))
Jared DeMott, Dr. Richard Enbody & Dr. Bill Punch: Revolutionizing the Field of Grey-box Attack Surface Testing with Evolutionary Fuzzing
Manage episode 152211981 series 1053194
Content provided by Black Hat Briefings, USA 2007 [Video] Presentations from the security conference.. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Black Hat Briefings, USA 2007 [Video] Presentations from the security conference. or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ro.player.fm/legal.
Runtime code coverage analysis is feasible and useful when application source code is not available. An evolutionary test tool receiving such statistics can use that information as fitness for pools of sessions to actively learn the interface protocol. We call this activity grey-box fuzzing. We intend to show that, when applicable, grey-box fuzzing is more effective at finding bugs than RFC compliant or capture-replay mutation black-box tools. This research is focused on building a better/new breed of fuzzer. The impact of which is the discovery of difficult to find bugs in real world applications which are accessible (not theoretical).
We have successfully combined an evolutionary approach with a debugged target to get real-time grey-box code coverage (CC) fitness data. We build upon existing test tool General Purpose Fuzzer (GPF) [8], and existing reverse engineering and debugging framework PaiMei [10] to accomplish this. We call our new tool the Evolutionary Fuzzing System (EFS).
We have shown that it is possible for our system to learn the targets language (protocol) as target communication sessions become more fit over time. We have also shown that this technique works to find bugs in a real world application. Initial results are promising though further testing is still underway.
This talk will explain EFS, describing its unique features, and present preliminary results for one test case. We will also discuss future research efforts.
…
continue reading
We have successfully combined an evolutionary approach with a debugged target to get real-time grey-box code coverage (CC) fitness data. We build upon existing test tool General Purpose Fuzzer (GPF) [8], and existing reverse engineering and debugging framework PaiMei [10] to accomplish this. We call our new tool the Evolutionary Fuzzing System (EFS).
We have shown that it is possible for our system to learn the targets language (protocol) as target communication sessions become more fit over time. We have also shown that this technique works to find bugs in a real world application. Initial results are promising though further testing is still underway.
This talk will explain EFS, describing its unique features, and present preliminary results for one test case. We will also discuss future research efforts.
89 episoade
Distribuie
