Optimizing seed inputs in fuzzing with machine learning

TL;DR

This paper introduces a machine learning framework that enhances seed input quality in fuzzing, especially for complex input formats like PDFs, leading to better code coverage and crash detection.

Contribution

It presents a novel neural network-based approach to generate improved seed inputs for fuzzing PDF viewers, improving coverage and bug discovery.

Findings

Increased code coverage in PDF viewers

Higher likelihood of detecting crashes

Effective generation of seed inputs using neural networks

Abstract

The success of a fuzzing campaign is heavily depending on the quality of seed inputs used for test generation. It is however challenging to compose a corpus of seed inputs that enable high code and behavior coverage of the target program, especially when the target program requires complex input formats such as PDF files. We present a machine learning based framework to improve the quality of seed inputs for fuzzing programs that take PDF files as input. Given an initial set of seed PDF files, our framework utilizes a set of neural networks to 1) discover the correlation between these PDF files and the execution in the target program, and 2) leverage such correlation to generate new seed files that more likely explore new paths in the target program. Our experiments on a set of widely used PDF viewers demonstrate that the improved seed inputs produced by our framework could significantly increase the code coverage of the target program and the likelihood of detecting program crashes.