Why GenAI Code Refinements Could Silently Undermining Your Security

🎥 Recorded live at the MLOps World | GenAI Summit 2025 — Austin, TX (October 9, 2025) Session Title: The Iteration Trap: Why GenAI Code Refinements Could Be Silently Undermining Your Security Speaker: Himanshu Joshi, Chief AI Officer (CAIO), COHUMAIN Labs Abstract: As AI-powered coding tools become the norm, their hidden risks are coming into focus. Over 80% of developers now rely on LLMs like GPT-4 or Copilot to assist with code generation and refinement — but what if each “improvement” actually makes your code less secure? In this lightning talk, Himanshu Joshi shares findings from a controlled study analyzing 400 code samples across 40 AI-driven refinement rounds. The results are startling: instead of improving, critical vulnerabilities increased by 37.6% after just five iterations. Even prompts focused on security sometimes introduced new cryptographic and logic flaws. This session introduces the concept of Feedback Loop Security Degradation — showing how iterative LLM refinements can silently erode code safety — and offers practical mitigation strategies including human-in-the-loop validation, limited iteration cycles, and automated vulnerability scanning. What you’ll learn: • Why iterative LLM code refinement can increase vulnerabilities over time • How prompt strategy (efficiency vs. security vs. feature focus) impacts risk • The mechanics of “Feedback Loop Security Degradation” • Practical guidelines to mitigate AI-driven security regressions • Why continuous human oversight and security analysis remain critical in GenAI workflows.