Analysis of the Precision of Large Language Models in the Identification of Security Vulnerabilities and Weaknesses in Generated Code

Federico Muñoz-Babiano; Paula Lamo; Ricardo S. Alonso

doi:10.14201/adcaij.32926

Analysis of the Precision of Large Language Models in the Identification of Security Vulnerabilities and Weaknesses in Generated Code

Federico Muñoz-Babiano

UNIR (International University of La Rioja), Av. de la Paz, 137, Logroño, 37008, La Rioja, Spain federico.munoz[at]unir.net
Paula Lamo
Ricardo S. Alonso

AIR Institute, HUB de Innovación Tecnológico La Aldehuela, Zamora, 49022, Castilla y León, Spain

https://doi.org/10.14201/adcaij.32926

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How to Cite

Muñoz-Babiano, F., Lamo, P., & Alonso, R. S. (2026). Analysis of the Precision of Large Language Models in the Identification of Security Vulnerabilities and Weaknesses in Generated Code. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e32926. https://doi.org/10.14201/adcaij.32926

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Editorial dates

Submitted:

26-02-2025

Acceptance:

06-02-2026

Published:

16-04-2026

Issue

Vol. 14 (2025)

Section

Articles

Keywords

code security
Common Vulnerabilities and Exposures (CVE)
Common Weaknesses Enumeration (CWE)
Large Language Models (LLM)

Supporting agencies

This publication is part of the project “Exploring the Practical Application of Generative Artificial Intelligence in Internet of Things and Connected Industry Educational Environments” (T24-002-I), financed by the Programa Propio de Inversiones Estratégicas para el Desarrollo de Capacidades de Investigación y Transferencia (Programa Incentiva) of the Instituto de Investigación, Transferencia e Innovación (ITEI) of the Vice-Rector’s Office for Transfer of the Universidad Internacional de La Rioja.

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