# DeepState

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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](https://agroce.github.io/bar18.pdf).

The [2018 IEEE Cybersecurity Development Conference](https://secdev.ieee.org/2018/home) 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)

```shell
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:

```shell
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:

```shell
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](/examples) and in our [paper](https://agroce.github.io/bar18.pdf).

## Fuzzing

DeepState now can 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:

```shell
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:

```shell
./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 first 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](https://empireslacking.herokuapp.com/) to discuss ongoing development and claim bounties. Check the [good first issue](https://github.com/trailofbits/deepstate/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) label for suggested contributions.

## License

DeepState is released under [The Apache License 2.0](LICENSE).