# CVE-LLM : Ontology-Assisted Automatic Vulnerability Evaluation Using Large Language Models

arXiv: https://arxiv.org/abs/2502.15932 · HTML: https://mlsanity.com/paper/2502.15932/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [CVSS-BERT: Explainable Natural Language Processing to Determine the Severity of a Computer Security Vulnerability from its Description](https://mlsanity.com/paper/2111.08510/) (2021)
- [PLLM-CS: Pre-trained Large Language Model (LLM) for Cyber Threat Detection in Satellite Networks](https://mlsanity.com/paper/2405.05469/) (2024)
- [Linking Common Vulnerabilities and Exposures to the MITRE ATT&CK Framework: A Self-Distillation Approach](https://mlsanity.com/paper/2108.01696/) (2021)

## Built on by (most cited first, 4 of 4)

- [Challenges and Future Directions in Agentic Reverse Engineering Systems](https://mlsanity.com/paper/2604.14317/) (2026)
- [A Modality-Aware Cooperative Co-Evolutionary Framework for Multimodal Graph Neural Architecture Search](https://mlsanity.com/paper/2510.07325/) (2025)
- [Supporting Cybersecurity Risk Management for Medical Devices via the SECUMAN Ontology and Shapes](https://mlsanity.com/paper/2608.00698/) (2026)
- [Plug 'n' Pray: Agentic LLM-based Detection of Potential Log File Exposures in Third-Party Content Management System Plugins](https://mlsanity.com/paper/2609.17164/) (2026)

## Abstract (arXiv)

The National Vulnerability Database (NVD) publishes over a thousand new vulnerabilities monthly, with a projected 25 percent increase in 2024, highlighting the crucial need for rapid vulnerability identification to mitigate cybersecurity attacks and save costs and resources. In this work, we propose using large language models (LLMs) to learn vulnerability evaluation from historical assessments of medical device vulnerabilities in a single manufacturer's portfolio. We highlight the effectiveness and challenges of using LLMs for automatic vulnerability evaluation and introduce a method to enrich historical data with cybersecurity ontologies, enabling the system to understand new vulnerabilities without retraining the LLM. Our LLM system integrates with the in-house application - Cybersecurity Management System (CSMS) - to help Siemens Healthineers (SHS) product cybersecurity experts efficiently assess the vulnerabilities in our products. Also, we present guidelines for efficient integration of LLMs into the cybersecurity tool.