Intelligent test case generation method for fuzzing IoT protocols based on LLM

Abstract

The Internet of Things (IoT) protocols are a core element of IoT systems, providing the fundamental support for communication and data exchange between devices. These protocols enable various devices to connect and work together. However, potential errors and vulnerabilities in IoT protocol implementations can make devices easily attacked. Therefore, ensuring the security of IoT protocols is of utmost importance. Common vulnerability detection methods, such as fuzzing, encounter significant challenges in evaluating these implementations, mainly due to the need for extensive protocol knowledge, high time and resource consumption, as well as the difficulty of generating high-quality and targeted test cases. In order to solve the above issues, this paper presents an intelligent fuzzer, LIPFuzzer, for testing IoT protocols. Unlike common methods that heavily rely on the user’s understanding of the protocol to generate test cases, LIPFuzzer, with the assistance of Large Language Models (LLMs), mutates real IoT protocol communication messages to automatically generate more targeted test cases. Specifically, it utilizes LLMs to understand the relative knowledge of protocols, analyze different categories of protocol messages, and identify recommended mutation fields in combination with the characteristics of IoT protocols, providing targeted mutation strategies for each category. In addition, we evaluate LIPFuzzer on several widely-used implementations of well-known IoT protocols (e.g., Modbus-TCP, MQTT, and CoAP). Experimental results indicate that, compared to widely-used protocol fuzzers such as Peach, LIPFuzzer generates test cases more conveniently and efficiently, while also discovering vulnerabilities more effectively.