Artificial Intelligence tools such as Bard, ChatGPT, and Bing Chat are prominent in the Large Language Model (LLM) sector, which is gaining traction due to their ability to process human language. These models are increasingly integrated into tech workflows, particularly in AI-generated code tools like GitHub Copilot, Amazon CodeWhisperer, Google Cloud Code, and others. While these tools enhance coding efficiency through features like auto-complete code plugins, they also pose security risks, as they can inadvertently introduce vulnerabilities such as Insecure Direct Object References (IDOR), SQL injection, and cross-site scripting (XSS). The blog post emphasizes the importance of security reviews for auto-generated code, illustrating common pitfalls and vulnerabilities that may arise, such as type juggling in token comparisons, Unicode case mapping collisions, and insecure deserialization configurations. It underscores the necessity for developers to manually review AI-generated code and suggests using security solutions like JFrog SAST to identify and mitigate potential vulnerabilities effectively. The article advocates for caution and continued vigilance in using AI tools for software development, as they are not yet foolproof in ensuring secure code outputs.