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Message-ID: <CAHXGaxAS6wW7s9oxeWqC=UNk2t=Lw7fT7=UmozsP5NpeL40Wbw@mail.gmail.com>
Date: Tue, 6 Dec 2016 10:26:14 -0800
From: Grant Murphy <grantcmurphy@...il.com>
To: oss-security@...ts.openwall.com
Subject: Re: Opensource Python whitebox code analysis tool recommendations
On Tue, Dec 6, 2016 at 9:02 AM, Fiedler Roman <Roman.Fiedler@....ac.at>
wrote:
> Hello list,
>
> I just stubled over effects of following programming error due to unwanted
> singleton in Python, bypassing intended process restrictions (allowed
> number
> of elements in my case) and of course data corruption:
>
> class A:
> def __init__(self, value=[]):
> self.value=value
> self.valueCloned=value[:]
> def show(self):
> print 'IDs value %x, cloned %x' % (id(self.value),
> id(self.valueCloned))
> def append(self, data):
> self.value.append(data)
>
> # Keep reference to avoid garbage collection interference.
> objFirst=A()
> objFirst.show()
> objNext=A()
> objNext.show()
> # Check references to prohibit optimization.
> if objFirst==objNext: raise Exception('Impossible')
>
>
>
> As this type of error seems to be more common in code, at least according
> to
> grep, are there tool recommendations to do automatic analysis of code?
>
> It should trace all non-trivial (not None, int, float, str, ...)
> constructor
> arguments assignments and catch at least problematic invocations like
> "self.value.append". A problem is, that in many cases just existence of
> constructor like the one before does not automatically lead to
> corruption/concurrency issues. For example the tool should not trigger on
> this
> (older but still in use) version of django_common/http.py or at least, when
> triggering, only at "json.dumps()".
>
> class JsonResponse(HttpResponse):
> def __init__(self, data={ }, errors=[ ], success=True):
> """
> data is a map, errors a list
> """
> json = json_response(data=data, errors=errors, success=success)
> super(JsonResponse, self).__init__(json, content_type='application/
> json')
>
> def json_response(data={ }, errors=[ ], success=True):
> data.update({
> 'errors': errors,
> 'success': len(errors) == 0 and success,
> })
> return json.dumps(data)
>
> Due to weak typing, it might be too hard to catch all problematic
> locations,
> e.g. field modified in subclass. Without source code analysis tools
> available
> to do such checks, I would also try out any approaches where the argument
> value is made immutable thus leading to crash in testbed.
>
> It would be great, if the tool would do the whole analysis more from the
> security than code quality perspective: it is more interesting to audit own
> code and referenced/redistributed third party stuff for things that "are
> very
> likely to be problematic/vulnerable" than have a quality tool recommending
> to
> change all those lines, which is not quite realistic.
>
> Kind regards,
> Roman
>
You could check out Bandit:
https://security.openstack.org/#bandit-static-analysis-for-python
I'm not sure it quite fits what you're after could be worth a look.
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