fuddly: a fuzzing and data manipulation framework

List of features

• Graph-based data model that enables:
  • to represent complex data formats and also to mix them
  • complex data manipulations
  • to dissect/absorb existing data
  • generation & mutation fuzzing strategy
• Fuzzing automation framework:
  • target abstraction
  • monitoring means based on independant probes
  • replay & logging
  • data manipulation based on operators (objects that implement specific data transformation)
  • scenario infrastructure (for modeling protocol logic)
  • virtual directors
• and so on...

What's still missing

• Refer to TODO file

About documentation

• Documentation is available here.
• In order to generate the documentation from the source, follow these steps:
  1. go to the folder docs/
  2. execute make html to generate HTML documentation
  3. execute make latexpdf to generate PDF documentation
  4. generated documentation is located in docs/build/

Launch fuddly test cases

The package test include all unit & integration test cases of fuddly itself. From the src/ directory, usage is as follows:

• To launch all the tests, issue the command:

  >> python -m fuddly.test -a
  

• To launch all the tests but the longer ones, issue the command:

  >> python -m fuddly.test
  

• To avoid data model specific test cases use the option --ignore-dm-specifics

• To launch a specific test category issue the following command:

  >> python -m fuddly.test fuddly.test.<test_package>.<test_module>.<Test_Class>.<test_method>
  

Miscellaneous

• Don't forget to populate <fuddly data folder>/imported_data/ with sample files for data models that need it
• Note that when the fuddly shell is launched, the path of the fuddly data folder is displayed as well as its configuration folder

Dependencies

• Compatible with Python3
• Mandatory:
  • sqlite3: SQLite3 data base
• Optional:
  • xtermcolor: Terminal color support
  • graphviz: For graphic visualization (e.g., scenario display)
  • paramiko: Python implementation of the SSHv2 protocol
  • serial: For serial port access
  • cups: Python bindings for libcups
  • rpyc: Remote Python Call (RPyC), a transparent and symmetric RPC library
  • pyxdg: XDG Base Directory support
• For testing:
  • ddt: Used for data-driven tests
  • mock: Used for mocking
• For documentation generation:
  • sphinx: sphinx >= 1.3 (with builtin napoleon extension)
  • texlive (optional): Needed to generate PDF documentation
  • readthedocs theme (optional): Privileged html theme for sphinx