SVRM: Composing Various Network Service Fuzzing Corpus with One Single Model
Wenfeng Lin, Zhiyuan Jiang, Fangliang Xu, Yunfei Su, Zhiwei Li, Lingchu Mao, Chaojing Tang
Abstract
Discovering vulnerabilities in network service is of great significance. Currently, coverage-guided fuzzing (CGF) is widely regarded as the most effective method. However, the efficiency of CGF depends on the quality of initial corpus. The initial corpus is a set of valid input examples used to initiate the fuzzing process. Constructing high-quality initial corpus typically requires manual efforts to understand the implementation details and corresponding protocol specifications, making it difficult to generalize across different protocol implementations.To generate high-quality corpus tailored to service under test (SUT), this paper proposes a protocol-independent smart generation method. The paper introduces a novel service communication model and utilizes active learning algorithms to automatically construct the model. By analyzing the minimum spanning tree of the model, we achieve automatic generation of high-quality corpus that adapts to the SUT.We conduct experiment by generating adaptive corpus for 6 targets of 6 different protocols in ProFuzzBench. Compared to the corpus provided by ProFuzzBench, the corpus generated by our system improve the state coverage of modern protocol fuzzers by 37.1% and discover known real protocol vulnerabilities at a speed 2.47x faster.
BibTeX
@inproceedings{icassp2025_svrmcomposingvar,
title = {SVRM: Composing Various Network Service Fuzzing Corpus with One Single Model},
author = {Wenfeng Lin and Zhiyuan Jiang and Fangliang Xu and Yunfei Su and Zhiwei Li and Lingchu Mao and Chaojing Tang},
booktitle = {ICASSP 2025},
year = {2025}
}