The Internet of Things (IoT) promises a world of interconnected convenience, but it also opens doors to a new wave of security threats. Traditional methods of finding vulnerabilities in IoT devices, like fuzzing—essentially bombarding systems with random data to trigger unexpected behavior—often fall short. They struggle to grasp the complexities of the HTTP protocol that governs much of the IoT web interface. Now, researchers are turning to the surprising power of Large Language Models (LLMs) to revolutionize IoT security testing. A new approach called ChatHTTPFuzz leverages LLMs to intelligently generate more meaningful and effective test data. Instead of blindly throwing random data at a system, ChatHTTPFuzz uses LLMs to understand the structure of HTTP requests and the logic of the backend code running on IoT devices. This allows it to craft test cases that are far more likely to expose vulnerabilities. Think of it like a skilled hacker who understands the system’s weaknesses, rather than someone randomly pounding on the keyboard. The LLM acts as a guide, helping to identify the most promising areas to probe for vulnerabilities. In tests on real-world IoT devices, ChatHTTPFuzz dramatically outperformed traditional fuzzing tools, uncovering a significant number of previously unknown vulnerabilities, some with critical severity levels. This research offers a glimpse into the future of security testing, where AI plays a central role in protecting our increasingly connected world. However, challenges remain. Accessing internal code coverage information in IoT devices is still complex, relying on techniques like emulation, and the rapidly evolving nature of both IoT technology and cyber threats means this remains an ongoing arms race. The integration of LLMs into security testing offers a promising path forward, suggesting that AI can be a powerful ally in the fight against IoT vulnerabilities.