# LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models

arXiv: https://arxiv.org/abs/2510.10179 · HTML: https://mlsanity.com/paper/2510.10179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 58 references that are in the map.
- First posted on arXiv 11 Oct 2025.

## Builds on (most cited first, 30 of 32)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining](https://mlsanity.com/paper/2305.10429/) (2023)
- [Better Zero-Shot Reasoning with Role-Play Prompting](https://mlsanity.com/paper/2308.07702/) (2023)
- [A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity](https://mlsanity.com/paper/2305.13169/) (2023)
- [Software Testing with Large Language Models: Survey, Landscape, and Vision](https://mlsanity.com/paper/2307.07221/) (2023)
- [A Review of Generalized Zero-Shot Learning Methods](https://mlsanity.com/paper/2011.08641/) (2020)
- [On the Effectiveness of Parameter-Efficient Fine-Tuning](https://mlsanity.com/paper/2211.15583/) (2022)
- [Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models](https://mlsanity.com/paper/2212.14834/) (2022)
- [Fuzz4All: Universal Fuzzing with Large Language Models](https://mlsanity.com/paper/2308.04748/) (2023)
- [From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future](https://mlsanity.com/paper/2408.02479/) (2024)
- [Unleashing the potential of prompt engineering for large language models](https://mlsanity.com/paper/2310.14735/) (2023)
- [The Art, Science, and Engineering of Fuzzing: A Survey](https://mlsanity.com/paper/1812.00140/) (2018)
- [Decoder-Only or Encoder-Decoder? Interpreting Language Model as a Regularized Encoder-Decoder](https://mlsanity.com/paper/2304.04052/) (2023)
- [WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models](https://mlsanity.com/paper/2310.15991/) (2023)
- [Evaluating Fuzz Testing](https://mlsanity.com/paper/1808.09700/) (2018)
- [ParaFuzz: An Interpretability-Driven Technique for Detecting Poisoned Samples in NLP](https://mlsanity.com/paper/2308.02122/) (2023)
- [Augmenting Greybox Fuzzing with Generative AI](https://mlsanity.com/paper/2306.06782/) (2023)
- [Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language Model](https://mlsanity.com/paper/2310.15657/) (2023)
- [When Fuzzing Meets LLMs: Challenges and Opportunities](https://mlsanity.com/paper/2404.16297/) (2024)
- [BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer](https://mlsanity.com/paper/2305.12534/) (2023)
- [MojoBench: Language Modeling and Benchmarks for Mojo](https://mlsanity.com/paper/2410.17736/) (2024)

## Abstract (arXiv)

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.