# Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing

arXiv: https://arxiv.org/abs/2403.03897 · HTML: https://mlsanity.com/paper/2403.03897/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2024.

## Built on by (most cited first, 15 of 15)

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- [R1-Fuzz: Specializing Language Models for Textual Fuzzing via Reinforcement Learning](https://mlsanity.com/paper/2509.20384/) (2025)
- [SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing](https://mlsanity.com/paper/2511.12448/) (2025)
- [Towards Reliable LLM-Driven Fuzz Testing: Vision and Road Ahead](https://mlsanity.com/paper/2503.00795/) (2025)
- [Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity](https://mlsanity.com/paper/2509.23449/) (2025)
- [ABTest: Behavior-Driven Testing for AI Coding Agents](https://mlsanity.com/paper/2604.03362/) (2026)

## Abstract (arXiv)

BusyBox, an open-source software bundling over 300 essential Linux commands into a single executable, is ubiquitous in Linux-based embedded devices. Vulnerabilities in BusyBox can have far-reaching consequences, affecting a wide array of devices. This research, driven by the extensive use of BusyBox, delved into its analysis. The study revealed the prevalence of older BusyBox versions in real-world embedded products, prompting us to conduct fuzz testing on BusyBox. Fuzzing, a pivotal software testing method, aims to induce crashes that are subsequently scrutinized to uncover vulnerabilities. Within this study, we introduce two techniques to fortify software testing. The first technique enhances fuzzing by leveraging Large Language Models (LLM) to generate target-specific initial seeds. Our study showed a substantial increase in crashes when using LLM-generated initial seeds, highlighting the potential of LLM to efficiently tackle the typically labor-intensive task of generating target-specific initial seeds. The second technique involves repurposing previously acquired crash data from similar fuzzed targets before initiating fuzzing on a new target. This approach streamlines the time-consuming fuzz testing process by providing crash data directly to the new target before commencing fuzzing. We successfully identified crashes in the latest BusyBox target without conducting traditional fuzzing, emphasizing the effectiveness of LLM and crash reuse techniques in enhancing software testing and improving vulnerability detection in embedded systems. Additionally, manual triaging was performed to identify the nature of crashes in the latest BusyBox.