Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverage hardly improves as existing methods are ineffective to focus the attention of fuzzing on covering the hard-to-trigger program paths. In other words, they cannot generate inputs that can break the bottleneck due to the fundamental difficulty in capturing the complex relations between the test inputs and program coverage. In particular, existing fuzzers suffer from the following main limitations: 1) lacking an overall analysis of the program to identify the most “rewarding” seeds, and 2) lacking an effective mutation strategy which could continuously select and mutates the more relevant “bytes” of the seeds. In this work, we propose an approach called ATTUZZ to address these two issues systematically. First, we propose a lightweight dynamic analysis technique that estimates the “reward” of covering each basic block and selects the most rewarding seeds accordingly. Second, we mutate the selected seeds according to a neural network model which predicts whether a certain “rewarding” block will be covered given certain mutations on certain bytes of a seed. The model is a deep learning model equipped with an attention mechanism which is learned and updated periodically whilst fuzzing. Our evaluation shows that ATTUZZ significantly outperforms 5 state-of-the-art grey-box fuzzers on 6 popular real-world programs and MAGMA data sets at achieving higher edge coverage and finding new bugs. In particular, ATTUZZ achieved 1.2X edge coverage and 1.8X bugs detected than AFL++ over 24-hour runs. In addition, ATTUZZ also finds 4 new bugs in the latest version of some popular software including p7zip and openUSD.

Keywords

Attention Model, Codes, Computer bugs, Deep learning, Electronic mail, Fuzzing, Image edge detection, Program Analysis, Recurrent neural networks

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Software Engineering

First Page

1

Last Page

18

ISSN

0098-5589

Identifier

10.1109/TSE.2023.3338129

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TSE.2023.3338129

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