Software vulnerabilities—those sneaky defects that let attackers seize control, steal data, or plant malware—are a constant threat in our digital world. A recent study from the Technical University of Munich and Siemens AG put large language models (LLMs) to the test to see how well they could spot these vulnerabilities in real-world code. Researchers tested six leading LLMs, including GPT-4 Turbo and Gemini 1.5 Pro, across five popular programming languages (Python, C, C++, Java, and JavaScript). The goal was to determine if these language whizzes could accurately detect and classify vulnerabilities based on the Common Weakness Enumeration (CWE) framework, a standardized list of software flaws. The results were intriguing. GPT-4 Turbo and GPT-4o emerged as top performers, demonstrating a remarkable ability to identify security weaknesses. Interestingly, even smaller LLMs like CodeLlama showed a knack for pinpointing vulnerabilities, highlighting the potential for efficient code analysis even with less resource-intensive models. But classifying the specific type of vulnerability proved more complex. While the larger LLMs generally performed well, the study found that providing a few examples (few-shot learning) dramatically boosted their accuracy, suggesting LLMs learn best by example. To see how these findings could translate to real-world development, the researchers developed CODEGUARDIAN, a VSCode extension that integrates LLM-powered vulnerability scanning. In a user study with 22 developers, CODEGUARDIAN helped users find vulnerabilities 66% faster and with 203% greater accuracy than manual methods. The study indicates LLMs can be powerful allies in the fight against security bugs, though challenges remain. Future research will focus on expanding datasets, refining prompting techniques, and improving integration into developer workflows to make LLM-powered tools even more effective.