Program Fuzzing on High Performance Computing Resources
Abstract
American Fuzzy Lop (AFL) is an evolutionary fuzzer that is strategically implemented as a tool for discovering bugs in software during vulnerability research. This work seeks to understand how to best implement AFL on the High-Performance Computing resources available on the unclassified network at Sandia National Laboratories. We investigate various methods of executing AFL, requesting varying numbers of tasks on single compute nodes with 36 physical cores and 72 total threads. A Python script called Blue Claw is presented as an automated testbed generator tool to assist in the tedious process of creating and executing experiments of any scale and duration.
- Authors:
-
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Publication Date:
- Research Org.:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1492735
- Report Number(s):
- SAND-2019-0674
671718
- DOE Contract Number:
- AC04-94AL85000; NA0003525
- Resource Type:
- Technical Report
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING
Citation Formats
Cioce, Christian R., Loffredo, Daniel George, and Salim, Nasser J. Program Fuzzing on High Performance Computing Resources. United States: N. p., 2019.
Web. doi:10.2172/1492735.
Cioce, Christian R., Loffredo, Daniel George, & Salim, Nasser J. Program Fuzzing on High Performance Computing Resources. United States. https://doi.org/10.2172/1492735
Cioce, Christian R., Loffredo, Daniel George, and Salim, Nasser J. 2019.
"Program Fuzzing on High Performance Computing Resources". United States. https://doi.org/10.2172/1492735. https://www.osti.gov/servlets/purl/1492735.
@article{osti_1492735,
title = {Program Fuzzing on High Performance Computing Resources},
author = {Cioce, Christian R. and Loffredo, Daniel George and Salim, Nasser J.},
abstractNote = {American Fuzzy Lop (AFL) is an evolutionary fuzzer that is strategically implemented as a tool for discovering bugs in software during vulnerability research. This work seeks to understand how to best implement AFL on the High-Performance Computing resources available on the unclassified network at Sandia National Laboratories. We investigate various methods of executing AFL, requesting varying numbers of tasks on single compute nodes with 36 physical cores and 72 total threads. A Python script called Blue Claw is presented as an automated testbed generator tool to assist in the tedious process of creating and executing experiments of any scale and duration.},
doi = {10.2172/1492735},
url = {https://www.osti.gov/biblio/1492735},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 01 00:00:00 EST 2019},
month = {Tue Jan 01 00:00:00 EST 2019}
}
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