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.