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

arXiv: https://arxiv.org/abs/2604.22176 · HTML: https://mlsanity.com/paper/2604.22176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 24 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Canonical Tensor Decomposition for Knowledge Base Completion](https://mlsanity.com/paper/1806.07297/) (2018)
- [Non-Determinism of "Deterministic" LLM Settings](https://mlsanity.com/paper/2408.04667/) (2024)
- [Data Quality for Software Vulnerability Datasets](https://mlsanity.com/paper/2301.05456/) (2023)
- [Knowledge Base Completion: Baselines Strike Back](https://mlsanity.com/paper/1705.10744/) (2017)
- [Rethinking Knowledge Graph Evaluation Under the Open-World Assumption](https://mlsanity.com/paper/2209.08858/) (2022)
- [Uncovering CWE-CVE-CPE Relations with Threat Knowledge Graphs](https://mlsanity.com/paper/2305.00632/) (2023)
- [Can LLMs Classify CVEs? Investigating LLMs Capabilities in Computing CVSS Vectors](https://mlsanity.com/paper/2504.10713/) (2025)
- [The (Un)Reliability of NVD Vulnerable Versions Data: an Empirical Experiment on Google Chrome Vulnerabilities](https://mlsanity.com/paper/1302.4133/) (2013)
- [Centrality Measures in multi-layer Knowledge Graphs](https://mlsanity.com/paper/2203.09219/) (2022)
- [CPE-Identifier: Automated CPE identification and CVE summaries annotation with Deep Learning and NLP](https://mlsanity.com/paper/2405.13568/) (2024)

## Abstract (arXiv)

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.