  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.
