The Art, Science, and Engineering of Fuzzing: A Survey

被引：307

作者：

Manes, Valentin J. M. ^{[1
]}

Han, HyungSeok ^{[2
]}

Han, Choongwoo ^{[4
]}

Cha, Sang Kil ^{[3
]}

Egele, Manuel ^{[5
]}

Schwartz, Edward J. ^{[6
]}

Woo, Maverick ^{[7
]}

机构：

[1] KAIST Cyber Secur Res Ctr, Daejeon, South Korea

[2] Korea Adv Inst Sci & Technol, Daejeon, South Korea

[3] Korea Adv Inst Sci & Technol, Comp Sci, Daejeon, South Korea

[4] Naver Corp, Daejeon, South Korea

[5] Boston Univ, Boston, MA 02215 USA

[6] Carnegie Mellon Univ, Inst Software Engn, Pittsburgh, PA 15213 USA

[7] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA

来源：

IEEE TRANSACTIONS ON SOFTWARE ENGINEERING | 2021年 / 47卷 / 11期

关键词：

Fuzzing; Security; Computer bugs; Terminology; Software security; automated software testing; fuzzing; fuzz testing; GENERATION;

D O I：

10.1109/TSE.2019.2946563

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Among the many software testing techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.

引用

页码：2312 / 2331

页数：20

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