oss-fuzz VS American Fuzzy Lop

> because the ifunc code was breaking with all sorts of build options and obviously caused many problems with various sanitizers

for example, https://github.com/google/oss-fuzz/pull/10667

Have you tried Googles grpc fuzzer?

https://github.com/google/oss-fuzz/blob/master/projects/grpc...

https://www.fda.gov/medical-devices/digital-health-center-ex...

oss-fuzz: https://github.com/google/oss-fuzz :

> We support the libFuzzer, AFL++, and Honggfuzz fuzzing engines in combination with Sanitizers, as well as ClusterFuzz, a distributed fuzzer execution environment and reporting tool.

> Currently, OSS-Fuzz supports C/C++, Rust, Go, Python, Java/JVM, and JavaScript code. Other languages supported by LLVM may work too. OSS-Fuzz supports fuzzing x86_64 and i386 builds.

I love fuzzing as a technique and use it quite regularly, but running AFL++ on even a single program occupies all threads of a high end AMD server for weeks. I'm running it locally so only paying for the electricity. If it was a cloud instance it would cost a small fortune. I think this is a reason it is not used more widely.

I will note that Google have a programme for doing fuzz testing on open source projects using computer from their cloud: https://google.github.io/oss-fuzz/

Yes, mosh has fuzz tests in oss-fuzz [1].

[1] https://github.com/google/oss-fuzz/tree/master/projects/mosh

We will explore how Jazzer is used to automatically generate malicious inputs for Java programs, and how it compares to Symflower, which can automatically generate unit tests to uncover bugs and errors in your code. With the help of Jazzer, many bugs - some of them even in the OpenJDK - were found already. Also, as of March 2021, Jazzer is officially part of OSS-Fuzz, Google's cloud fuzzing engine. It should be noted that Jazzer is a pure "bug detection" utility that finds reproducers for errors in user code. Symflower can do the same, but provides additional functionalities to boost developer productivity, like generating high coverage unit tests and providing test templates for the software developer or tester.

There's some efforts to guide test generation for property based testing to make the instruction pointer explore as large a space as possible.

This effort is more mature in the fuzzing community. See eg American Fuzzy Lop https://github.com/google/AFL

What you're describing, just generating random input to test a program, is sometimes called "blind fuzzing" but the state-of-the-art is far beyond that. Maybe try reading through the documentation of e.g. https://github.com/google/AFL to see what a fuzzer does and why just producing random input isn't even scratching the surface.

for general riscv I used to use this https://github.com/google/AFL I dont know if it supports x64 tho.

Ex ( AFL, WinAFL, HonggFuzz, LibFuzzer, Jazzer )

I wrote this little fuzz test target in order to fuzz it with afl (under ASan and UBSan):

afl, which is trivial to apply to this program:

I made my own version of a TCL interpreter (well, a very TCL like langauge) derived from "picol" available at https://github.com/howerj/pickle. There are many different re-implementations and derivatives of this interpreter but they all seem very "crashy", this one has been significantly hardened by using a fuzzer on it which ran for months called American Fuzzy Lop https://lcamtuf.coredump.cx/afl/ . It is also more suitable for embedded use whilst still not having arbitrary restrictions like many other implementations.

On Linux afl is a very powerful bug-finding tool, and it's a great companion when doing code review. Composes well with ASan and UBSan.

b-, https://lcamtuf.coredump.cx/afl/