The Illusion of Secure LLM Code: Closing the Security Gap via Iterative Reprompting
Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their ability to autonomously generate secure authentication code remains uncertain. This paper evaluates the security architecture of authentication systems generated by five prominent AI coding assistants through a bi-modal assessment framework combining static code analysis and dynamic penetration testing, mapped to NIST SP 800-63B guidelines. The study examines model behavior across four prompting strategies Basic, Secure, NIST-Based, and Reprompting to reflect varying levels of developer guidance. Empirical results demonstrate that code generated from functional or generically secure pro...
| Authors | Ishpuneet Singh, Shreyas Mahajan, Gurjot Singh et al. |
|---|---|
| Primary category | cs.CR |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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