Vibe Coding Debt: The Security Risks of AI-Generated Codebases (instatunnel.my)

A new phenomenon called "Vibe Coding Debt," popularized by former Tesla AI lead Andrej Karpathy, highlights the growing security risks linked to AI-generated codebases. While vibe coding enables rapid application development through Large Language Models (LLMs) using natural language prompts, it risks embedding security vulnerabilities deep within applications. According to the Veracode 2025 GenAI Code Security Report, nearly 45% of AI-generated code contains security flaws, with LLMs often prioritizing convenience over security — for instance, suggesting wildcards for CORS settings that expose APIs to potential cross-site request forgery attacks. The implications for the AI/ML community are profound. As developers increasingly rely on AI for coding, there is a critical need for robust security oversight. The rise of Vibe Coding Debt serves as a cautionary tale; without systematic review and integration of security practices, such as the proposed SHIELD framework and secure prompting techniques, developers risk hitting a "6-Month Wall" where accumulated security debt leads to unmaintainable software. This underscores the importance of maintaining human oversight in AI-assisted development, ensuring that security remains a core focus in the coding process.