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OSS-Fuzz initial impressions

 4 years ago
source link: https://alexgaynor.net/2016/dec/03/oss-fuzz-initial-impressions/
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OSS-Fuzz initial impressions

Sat, Dec 3, 2016

In case you haven’t heard, this week Google announced a project called OSS-Fuzz. The basic idea of fuzz testing is take random inputs, throw them at a program, and see if it breaks. The basic idea of OSS-Fuzz is to use buttloads of servers that Google has lying around to do fuzz testing for open source. OSS-Fuzz already has an impressive trophy case of vulnerabilities found, from running over 4 trillion test cases per week.

I like open source, and I like security, and I don’t have my own personal server farm with thousands of cores, so I figured I’d give OSS-Fuzz a spin. Here’s what I found.

The first step to using OSS-Fuzz is to send a pull request proposing your project for inclusion (OSS-Fuzz is currently limited to projects with “a significant user base and/or be critical to the global IT infrastructure”). I decided to go with libyaml, and sending a pull request is easy enough. When OSS-Fuzz finds a crash, it files it on a private bug tracker where only maintainers can see the details until it’s fixed, so you’ll see Ian Cordasco needed to give explicit permission for me to have access to the bug reports, since I’m not a libyaml maintainer.

Once that’s accepted, you need to write the fuzzer itself. There’s a few pieces you’ll need:

  • A Dockerfile which clones the source and gets everything in order.
  • A build.sh which compiles the library you want to test, and your fuzzing program.
  • And the driver: a C++ function, LLVMFuzzerTestOneInput(const uint8_t *, size_t).

You can also optionally include a corpus of seed files and a dictionary. For libyaml I use the examples/ directory from the upstream repository as a seed corpus, these show off various YAML syntaxes. The dictionary is a simple list of every token I could find YAML using. Both of these help the fuzzer to generate more interesting inputs.

All in all the pull request was less than 100 lines of code. Once it’s merged, the infrastructure will notice in short order, build your fuzzer, and it’s off to the races!

So that’s all the work you have to do. What do you get in return?

  • Buttloads of compute power. In less than a day, OSS-Fuzz ran over 17 billion testcases against libyaml. Based on the reported executions-per-second, it looks like libyaml received about 30 days of CPU time, in less than one calendar day.
  • Life cycle management. OSS-Fuzz will automatically file a private bug when it discovers a crash, it’ll leave a comment on the bug when it thinks the crash has been fixed, and then make the bug public seven days after it’s been fixed. It also handles automatically rebuilding when the upstream source changes.
  • Crash aggregation. OSS-Fuzz will try to automatically aggregate crashes with the same root cause, so you can focus on fixing them, rather than disentangling them.
  • Coverage reports. OSS-Fuzz will show you a report of what coverage looks like from running all the generated inputs against your library. This helps you make sure that the fuzzing is finding all the interesting paths in your programs.

Overall, using OSS-Fuzz was an extremely good experience. It was almost no work to write a fuzzing function and get it running, and OSS-Fuzz handles tons of the details around making fuzzing at scale practical; this makes the experience far more pleasant than if I’d jerry–rigged something together myself.

I’m hopeful that OSS-Fuzz will be a huge leap forward for the security of critical open source software. If you’ve got a popular open source library written in C/C++ that processes untrusted user input, you owe it to yourself to get it running on OSS-Fuzz.


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