A unit test-like interface for fuzzing and symbolic execution
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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.

The 2018 IEEE Cybersecurity Development Conference included 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, libFuzzer, 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
  • Provides test replay for regression plus effective automatic test case reduction to aid debugging
  • 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

To run these tests, you can just use the native executable, e.g.:

$DEEPSTATE/build/examples/IntegerOverflow --input_test_dir out

to run all the generated tests, or

$DEEPSTATE/build/examples/IntegerOverflow --input_test_files_dir out/IntegerOverflow.cpp/SignedInteger_AdditionOverflow --input_which_test SignedInteger_AdditionOverflow

to run the tests in one directory (in this case, you want to specify which test to run, also). You can also run a single test, e.g.:

$DEEPSTATE/build/examples/IntegerOverflow --input_test_file out/IntegerOverflow.cpp/SignedInteger_AdditionOverflow/a512f8ffb2c1bb775a9779ec60b699cb.fail--input_which_test SignedInteger_AdditionOverflow

In the absence of an --input_which_test argument, DeepState defaults to the last-defined test. Run the native executable with the --help argument to see all DeepState options.

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. A more extensive example, using DeepState and libFuzzer to test a user-mode file system, is available here; in particular the Tests.cpp file and CMakeLists.txt show DeepState usage.

Fuzzing with libFuzzer

If you install clang 6.0 or later, and run cmake when you install with the BUILD_LIBFUZZER environment variable 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. For example, generating some tests of the OneOf example (up to 5,000 runs), then running those tests to examine the results, would look like:

mkdir OneOf_libFuzzer_corpus
./OneOf_LF -runs=5000 OneOf_libFuzzer_corpus
./OneOf --input_test_files_dir OneOf_libFuzzer_corpus

Use the LIBFUZZER_WHICH_TEST environment variable to control which test libFuzzer runs, using a fully qualified name (e.g., Arithmetic_InvertibleMultiplication_CanFail). By default, you get the last test defined (which works fine if there is only one test). Obviously, libFuzzer may work better if you provide a non-empty corpus, but fuzzing will work even without an initial corpus, unlike AFL.

One hint when using libFuzzer is to avoid dynamically allocating memory during a test, if that memory would not be freed on a test failure. This will leak memory and libFuzzer will run out of memory very quickly in each fuzzing session.

Test case reduction

While tests generated by symbolic execution are likely to be highly concise already, fuzzer-generated tests may be much larger than they need to be.

DeepState provides a test case reducer to shrink tests intelligently, aware of the structure of a DeepState test. For example, if your executable is named TestFileSystem and the test you want to reduce is named rmdirfail.test you would use it like this:

deepstate-reduce ./TestFileSystem rmdirfail.test minrmdirfail.test

In many cases, this will result in finding a different failure or crash that allows smaller test cases, so you can also provide a string that controls the criteria for which test outputs are considered valid reductions (by default, the reducer looks for any test that fails or crashes). Only outputs containing the --criteria are considered to be valid reductions:

deepstate-reduce ./TestFileSystem rmdirfail.test minrmdirfail.test --criteria "FATAL: /root/testfs/super.c(252)"

The output will look something like:

ORIGINAL TEST HAS 119 BYTES
ONEOF REMOVAL REDUCED TEST TO 103 BYTES
ONEOF REMOVAL REDUCED TEST TO 87 BYTES
ONEOF REMOVAL REDUCED TEST TO 67 BYTES
ONEOF REMOVAL REDUCED TEST TO 51 BYTES
BYTE RANGE REMOVAL REDUCED TEST TO 50 BYTES
BYTE RANGE REMOVAL REDUCED TEST TO 49 BYTES
BYTE REDUCTION: BYTE 3 FROM 4 TO 0
BYTE REDUCTION: BYTE 43 FROM 4 TO 0
ONEOF REMOVAL REDUCED TEST TO 33 BYTES
ONEOF REMOVAL REDUCED TEST TO 17 BYTES
BYTE REDUCTION: BYTE 7 FROM 2 TO 1
BYTE REDUCTION: BYTE 15 FROM 2 TO 1
NO REDUCTIONS FOUND
PADDING TEST WITH 3 ZEROS

WRITING REDUCED TEST WITH 20 BYTES TO minrmdirfail.test

You can use --which_test <testname> to specify which test to run, as with the --input_which_test options to test replay.

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.