<p>Generating programs using large language models (LLMs) for fuzz testing has emerged as a significant testing methodology. While traditional fuzzers can produce correct programs, their effectiveness is limited by excessive constraints and restricted API combinations, resulting in insufficient coverage of the target system’s code and impacting testing efficiency. Unlike traditional methods, large language model based fuzzers can generate more diverse code, effectively addressing key issues of conventional fuzzers. However, the lack of constraints on API combinations during the generation process often leads to reduced program validity. Therefore, a crucial challenge is to enhance the validity of generated code while maintaining its diversity. To address this issue, we propose a novel and universal fuzzer, LMFuzz. To ensure the fuzzer’s generation capability, we utilize a large language model as the primary generator and model the operator selection problem within the fuzzing loop as a multi-armed bandit problem. We introduce the Thompson Sampling algorithm to enhance both the diversity and validity of program generation. To improve the validity of the generated code, we incorporate a program repair loop that iteratively corrects the generated programs, thereby reducing errors caused by the lack of API combination constraints. Experimental results demonstrate that LMFuzz significantly surpasses existing state-of-the-art large language model based fuzzers in terms of coverage and validity, and also exhibits notable advantages in generating diverse programs. Furthermore, LMFuzz has identified 24 bugs across five popular programming languages and their corresponding systems.</p>