The rise of large language models (LLMs) like GPT-4 has revolutionized software development. But can these powerful AI tools produce secure code, or are they inadvertently opening doors for hackers? A new study investigated just that. Researchers explored how well four popular LLMs (GPT-3.5, GPT-4, Code Llama, and CodeGeeX2) could create secure Python code, a language widely used in web development where security is paramount. The results were startling. Over 75% of the AI-generated code contained vulnerabilities! It turns out that while LLMs are great at fulfilling functional requirements, they often miss critical security risks lurking beneath the surface. They fall into traps, so to speak, generating code that meets the task's basic requirements but remains vulnerable to exploits. Think of it like this: you ask an LLM to build a house (write code), and it builds a beautiful house with all the right rooms. However, it forgets to lock the doors and windows (add security measures), leaving it wide open to intruders. The study also tested whether LLMs could act as their own code reviewers, spotting and fixing vulnerabilities. Unfortunately, they struggled here, too. While GPT-4 showed some promise in identifying issues, both GPT-3.5 and GPT-4 had high rates of false positives, flagging secure code as vulnerable. Their ability to fix the code, even when they recognized the problem, was limited, especially when trying to correct their own mistakes. This suggests that LLMs have blind spots, much like human programmers, when reviewing their own work. But there's hope! Researchers developed a simple tool that significantly improved the LLMs' ability to write secure code. This tool works by giving the LLM feedback on its code, prompting it to iteratively refine and improve its work, guided by input from security analysis tools. With this feedback loop, the success rate of producing secure code jumped significantly, indicating a promising future for AI-assisted secure code development. So, what does this all mean? While LLMs can't be trusted to produce secure code out-of-the-box, they show huge potential with the right guidance. Tools that provide real-time feedback and iterative refinement, coupled with more explicit prompting about potential security pitfalls, could transform LLMs into powerful allies in the fight for secure software. This research highlights the ongoing challenge of creating truly secure AI-generated code, but it also illuminates the path toward a future where AI and human developers collaborate to build a more secure digital world.