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.

The 2018 IEEE Cybersecurity Development Conference will include a full tutorial on effective use of DeepState.

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, file-based fuzzing with e.g., AFL; 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 and G++ 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 g++-multilib cmake python python-setuptools libffi-dev z3
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 is in the examples and in our paper.

Fuzzing with libFuzzer

If you install a recent-enough clang, and run cmake when you install with BUILD_LIBFUZZER defined, you can generate tests using LlibFuzzer. Because both DeepState and libFuzzer want to be main, this requires building a different executable for libFuzzer. The examples directory shows how this can be done. The libFuzzer executable works like any other libFuzzer executable, and the tests produced can be run using the normal DeepState executable.

Fuzzing with AFL

DeepState can also be used with a file-based fuzzer (e.g. AFL). There are a few steps to this. First, compile DeepState itself with any needed instrumentation. E.g., to use it with AFL, you might want to add something like:

SET(CMAKE_C_COMPILER /usr/local/bin/afl-gcc)
SET(CMAKE_CXX_COMPILER /usr/local/bin/afl-g++)

to deepstate/CMakeLists.txt. Second, do the same for your DeepState test harness and any code it links to you want instrumented. Finally, run the fuzzing via the interface to replay test files. For example, to fuzz the OneOf example, if we were in the deepstate/build/examples directory, you would do something like:

afl-fuzz -d -i corpus -o afl_OneOf -- ./OneOf --input_test_file @@ --abort_on_fail

where corpus contains at least one file to start fuzzing from. The file needs to be smaller than the DeepState input size limit, but has few other limitations (for AFL it should also not cause test failure). The abort_on_fail flag makes DeepState crashes and failed tests appear as crashes to the fuzzer.

To replay the tests from AFL:

./OneOf --input_test_files_dir afl_OneOf/crashes
./OneOf --input_test_files_dir afl_OneOf/queue

Finally, if an example has more than one test, you need to specify, with a fully qualified name (e.g., Arithmetic_InvertibleMultiplication_CanFail), which test to run, using the --input_which_test flag to the binary. By default, DeepState will run the last test defined.

You can compile with afl-clang-fast and afl-clang-fast++ for deferred instrumentation. You'll need code like:

#ifdef __AFL_HAVE_MANUAL_CONTROL
  __AFL_INIT();
#endif

just before the call to DeepState_Run() (which reads the entire input file) in your main.

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.

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