  State machines play a pivotal role in augmenting the efficacy of protocol
analyzing to unveil more vulnerabilities. However, the task of inferring state
machines from network protocol implementations presents significant challenges.
Traditional methods based on dynamic analysis often overlook crucial state
transitions due to limited coverage, while static analysis faces difficulties
with complex code structures and behaviors. To address these limitations, we
propose an innovative state machine inference approach powered by Large
Language Models (LLMs). Utilizing text-embedding technology, this method allows
LLMs to dissect and analyze the intricacies of protocol implementation code.
Through targeted prompt engineering, we systematically identify and infer the
underlying state machines. Our evaluation across six protocol implementations
demonstrates the method's high efficacy, achieving an accuracy rate exceeding
90% and successfully delineating differences on state machines among various
implementations of the same protocol. Importantly, integrating this approach
with protocol fuzzing has notably enhanced AFLNet's code coverage by 10% over
RFCNLP, showcasing the considerable potential of LLMs in advancing network
protocol security analysis. Our proposed method not only marks a significant
step forward in accurate state machine inference but also opens new avenues for
improving the security and reliability of protocol implementations.
