MoonLight is a research project looking at efficient methods for designing a corpus typically for use in fuzzing campaigns. We call this design process distillation.
Corpus distillation is the process by which we take a very large corpus of file types - e.g. 100,000 PDF files - called seeds - and choose a much smaller subset to fuzz against - say 1,000 PDF files. However, we want this smaller subset corpus to have the same "expressive power" as the original large corpus. We don't want to lose any information in the distillation process.
Currently "expressive power" means maximising code coverage. However, we are planning other types of measures of interest, such as execution complexity and usage of specific libraries or system calls.
The project name "MoonLight" is a pun on the word distillation. We use the term distillation in preference to the commonly used term reduction. This is because in fuzzing parlance, "reduction" is a term that is also used in the crash triage process.
We have included five benchmark corpora in this repo. The following table gives you an understanding of what level of distillations you should get when you run the tool.
| File Type | Collection Size | Distilled Size | Improvement Gain |
|---|---|---|---|
| 42,056 | 664 | 63 | |
| DOC | 7,836 | 745 | 10 |
| PNG | 4,831 | 94 | 51 |
| TTF | 5,666 | 27 | 210 |
| HTML | 69,991 | 410 | 171 |
Now if you were fuzzing Adobe Acrobat, instead of using 42,000 files you would only need the much smaller subset of 664 files.
Sometimes what is important is not just the number of seeds in your corpus but the total weight (or size) of the seeds in bytes. This is particularly important if your fuzzer is IO bound. In this case the corpus design and construction is choosing seeds which not only maximise code coverage but also have the smallest weight.
MoonLight can perform both weighted and unweighted distillations.
For more information on the performance of MoonLight against the current state of the art see Results and our paper on Arxiv. If you use MoonLight, please cite our work:
@article{2020:Herrera:MoonLight,
author = {Adrian Herrera and
Hendra Gunadi and
Liam Hayes and
Shane Magrath and
Felix Friedlander and
Maggi Sebastian and
Michael Norrish and
Antony L. Hosking},
title = {Corpus Distillation for Effective Fuzzing: A Comparative Evaluation},
journal = {CoRR},
volume = {abs/1905.13055},
year = {2020},
url = {http://arxiv.org/abs/1905.13055},
archivePrefix = {arXiv},
eprint = {1905.13055},
}
See here
See here
For more information on the expected input format of seed trace data see here.