# Structured Extraction of Vulnerabilities in OpenVAS and Tenable WAS Reports Using LLMs

arXiv: https://arxiv.org/abs/2511.15745 · HTML: https://mlsanity.com/paper/2511.15745/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2025.

## Built on by (most cited first, 1 of 1)

- [AnonShield: Scalable On-Premise Pseudonymization for CSIRT Vulnerability Data](https://mlsanity.com/paper/2606.15650/) (2026)

## Abstract (arXiv)

This paper proposes an automated LLM-based method to extract and structure vulnerabilities from OpenVAS and Tenable WAS scanner reports, converting unstructured data into a standardized format for risk management. In an evaluation using a report with 34 vulnerabilities, GPT-4.1 and DeepSeek achieved the highest similarity to the baseline (ROUGE-L greater than 0.7). The method demonstrates feasibility in transforming complex reports into usable datasets, enabling effective prioritization and future anonymization of sensitive data.