In the modern era where software plays a pivotal role, software security and vulnerability analysis are essential for secure software development. Fuzzing test, as an efficient and tra-ditional software testing method, has been widely adopted across various domains. Meanwhile, the rapid development in Large Language Models (LLMs) has facilitated their application in the field of software testing, demonstrating remarkable performance. As existing fuzzing test techniques are not fully automated and software vulnerabilities continue to evolve, there is a growing interest in leveraging large language models to generate fuzzing test. In this paper, we present a systematic overview of the developments that utilize large language models for the fuzzing test. To our best knowledge, this is the first work that covers the intersection of three areas, including LLMs, fuzzing test, and fuzzing test generated based on LLMs. A statistical analysis and discussion of the literature are conducted by summarizing the state-of-the-art methods up to date of the submission. Our work also investigates the potential for widespread deployment and application of fuzzing test techniques generated by LLMs in the future, highlighting their promise for advancing automated software testing practices.

The rapid development of large language models (LLMs) has revolutionized software testing, particularly fuzz testing, by automating the generation of diverse and effective test inputs. This advancement holds great promise for improving software reliability. Meanwhile, the introduction of MOJO, a high-performance AI programming language blending Python’s usability with the efficiency of C and C++, presents new opportunities to enhance AI model scalability and programmability. However, as a new language, MOJO lacks comprehensive testing frameworks and a sufficient corpus for LLM-based testing, which exacerbates model hallucination. In this case, LLMs will generate syntactically valid but semantically incorrect code, significantly reducing the effectiveness of fuzz testing. To address this challenge, we propose MOJOFuzzer, the first adaptive LLM-based fuzzing framework designed for zero-shot learning environments of emerging programming languages. MOJOFuzzer integrates a mutil-phase framework that systematically eliminates low-quality generated inputs before execution, significantly improving test case validity. Furthermore, MOJOFuzzer dynamically adapts LLM prompts based on runtime feedback for test case mutation, enabling an iterative learning process that continuously enhances fuzzing efficiency and bug detection performance. Our experimental results demonstrate that MOJOFuzzer significantly enhances test validity, API coverage, and bug detection performance, outperforming traditional fuzz testing and state-of-the-art LLM-based fuzzing approaches. Using MOJOFuzzer, we have conducted a first large-scale fuzz testing evaluation of MOJO, uncorvering 13 previous unknown bugs. This study not only advances the field of LLM-driven software testing but also establishes a foundational methodology for leveraging LLMs in the testing of emerging programming languages.