[Dailydave] Code analysis and scale

Dave Aitel dave at immunityinc.com
Tue Sep 8 14:49:26 EDT 2015

Reminder: If you don't post from the email address that you are
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it to the list.

Sometimes people get upset that their post isn't showing up, and this is
why and it's not my fault, or your fault, but simply how the mailing
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In any case, very interested in how someone combines AFL-style fuzzing
with symbolic execution.


On 9/8/2015 12:06 PM, Andrew Ruef wrote:
> does cloud9 count as distributed? that is open source at least. 
> surely also different CGC systems count as distributed? we used 10k cores and TBs of RAM on symbolic execution... 
>> On Sep 6, 2015, at 12:02, Halvar Flake <HalVar at gmx.de> wrote:
>> Hey all,
>> while I really should not be posting here while I am on my kinda-sabbatical, the ocean
>> is entirely flat today and I don't feel like doing real work - so posting to DD is a 
>> nice middle ground.
>> There was a period in my life where at each and every conference I attended, some
>> bright and very motivated youngster would come up to me and excitedly tell me about
>> this new reverse engineering framework he was building - usually in Python or Ruby - where
>> everything was an object, and it would all be so great once development got a bit further.
>> Over the years, I must have heard about 10+ such frameworks, and each time the
>> authors eventually ran into the same problem: Binaries are larger than people think,
>> and your RAM is more limited than you think.
>> A larger real-world application will, once all dependencies are loaded and mapped
>> into it's address space, easily exceed 100 megs of executable code. With x86_64
>> instructions averaging a bit above 4 bytes, we are quickly talking about 25m+ instructions.
>> If, for some bizarre reason, you are confined to a 32-bit process, you have 3GB of
>> address space to distribute among 25m+ instructions, which means that in the best
>> case you can afford to spend 128 bytes per instruction - not counting heap overhead.
>> On my machine, an empty Python dictionary takes 280 bytes, an empty string 37.
>> In a more realistic scenario, you have 32 GB of RAM in your machine, which gives you
>> a bit more than 1k of memory per instruction. That should be plenty, no?
>> Not so much - if you want to perform any sophisticated analysis on code, you will want
>> to have some approximation of the program state associated with program points, and
>> the number of program points where a reasonable approximation of this can be done
>> in 1k or less is not going to be large.
>> Where am I going with all this rambling?
>> While machine code is not "big data" in the modern, search-enginey-social-networky-sense,
>> real-world-programs are "not small data" - as soon as you wish to associate extra
>> information with parts of the program, you will quickly exceed the ability to keep it all in
>> memory on a single machine - provided you analyse something "real" instead of 
>> notepad.
>> It is interesting that there are no distributed static analysis frameworks yet - and how easy
>> it is to conveniently forget about scale issues when "architecting" (e.g. dreaming about)
>> the reverse engineering framework one would like to have.
>> Cheers,
>> Halvar
>> PS: It is possible that the successes of fuzzing are due mainly due to the
>> fact that it happens to be embarrassingly parallel.
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