ZEzer01·Sep 10, 2016
aoh also wrote the very awesome Radamsa fuzzing engine in it - https://github.com/aoh/radamsa
In Owl Lisp – A purely functional Scheme that compiles to Caoh
radamsa
Testing & Quality2016–2016
aoh / radamsa
radamsa
a general-purpose fuzzer
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3Mentions
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a general-purpose fuzzer
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HN mentions by week3 dated mentions
Observed mentions: 2016-07-12 15:22:19 UTC to 2016-09-10 20:46:34 UTC. Weeks start Monday, UTC.
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View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Jul 11 | 2016-07-11 00:00:00 UTC | 2016-07-18 00:00:00 UTC | 1 | 0 | 1 |
| Jul 18 | 2016-07-18 00:00:00 UTC | 2016-07-25 00:00:00 UTC | 0 | 0 | 0 |
| Jul 25 | 2016-07-25 00:00:00 UTC | 2016-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 1 | 2016-08-01 00:00:00 UTC | 2016-08-08 00:00:00 UTC | 0 | 0 | 0 |
| Aug 8 | 2016-08-08 00:00:00 UTC | 2016-08-15 00:00:00 UTC | 0 | 0 | 0 |
| Aug 15 | 2016-08-15 00:00:00 UTC | 2016-08-22 00:00:00 UTC | 0 | 0 | 0 |
| Aug 22 | 2016-08-22 00:00:00 UTC | 2016-08-29 00:00:00 UTC | 0 | 0 | 0 |
| Aug 29 | 2016-08-29 00:00:00 UTC | 2016-09-05 00:00:00 UTC | 0 | 0 | 0 |
| Sep 5 | 2016-09-05 00:00:00 UTC | 2016-09-12 00:00:00 UTC | 2 | 1 | 1 |
What Hacker News says
3 sourcesRadamsa – a general-purpose fuzzer
1 points · Open discussionI worked on the cyber reasoning system (CRS) at Trail of Bits for our entry into the Cyber Grand Challenge [1]. Some slides describing the system are here [2].
Specifically, I implemented our fuzzer. I created a dynamic binary translator [3] that emulated the DECREE [4] operating system and x86 arhcitecture. It had the Radamsa [5] mutator built-in, along with a number of other simpler mutators.
I think our fuzzer out-performed our competitors, though I am biased ;-) The fuzzer was single-threaded, but could perform more than a million fuzz/mutate-execute (with coverage) iterations every two hours. Before I optimized it, it beat the pants off PIN [6]. We ran many such fuzzer processes concurrently. They would saturate the CPUs, and actually performed no I/O because I emulated all I/O in memory ;-) This was key to us achieving such high-th
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roughput.
Our fuzzer wasn't super smart (though Radamsa is), but it benefited a lot from a feedback loop with our symbolic executors [7]. The symbolic executors would produce inputs that would then get fuzzed. These inputs could feed back into the symbolic executors, etc. That added more brains to our system.
All in all, we ran the CRS across something like 300 large EC2 nodes (across three availability zones). Per node, 8 or so fuzzers processes were running constantly for 24 hours. I'd ballpark that as 28.8 billion mutate+execute cycles.
In conclusion, the key for us was to make a production-quality, high-throughput fuzzer that did only one thing really well and really fast, then complement it with other more sophisticated tools like symbolic executors.
[1] https://blog.trailofbits.com/2015/07/15/how-we-fared-in-the-...
[2] http://infiltratecon.com/archives/Slides_Artem_Dinaburg.pdf
[3] https://en.wikipedia.org/wiki/Binary_translation
[4] https://github.com/CyberGrandChallenge/libcgc
[5] https://github.com/aoh/radamsa
[6] https://software.intel.com/en-us/articles/pin-a-dynamic-bina...
[7] https://en.wikipedia.org/wiki/Symbolic_execution