# Snipuzz: Black-box Fuzzing of IoT Firmware via Message Snippet Inference

> Snipuzz fuzzes IoT firmware as a client, using device responses to infer message snippets that guide mutations. It was evaluated on 20 real-world IoT devices and compared with four IoT fuzzing approaches.

- Authors: Xiaotao Feng; Ruoxi Sun; Xiaogang Zhu; Minhui Xue; Sheng Wen; Dongxi Liu; Surya Nepal; Yang Xiang
- Venue: ACM CCS 2021
- Status: Conference accepted
- Publication / posting date: May 12, 2021
- Date source: First arXiv submission date; the arXiv comment names the venue.
- First arXiv submission: May 12, 2021
- arXiv: 2105.05445
- Reviewed arXiv version: v2
- Sources reviewed: 2026-11-06
- Added to the catalog: 2026-11-06
- Original paper: <https://arxiv.org/abs/2105.05445>
- PDF: <https://arxiv.org/pdf/2105.05445>
- BibTeX: <https://awesomerea.org/papers/citations/paper-snipuzz.bib>
- Catalog page: <https://awesomerea.org/papers/paper-snipuzz/>
- Record JSON: <https://awesomerea.org/papers/paper-snipuzz/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-snipuzz/index.md)

## Scope and limitations

The evaluation covers 20 popular real-world IoT devices and comparisons with IoTFuzzer, BooFuzz, Doona, and Nemesys. It reports five newly discovered zero-day vulnerabilities, three exposed only by Snipuzz.

## Why it fits this collection

It infers message structure from device responses to guide black-box testing of IoT firmware.

## Publication and review notes

The arXiv comment reads: Accepted to ACM CCS 2021

## Primary sources

1. <https://arxiv.org/abs/2105.05445>

## About this entry

Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.