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

Beatriz Machado; Douglas Lautert; Cristhian Kapelinski; Diego Kreutz

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arXiv · 2511.15745

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

Beatriz Machado·Douglas Lautert·Cristhian Kapelinski·Diego Kreutz

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Abstract

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.

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Beatriz Machado, Douglas Lautert, Cristhian Kapelinski, Diego Kreutz. 2025-11-18. Structured Extraction of Vulnerabilities in OpenVAS and Tenable WAS Reports Using LLMs. https://arxiv.org/abs/2511.15745

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