exploited. We also show that FixV2W significantly improves the performance of ML models relying on NVD data. For instance, for a model geared at uncovering unknown CVE-CWE mappings, FixV2W improves the Mean Reciprocal Rank (MRR) from 0.174 to 0.608. These results show that FixV2W is a promising approach to identify and thwart emerging threats.