The promise of AI-powered coding tools is alluring: faster development, increased productivity, and automated tasks. But what about security? Recent research reveals a critical challenge: AI-generated code, while efficient, can be riddled with security vulnerabilities. These vulnerabilities often stem from flaws in the massive datasets used to train these AI models. Essentially, the AI learns to write buggy code because it's trained on examples of buggy code. The problem is further compounded by the AI’s tendency to create redundant code snippets with subtle variations, making it incredibly difficult to track down and fix all instances of a vulnerability. Imagine a game of whack-a-mole, but every time you whack one mole, several slightly different ones pop up elsewhere. This poses a significant hurdle for developers who must then meticulously hunt down and repair these scattered vulnerabilities, often resembling variations of the same underlying flaw. Traditional vulnerability handling methods rely heavily on manual review and patching, but these methods simply don't scale when dealing with AI-generated code. Researchers are actively exploring solutions leveraging Large Language Models (LLMs) to automate the process of detecting, pinpointing, and repairing these vulnerabilities. They are experimenting with different techniques, including fine-tuning AI models with secure coding practices and creating smarter prompts that guide the AI toward producing more secure code. While these initial efforts show promise, significant challenges remain. One major obstacle is the sheer volume of known vulnerabilities and the constant emergence of new ones. Keeping the AI up-to-date with the latest security threats is an ongoing battle. Additionally, current AI models struggle to genuinely understand security concepts; they rely more on pattern recognition, making them susceptible to overlooking nuanced or complex vulnerabilities. The road ahead requires further research into how to enhance AI’s understanding of security and how to build more robust, adaptive systems that can keep pace with the ever-evolving threat landscape. Ultimately, the goal is not just to fix vulnerabilities after the fact, but to teach AI to write secure code from the start, ushering in a future where AI truly empowers developers without compromising security.