Static Analysis of Vulnerabilities That AI-Based Code Assistants May Introduce - Mengüllüoǧlu, Mehmet Ali; Tatar, Zeynep Nisanur; Maral, Yusuf; Ulaş, Harun; Baykara, Muhammet (Institute of Electrical and Electronics Engineers Inc., 2026)
With the rapid development of Large Language Models (LLMs) today, they are beginning to be seen as an alternative to working with software experts for individuals without software expertise when developing web environments. In previous studies on this 'vibe coding' method, which appears low-cost and practical in theory, the individuals' knowledge base and the method itself have been overshadowed by the template databases used. In this study, by mimicking the methods of vibe coders, project files generated by various language models using a single prompt without any intervention are identified and compared using two different Static Application Security Testing (SAST) tools within an AI-powered coding agent-based (Agent-first) autonomous development environment. © 2026 IEEE.
2nd International Symposium on AI-Driven Engineering Systems, ISADES 2026 -- 19 June 2026 through 20 June 2026 -- Hybrid, Mbale -- 226193
Keywords: Agent-First; Antigravity; Artificial Intelligence; Claude Opus; Claude Sonnet; Gemini; Linux; Llms; Owasp; Sast; Security; Semgrep; Snyk; Vibe Coding; Vulnerability