The rise of GenerativeAI, Large Language Models (LLMs), and their capabilities to create human-like text, including writing code, presents both transformative benefits and unprecedented security challenges in software development. AI models can generate information or data not explicitly present in their training data, known as "hallucinations," which pose significant security concerns. These models lack the ability to recognize security implications, leading to potential vulnerabilities such as path traversal attacks, input validation issues, and TOCTOU (time-of-check time-of-use) security issues. To mitigate these risks, developers must remain aware of the potential for AI-generated code to propagate vulnerabilities and adopt secure coding practices, including implementing stringent code reviews, integrating tools for static application security testing, and fostering an environment of continuous learning and adaptation within development teams. Ultimately, human oversight is still vital to ensure robust, secure codebases, and AI code generation tools should be seen as supportive instruments that need human guidance for generating truly secure, production-quality code.