A parallel fuzzing method based on two-stage mutation

Ran Tang; Tianchang Yang; Zhiwei Fei

doi:10.1117/12.2624946

6 December 2021 A parallel fuzzing method based on two-stage mutation

Ran Tang, Tianchang Yang, Zhiwei Fei

Proceedings Volume 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021); 1208511 (2021) https://doi.org/10.1117/12.2624946
Event: 2021 International Conference on Green Communication, Network, and Internet of Things, 2021, Kunming, China

Abstract

Fuzzing has become one of the most commonly used methods for exploiting software program vulnerabilities because of its ease of use and effectiveness. With the increasing complexity and importance of network services, fuzzing in parallel mode can greatly improve the efficiency of finding vulnerabilities. Typical representatives are Google's Clusterfuzz and the parallel mode of fuzzers such as AFL, Libfuzz. However, due to the lack of effective control in the process of mutating seeds in different fuzzing instances, the existing parallel fuzzing methods have problems such as high redundancy of generated test cases and low comprehensive coverage. In response to this problem, we proposes a parallel fuzzing method based on two-stage mutation. First, we use random mutations to perform fuzzing test within a period of time, divide the detected state space of the target program into independent state subspaces, and then distribute the corresponding tasks to each fuzzing instance, and use the same ordered mutation on each instance to ensure the generated test cases are lowredundant. We use this method to conduct experiments on the LAVA-M test set and open source software OpenJPEG. The experimental results show that it can improve the efficiency of fuzzing and find more crashes.

Citation Download Citation

Ran Tang, Tianchang Yang, and Zhiwei Fei "A parallel fuzzing method based on two-stage mutation", Proc. SPIE 12085, International Conference on Green Communication, Network, and Internet of Things (GCNIoT 2021), 1208511 (6 December 2021); https://doi.org/10.1117/12.2624946

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KEYWORDS

Mining

Aerospace engineering

Open source software

Process control

Target detection

Distributed computing

Genetic algorithms

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