kursh / deepstate

A unit test-like interface for fuzzing and symbolic execution

DeepState

DeepState is a framework that provides C and C++ developers with a common interface to various symbolic execution and fuzzing engines. Users can write one test harness using a Google Test-like API, then execute it using multiple backends without having to learn the complexities of the underlying engines. It supports writing unit tests and API sequence tests, as well as automatic test generation. Read more about the goals and design of DeepState in our paper.

Overview of Features

• Tests look like Google Test, but can use symbolic execution/fuzzing to generate data (parameterized unit testing)
  • Easier to learn than binary analysis tools/fuzzers, but provides similar functionality
• Already supports Manticore, Angr, Dr. Fuzz; more back-ends likely in future
  • Switch test generation tool without re-writing test harness
    • Work around show-stopper bugs
    • Find out which tool works best for your code under test
    • Different tools find different bugs/vulnerabilities
    • Fair way to benchmark/bakeoff tools
• Supports API-sequence generation with extensions to Google Test interface
  • Concise readable way (OneOf) to say "run one of these blocks of code"
  • Same construct supports fixed value set non-determinism
  • E.g., writing a POSIX file system tester is pleasant, not painful as in pure Google Test idioms
• Provides high-level strategies for improving symbolic execution/fuzzing effectiveness
  • Pumping (novel to DeepState) to pick concrete values when symbolic execution is too expensive
  • Automatic decomposition of integer compares to guide coverage-driven fuzzers

Supported Platforms

DeepState currently targets Linux, with macOS support in progress.

Dependencies

Build:

• CMake
• GCC with multilib support
• Python 2.7
• Setuptools

Runtime:

• Python 2.7
• Z3 (for the Manticore backend)

Building on Ubuntu 16.04 (Xenial)

$ sudo apt update && sudo apt-get install build-essential gcc-multilib cmake python python-setuptools
$ git clone https://github.com/trailofbits/deepstate deepstate
$ mkdir deepstate/build && cd deepstate/build
$ cmake ../
$ make

Installing

Assuming the DeepState build resides in $DEEPSTATE, run the following commands to install the DeepState python package:

$ virtualenv venv
$ . venv/bin/activate
$ python $DEEPSTATE/build/setup.py install

The virtualenv-enabled $PATH should now include two executables: deepstate and deepstate-angr. These are executors, which are used to run DeepState test binaries with specific backends (automatically installed as Python dependencies). The deepstate executor uses the Manticore backend while deepstate-angr uses angr. They share a common interface where you may specify a number of workers and an output directory for saving backend-generated test cases.

You can check your build using the test binaries that were (by default) built and emitted to deepstate/build/examples. For example, to use angr to symbolically execute the IntegerOverflow test harness with 4 workers, saving generated test cases in a directory called out, you would invoke:

$ deepstate-angr --num_workers 4 --output_test_dir out $DEEPSTATE/build/examples/IntegerOverflow

The resulting out directory should look something like:

out
└── IntegerOverflow.cpp
   ├── SignedInteger_AdditionOverflow
   │   ├── a512f8ffb2c1bb775a9779ec60b699cb.fail
   │   └── f1d3ff8443297732862df21dc4e57262.pass
   └── SignedInteger_MultiplicationOverflow
       ├── 6a1a90442b4d898cb3fac2800fef5baf.fail
       └── f1d3ff8443297732862df21dc4e57262.pass

Usage

DeepState consists of a static library, used to write test harnesses, and command-line executors written in Python. At this time, the best documentation are the examples and our paper.

Contributing

All accepted PRs are awarded bounties by Trail of Bits. Join the #deepstate channel on the Empire Hacking Slack to discuss ongoing development and claim bounties. Check the good first issue label for suggested contributions.

License

DeepState is released under The Apache License 2.0.