Add basic documentation on function modeling (#386)
* Add basic models docs * Phrasing * Minor fixes
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API
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===
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API Reference
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=============
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This API is under active development, and should be considered unstable.
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@ -8,6 +8,7 @@ Manticore is a prototyping tool for dynamic binary analysis, with support for sy
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:caption: Contents:
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api
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models
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Indices and tables
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29
docs/models.rst
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29
docs/models.rst
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Function Models
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===============
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The Manticore function modeling API can be used to override a certain
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function in the target program with a custom implementation in Python.
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This can greatly increase performance.
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Manticore comes with implementations of function models for some common library routines (core models),
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and also offers a user API for defining user-defined models.
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To use a core model, use the :meth:`~manticore.core.state.State.invoke_model` API. The
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available core models are documented in the API Reference::
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from manticore.models import strcmp
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addr_of_strcmp = 0x400510
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@m.hook(addr_of_strcmp)
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def strcmp_model(state):
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state.invoke_model(strcmp)
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To implement a user-defined model, implement your model as a Python function, and pass it to
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:meth:`~manticore.core.state.State.invoke_model`. See the
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:meth:`~manticore.core.state.State.invoke_model` documentation for more. The
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`core models <https://github.com/trailofbits/manticore/blob/master/manticore/models.py>`_
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are also good examples to look at and use the same external user API.
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@ -359,6 +359,8 @@ class State(object):
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function, the following arguments correspond to the arguments of the C function
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being modeled. If the `model` models a variadic function, the following argument
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is a generator object, which can be used to access function arguments dynamically.
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The `model` callable should simply return the value that should be returned by the
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native function being modeled.
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:param callable model: Model to invoke
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'''
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