Security Degradation in Iterative AI Code Generation: A Systematic Analysis of the Paradox

The rapid adoption of Large Language Models (LLMs) for code generation has transformed software development, yet little attention has been given to how security vulnerabilities evolve through iterative LLM feedback. This paper analyzes security degradation in AI-generated code through a controlled e...

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Bibliographic Details
Published in:	IEEE International Symposium on Technology and Society (Print) pp. 1 - 8
Main Authors:	Shukla, Shivani, Joshi, Himanshu, Syed, Romilla
Format:	Conference Proceeding
Language:	English
Published:	IEEE 10.09.2025
Subjects:	AI-Generated Code Codes Degradation Feedback loop Feedback Loops Iterative Feedback Iterative methods Large language models LLM Prompting Strategies Safety Secure Coding Practices Security Security Vulnerabilities Software Software development management Software Security Systematics
ISSN:	2158-3412
Online Access:	Get full text
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