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March 25, 2026MathematicsOpen Access

A Zero-Touch Vulnerability Remediation Framework Based on OpenVAS, Threat Intelligence, and RAG-Enhanced Large Language Models

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Authors

CHCheng-Hui HsiehCCChen-Yi ChengYWYue Wang

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Overview

A framework integrates OpenVAS and threat intelligence to enhance vulnerability management and automation.

Key Points

  • The research aims to develop an automated framework for vulnerability remediation that reduces the manual effort required and enhances accuracy.
  • Utilized OpenVAS for vulnerability scanning and normalized findings into JSON format.
  • Applied multi-source threat intelligence for informed decision-making in remediation.
  • Leveraged Retrieval-Augmented Generation (RAG) with dual LLM verification for improved accuracy.
  • Implemented automated patch execution through CI/CD pipelines with rollback capabilities.
  • Increased accuracy of vulnerability remediation from 52.0% to 76.7–82.6%.
  • Reduced hallucination rates from 23.4% to 7.8%.
  • Routed 34.9% of cases to high-confidence auto-execution tier with a 4.1% rollback rate.
  • Achieved zero service outages during the remediation process.

Cite This Study

Hsieh et al. (2026) studied this question.

synapsesocial.com/papers/69c37b33b34aaaeb1a67d5b2https://doi.org/10.3390/math14061072
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