In the ever-evolving world of cybersecurity, staying ahead of malicious actors is a constant challenge. Traditional vulnerability scanners, while essential, often miss critical weaknesses. Could Large Language Models (LLMs), the brains behind AI chatbots, be the key to boosting our defenses? New research suggests they can. By combining traditional Static Application Security Testing (SAST) with the analytical power of LLMs, a novel approach called LLM-supported SAST (LSAST) is emerging. Think of it as giving your security scanner a powerful AI assistant. This assistant doesn't just look for known patterns; it reasons about the code, understanding its logic and potential vulnerabilities. The researchers tested LSAST against several open-source vulnerable applications written in various languages. The results were impressive. LSAST outperformed traditional SAST and standard LLMs in finding vulnerabilities, particularly in uncovering weaknesses that conventional scanners missed. But LLMs aren't a silver bullet. They face two key challenges: reliance on outdated training data and privacy concerns around sending code to third-party providers. To combat these issues, the researchers used an open-source, locally hosted LLM and added a system to feed it the latest vulnerability information. The results are promising, but the journey isn’t over. Future research will focus on improving the knowledge retrieval system, applying advanced reasoning techniques, and enhancing the LLM's ability to scan large codebases while maintaining context. The goal? To build a supercharged vulnerability scanner that can detect and understand complex threats, ensuring that our digital world stays one step ahead of the hackers.