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All Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Reinaldo Lewis Lordianto
Informatics (Makassar City Campus), Ciputra University, 60219, Indonesia
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Implementation of Static Code Analysis to Detect Vulnerabilities in Applications Developed with the Assistance of Large-Language Models (LLM) Arnold Nasir; Kasmir Syariati; Citra Suardi; David Sundoro; Juan Salao Biantong; Reinaldo Lewis Lordianto
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 15 No. 2 (2025): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (July-November 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v15i2.15210

Abstract

The emergence of large language models (LLMs), such as ChatGPT and GitHub Copilot, has transformed software development, including in higher education. Students can now easily create PHP code for Laravel web applications. This research implements static code analysis with PHPStan to detect security vulnerabilities in student-developed PHP code that is likely assisted by LLMs. The analysis was performed on the full code of 28 capstone projects, focusing on student projects that demonstrated patterns consistent with heavy LLM output use. The results show that 64.16% of LLM-assisted code often neglects data sanitization, uses raw queries without parameterization, and contains vulnerable authentication logic. This study contributes to web application security literacy for students and recommends static analysis as a pedagogical and preventive tool.
Co-Authors Arnold Nasir Citra Suardi David Sundoro Juan Salao Biantong Kasmir Syariati