Abstract: With the widespread adoption of software systems,security issues have become increasingly prominent.Fuzzing,as an effective vulnerability detection technique,plays a crucial role in software development.However,traditional fuzzing tools rely on manually written driver programs,suffering from inefficiency and insufficient coverage.To address these challenges,this study proposes an automated fuzzing driver generation method based on large language models(LLMs).The approach incorporates an intelligent code parsing module to extract function interfaces and structure definitions,leverages the code generation capabilities of LLMs to automatically produce driver programs compliant with the Honggfuzz framework,and introduces a feedback-based correction mechanism to improve driver generation success rates.Experimental results demonstrate that the proposed method achieves a 100% driver generation success rate and fuzzing interface coverage in the open-source cJSON library and an in-house TBox project.For the open-source Libtiff library,the driver generation success rate reaches 76.2%,with a fuzzing interface coverage of 40.5%.Ablation studies on Qwen2.5-coder(14 B parameters) and Qwen2.5-coder(32 B parameters) indicate that the feedback correction mechanism further optimizes driver generation success rates,improving them by 5.9% and 2.4%,respectively.This method significantly enhances the automation level and coverage of fuzzing,providing an efficient solution for vulnerability detection in complex software systems.Future work may focus on optimizing the code parsing module,refining prompt templates,and enhancing LLM adaptability to further improve the method's generalizability and vulnerability discovery capabilities.

Key words: Fuzzing testing, Large language models, Driver generation, Automated testing, Feedback-based correction mechanism