{"ID":2840230,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15745","arxiv_id":"2511.15745","title":"Structured Extraction of Vulnerabilities in OpenVAS and Tenable WAS Reports Using LLMs","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.","short_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 similar...","url_abs":"https://arxiv.org/abs/2511.15745","url_pdf":"https://arxiv.org/pdf/2511.15745v1","authors":"[\"Beatriz Machado\",\"Douglas Lautert\",\"Cristhian Kapelinski\",\"Diego Kreutz\"]","published":"2025-11-18T20:42:19Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[\"Large Language Model\"]","has_code":false}