mirror of https://github.com/python/cpython
62 lines
2.2 KiB
ReStructuredText
62 lines
2.2 KiB
ReStructuredText
Fuzz Tests for CPython
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======================
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These fuzz tests are designed to be included in Google's `oss-fuzz`_ project.
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oss-fuzz works against a library exposing a function of the form
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``int LLVMFuzzerTestOneInput(const uint8_t* data, size_t length)``. We provide
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that library (``fuzzer.c``), and include a ``_fuzz`` module for testing with
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some toy values -- no fuzzing occurs in Python's test suite.
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oss-fuzz will regularly pull from CPython, discover all the tests in
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``fuzz_tests.txt``, and run them -- so adding a new test here means it will
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automatically be run in oss-fuzz, while also being smoke-tested as part of
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CPython's test suite.
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In addition, the tests are run on GitHub Actions using CIFuzz for PRs to the
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main branch changing relevant files.
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Adding a new fuzz test
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----------------------
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Add the test name on a new line in ``fuzz_tests.txt``.
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In ``fuzzer.c``, add a function to be run::
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static int $fuzz_test_name(const char* data, size_t size) {
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...
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return 0;
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}
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And invoke it from ``LLVMFuzzerTestOneInput``::
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#if !defined(_Py_FUZZ_ONE) || defined(_Py_FUZZ_$fuzz_test_name)
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rv |= _run_fuzz(data, size, $fuzz_test_name);
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#endif
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Don't forget to replace ``$fuzz_test_name`` with your actual test name.
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``LLVMFuzzerTestOneInput`` will run in oss-fuzz, with each test in
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``fuzz_tests.txt`` run separately.
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Seed data (corpus) for the test can be provided in a subfolder called
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``<test_name>_corpus`` such as ``fuzz_json_loads_corpus``. A wide variety
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of good input samples allows the fuzzer to more easily explore a diverse
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set of paths and provides a better base to find buggy input from.
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Dictionaries of tokens (see oss-fuzz documentation for more details) can
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be placed in the ``dictionaries`` folder with the name of the test.
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For example, ``dictionaries/fuzz_json_loads.dict`` contains JSON tokens
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to guide the fuzzer.
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What makes a good fuzz test
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---------------------------
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Libraries written in C that might handle untrusted data are worthwhile. The
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more complex the logic (e.g. parsing), the more likely this is to be a useful
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fuzz test. See the existing examples for reference, and refer to the
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`oss-fuzz`_ docs.
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.. _oss-fuzz: https://github.com/google/oss-fuzz
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