1dFuzz: Reproduce 1-Day Vulnerabilities with Directed Differential Fuzzing

Songtao Yang, Yubo He, Kaixiang Chen, Zheyu Ma, Xiapu Luo, Yong Xie, Jianjun Chen, Chao Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

1-day vulnerabilities are common in practice and have posed severe threats to end users, as adversaries could learn from released patches to find them and exploit them. Reproducing 1-day vulnerabilities is also crucial for defenders, e.g., to block attack traffic against 1-day vulnerabilities. A core question that affects the effectiveness of recognizing and triggering 1-day vulnerabilities is what is the unique feature of a security patch. After conducting a large-scale empirical study, we point out that a common and unique feature of patches is the trailing call sequence (TCS) and present a novel directed differential fuzzing solution 1dFuzz to efficiently reproduce 1-day vulnerabilities in this paper. Based on the TCS feature, we present a locator 1dLoc able to find candidate patch locations via static analysis, a novel TCS-based distance metric for directed fuzzing, and a novel sanitizer 1dSan able to catch PoCs for 1-day vulnerabilities during fuzzing. We have systematically evaluated 1dFuzz on a set of real-world software vulnerabilities in 11 different settings. Results show that 1dFuzz significantly outperforms state-of-the-art (SOTA) baselines and could find up to 2.26x more 1-day vulnerabilities with a 43% shorter time.
Original languageEnglish
Title of host publicationProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis
Pages867–879
Publication statusPublished - Jul 2023

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