This was causing issues with imports with many "dots" in the name.
Previously, the test added in this commit would not have the desired
result for the `check` call.
This was due to bad manual magic: restricting the attribute name makes
sense when we're talking about submodules of a package, but it doesn't
when we're talking about reexported modules.
Also (hopefully) fixes the tests so that the Python 3-specific bits are
ignored under Python 2.
Extends the tests to
1. Account parts of the test code that may be specific to Python 2 or 3,
2. Also track which arguments passed to `check` are references to
modules.
The latter revealed a bunch of spurious results, which I have annotated
accordingly.
I could have sworn I added all of them to the batch, but somehow these slipped through.
Co-authored-by: yoff <lerchedahl@gmail.com>
Co-authored-by: Rasmus Wriedt Larsen <rasmuswriedtlarsen@gmail.com>
I initially created this as a dataflow test, but then realized it could
just be an ESSA test. I cound't find any existing ESSA tests though :|
so created a new dir for it.
A slightly complicated test setup. I wanted to both make sure I captured
the semantics of Python and also the fact that the kinds of global flow
we expect to see are indeed present.
The code is executable, and prints out both when the execution reaches
certain files, and also what values are assigned to the various
attributes that are referenced throughout the program. These values are
validated in the test as well.
My original version used introspection to avoid referencing attributes
directly (thus enabling better error diagnostics), but unfortunately
that made it so that the model couldn't follow what was going on.
The current setup is a bit clunky (and Python's scoping rules makes it
especially so -- cf. the explicit calls to `globals` and `locals`), but
I think it does the job okay.
Note: I kept the modeling using the old approach with type-trackers
instead of `DataFlow::MethodCallNode`.
I would like a meta query for DCA to show sinks before doing this, so I
can be absolutely sure we don't loose out on any important sinks on
this... so will postpone this work to a small one-off task (added to my
todo list).
I realized the modeling was done in a non-recommended way, so I changed
the modeling. It was very nice that I could use API graphs for the flask
part, and a little sad when I couldn't for Django/Tornado.
It's really hard to audit that this is all good.. I tried my best with
`icdiff` though -- and there is a problem with
ql/src/experimental/Security/CWE-348/ClientSuppliedIpUsedInSecurityCheck.ql
that needs to be fixed in the next commit
