mirror of https://github.com/google/oss-fuzz.git
109 lines
5.7 KiB
Markdown
109 lines
5.7 KiB
Markdown
# Ideal integration with OSS-Fuzz
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OSS projects have different build and test systems. So, we can not expect them
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to have a unified way of implementing and maintaining fuzz targets and integrating
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them with OSS-Fuzz. However, we will still try to give recommendations on the preferred ways.
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Here are several features (starting from the easiest) that will make automated fuzzing
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simple and efficient, and will allow to catch regressions early on in the development cycle.
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## Fuzz Target
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The code of the [fuzz target(s)](http://libfuzzer.info/#fuzz-target) should be part of the project's source code repository.
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All fuzz targets should be easily discoverable (e.g. reside in the same directory, or follow the same naming pattern, etc).
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This makes it easy to maintain the fuzzers and minimizes breakages that can arise as source code changes over time.
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Make sure to fuzz the target locally for a small period of time to ensure that
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it does not crash, hang, or run out of memory instantly.
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See details at http://libfuzzer.info and http://tutorial.libfuzzer.info.
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The interface between the [fuzz target]((http://libfuzzer.info/#fuzz-target))
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and the fuzzing engines is C, so you may use C or C++ to implement the fuzz target.
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Examples:
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[boringssl](https://github.com/google/boringssl/tree/master/fuzz),
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[SQLite](https://www.sqlite.org/src/artifact/ad79e867fb504338),
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[s2n](https://github.com/awslabs/s2n/tree/master/tests/fuzz),
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[openssl](https://github.com/openssl/openssl/tree/master/fuzz),
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[FreeType](http://git.savannah.gnu.org/cgit/freetype/freetype2.git/tree/src/tools/ftfuzzer),
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[re2](https://github.com/google/re2/tree/master/re2/fuzzing),
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[harfbuzz](https://github.com/behdad/harfbuzz/tree/master/test/fuzzing),
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[pcre2](http://vcs.pcre.org/pcre2/code/trunk/src/pcre2_fuzzsupport.c?view=markup),
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[ffmpeg](https://github.com/FFmpeg/FFmpeg/blob/master/tools/target_dec_fuzzer.c).
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## Seed Corpus
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The *corpus* is a set of inputs for the fuzz target (stored as individual files).
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When starting the fuzzing process, one should have a "seed corpus",
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i.e. a set of inputs to "seed" the mutations.
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The quality of the seed corpus has a huge impact on fuzzing efficiency as it allows the fuzzer
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to discover new code paths more easily.
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The ideal corpus is a minimal set of inputs that provides maximal code coverage.
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For better OSS-Fuzz integration,
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the seed corpus should be available in revision control (can be same or different as the source code).
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It should be regularly extended with the inputs that (used to) trigger bugs and/or touch new parts of the code.
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Examples:
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[boringssl](https://github.com/google/boringssl/tree/master/fuzz),
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[openssl](https://github.com/openssl/openssl/tree/master/fuzz),
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[nss](https://github.com/mozilla/nss-fuzzing-corpus) (corpus in a separate repo).
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## Regression Testing
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The fuzz targets should be regularly tested (not necessarily fuzzed!) as a part of the project's regression testing process.
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One way to do so is to link the fuzz target with a simple driver
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(e.g. [this one](https://github.com/llvm-mirror/llvm/tree/master/lib/Fuzzer/standalone))
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that runs the provided inputs and use this driver with the seed corpus created in previous step.
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It is recommended to use [sanitizers](https://github.com/google/sanitizers) during regression testing.
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Examples: [SQLite](https://www.sqlite.org/src/artifact/d9f1a6f43e7bab45),
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[openssl](https://github.com/openssl/openssl/blob/master/fuzz/test-corpus.c)
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## Fuzzing dictionary
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For some input types, a simple dictionary of tokens used by the input language
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can have a dramatic positive effect on fuzzing efficiency.
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For example, when fuzzing an XML parser, a dictionary of XML tokens will help.
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AFL has a [collection](https://github.com/rc0r/afl-fuzz/tree/master/dictionaries)
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of such dictionaries for some of the popular data formats.
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Ideally, a dictionary should be maintained alongside the fuzz target.
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The syntax is described [here](http://libfuzzer.info/#dictionaries).
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## Build support
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A plethora of different build systems exist in the open-source world.
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And the less OSS-Fuzz knows about them, the better it can scale.
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An ideal build integration for OSS-Fuzz would look like this:
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* For every fuzz target `foo` in the project, there is a build rule that builds `foo_fuzzer`,
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a binary that contains the fuzzing entry point (`LLVMFuzzerTestOneInput`)
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and all the code it depends on, and that uses the `main()` function from `$LIB_FUZZING_ENGINE`
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(env var [provided](new_project_guide.md) by OSS-Fuzz environment).
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* The build system supports changing the compiler and passing extra compiler
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flags so that the build command for a `foo_fuzzer` looks similar to this:
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```bash
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# Assume the following env vars are set:
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# CC, CXX, CFLAGS, CXXFLAGS, LIB_FUZZING_ENGINE
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$ make_or_whatever_other_command foo_fuzzer
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```
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This will allow to have minimal OSS-Fuzz-specific configuration and thus be more robust.
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There is no point in hardcoding the exact compiler flags in the build system because they
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a) may change and b) are different depending on the fuzzing engine and the sanitizer being used.
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## Not a project member?
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If you are a member of the project you want to fuzz, most of the steps above are simple.
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However in some cases, someone outside the project team may want to fuzz the code
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and the project maintainers are not interested in helping.
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In such cases, we can host the fuzz targets, dictionaries, etc in OSS-Fuzz's
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repository and mention them in the Dockerfile.
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Examples: [libxml2](../projects/libxml2), [c-ares](../projects/c-ares), [expat](../projects/expat).
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This is far from ideal because the fuzz targets will not be continuously tested
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and hence may quickly bitrot.
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If you are not a project maintainer, we may not be able to CC you to security bugs found by OSS-Fuzz.
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