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Title: 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 autornated testbed generator tool to assist in the tedious process of creating and executing experiments of any scale and duration. This page left blank

Authors:
; ;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
National Security Agency
OSTI Identifier:
1492735
Report Number(s):
SAND2019-0674
671718
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

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. doi:10.2172/1492735.
Cioce, Christian R, Loffredo, Daniel George, and Salim, Nasser J. Tue . "Program Fuzzing on High Performance Computing Resources.". United States. doi: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 autornated testbed generator tool to assist in the tedious process of creating and executing experiments of any scale and duration. This page left blank},
doi = {10.2172/1492735},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}