PyDySoFu 0.1a3

Description:

PyDySoFu 0.1a3

PyDySoFu - Python Dynamic Source Fuzzer
A library for fuzzing Python functions at runtime.
Contributors


Tom Wallis
School of Computing Science, University of Glasgow
GitHub ID: probablytom
[email protected]


Tim Storer
School of Computing Science, University of Glasgow
GitHub ID: twsswt
[email protected]


Overview
PyDySoFu is a library for performing source code fuzzing of Python programs at runtime. Fuzzing operations are
implemented in an extensible library of fuzzers. The fuzzers can be applied to functions in in two ways:

By constructing an Aspect Oriented Programming like advice dictionary, mapping function pointers to fuzzers
(recommended).
By decorating fuzzable operations with an @fuzz decorator, parameterised with the desired fuzzer.

The AOP approach is preferred because this separates concerns between the implementation of reference functions and the
specification of fuzzers, allowing many different fuzzings of the same program to be constructed.
Each fuzzing operator is a function that accepts the body of a work flow function (as a list of statements) and returns
a fuzzed list of statements.
Applications
PyDySoFu was originally developed as a fuzzing tool to simulate contingent behaviour in socio-technical systems.
However, there are a number of other possible applications:

Simulating Byzantine disruption in distributed system testing.
Simulations of complex behaviour in distributed systems.
Fallible behaviour in human like computing.
Modelling physical systems that are not amenable to stochastic analysis due to emergent complexity.

Available Fuzzers
The core library includes both atomic and composite fuzzers for building more complex behaviours:

Identity
Applying a fuzzer to a subset of function body steps using a filter. Filters provided include:
Identity
Random selection
n last steps
Excluding control structures
Inverting a selection
Removing steps
Replacing steps
Duplicating steps
Inserting extra steps
Shuffling steps
Applying a sequence of fuzz operators
Choosing a random fuzz operator to apply from a probability distribution.
Applying a fuzz operator conditionally.
Replacing the iterable of a foreach loop
Replacing a condition expression
Recursing into composite steps
Swapping if blocks

A number of demonstrator fuzzers are also provided that combine the core fuzzers described above:

Remove last step(s)
Duplicate last step
Remove random step

Tutorials and Examples

There is a Jupyter Notebook tutorial available ./tutorial.ipynb.