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Bug fixing guidance

This page provides brief guidance on how to prioritise and fix bugs reported by OSS-Fuzz.

Threat modelling

In general the severity of an issue reported by OSS-Fuzz must be determined relative to the threat model of the project under analysis. Therefore, although the fuzzers OSS-Fuzz makes an effort into determining the severity of the bug the true severity of the bug depends on the threat model of the project.

Bug prioritisation

Security issues

These are the top priority of solving. A label is attached to these on the OSS-Fuzz testcase page and you can also search up all of these on monorail using the search pattern -Bug=security.

Issues of this kind include issues reported by Address Sanitizer, e.g. heap-based buffer overflows, stack-based buffer overflows and use-after-frees.

Functional issues and memory leaks

These are issues that in general can tamper with the functionality of the application. The bugs that have highest priority in this case are those that can be easily triggered by an untrusted user of the project.

Timeouts and out-of-memory

These are in general the least prioritised issues to solve.

Bug prioritisation of non C/C++ projects

Currently there is no prioritisation of bugs in non C/C++ projects. As such, in this scenario it is crucial you do the analysis yourself relative to the threat model of your project.

Non-reproducible bugs

OSS-Fuzz will report some bugs that are labeled Reliably reproduces: NO and these can be tricky to deal with. A non-reproducible bug is an issue that OSS-Fuzz did indeed discover, however, OSS-Fuzz is unable to reproduce the bug with python infra/helper.py reproduce. In general, our suggestion is to do analysis of the bug and determine whether there in fact is an issue.

The non-reproducible bugs can be of varying nature. Some of these bugs will be due to some internal state of the target application being manipulated over the cause of several executions of the fuzzer function. This could be several hundreds or even thousands of executions and the bug may not be reproducible by a single fuzzer test-case, however, there is indeed a bug in the application. There are other reasons why bugs may be non-reproducible and in general any non-determinism introduced into the application can have an effect on this.

In the case of non-reproducible bugs our advice is to put effort into analysing the potential bug and also assess whether this is due to some internal state that persists between each fuzz run. If that is indeed the case then we also suggest investigating whether the fuzzer can be written such that the internal state in the code will be reset between each fuzz run.

Should all reported issues be solved?

It is reported by some project maintainers that fixing timeout issues reported by OSS-Fuzz can increase the complexity of the project’s source code. The result of this is that maintainers put effort into solving a timeout issue and the fix results in additional complexity of the project. The question is whether in a scenario like this if the overall result actually improves the state of the application.

In order to answer this question we must assess the issue relative to the threat model. Following the timeout anecdote then some timing issues can have severe security implications. For example, if the timeout issue can cause manipulation of control-flow then the timing issue may be of high security severity. As such, it is difficult to say in the general case whether or not some bugs should not be solved, as it should be analysed and determined on a project-by-project basis.

In the event that a bug is reported by OSS-Fuzz that is not relevant to security or reliability of the application then there may still be a point to fixing the bug. For example, if the issue is often run into by the fuzzer then the fuzzer may have difficulty exploring further code in the target, and thus fixing the bug will allow the fuzzer to explore further code. In this case some suggested examples of resolving the issue could be:

  • Perform a hot-patch that is only applied during fuzzer executions and does not overcomplicate the project’s code.
  • Patch the code of the fuzzer to avoid the timeout. For example, some fuzzers restrict the size of the input to avoid certain deep recursions or time-intensive loops.
  • Patch the code in the target despite complicating things.