LibAFL, the fuzzer library.

Advanced Fuzzing Library - Slot your own fuzzers together and extend their features using Rust.

LibAFL is written and maintained by

Why LibAFL?

LibAFL gives you many of the benefits of an off-the-shelf fuzzer, while being completely customizable. Some highlight features currently include:

fast: We do everything we can at compile time, keeping runtime overhead minimal. Users reach 120k execs/sec in frida-mode on a phone (using all cores).
scalable: Low Level Message Passing, LLMP for short, allows LibAFL to scale almost linearly over cores, and via TCP to multiple machines.
adaptable: You can replace each part of LibAFL. For example, BytesInput is just one potential form input: feel free to add an AST-based input for structured fuzzing, and more.
multi platform: LibAFL was confirmed to work on Windows, MacOS, Linux, and Android on x86_64 and aarch64. LibAFL can be built in no_std mode to inject LibAFL into obscure targets like embedded devices and hypervisors.
bring your own target: We support binary-only modes, like Frida-Mode, as well as multiple compilation passes for sourced-based instrumentation. Of course it's easy to add custom instrumentation backends.

Overview

LibAFL is a collection of reusable pieces of fuzzers, written in Rust. It is fast, multi-platform, no_std compatible, and scales over cores and machines.

It offers a main crate that provide building blocks for custom fuzzers, libafl, a library containing common code that can be used for targets instrumentation, libafl_targets, and a library providing facilities to wrap compilers, libafl_cc.

LibAFL offers integrations with popular instrumentation frameworks. At the moment, the supported backends are:

SanitizerCoverage, in libafl_targets
Frida, in libafl_frida
QEMU user-mode, in libafl_qemu

Getting started

Install the Dependecies

The Rust development language.
We highly recommend not to use e.g. your Linux distribition package as this is likely outdated. So rather install Rust directly, instructions can be found here.
LLVM tools
The LLVM tools are needed (newer than LLVM 11.0.0 but older than LLVM 15.0.0)
Cargo-make
We use cargo-make to build the fuzzers in fuzzers/ directory. You can install it with

cargo install cargo-make

Clone the LibAFL repository with

git clone https://github.com/AFLplusplus/LibAFL

Build the library using

cargo build --release

Build the API documentation with

cargo doc

Browse the LibAFL book (WIP!) with (requires mdbook)

cd docs && mdbook serve

We collect all example fuzzers in ./fuzzers. Be sure to read their documentation (and source), this is the natural way to get started!

The best-tested fuzzer is ./fuzzers/libfuzzer_libpng, a multicore libfuzzer-like fuzzer using LibAFL for a libpng harness.

Resources

Installation guide
Online API documentation
The LibAFL book (WIP) online or in the repo
Our RC3 talk explaining the core concepts
Our Fuzzcon Europe talk with a (a bit but not so much outdated) step-by-step discussion on how to build some example fuzzers
The Fuzzing101 solutions & series of blog posts by epi

Contributing

For bugs, feel free to open issues or contact us directly. Thank you for your support. <3

Even though we will gladly assist you in finishing up your PR, try to

keep all the crates compiling with stable rust (hide the eventual non-stable code under cfgs)
run cargo fmt on your code before pushing
check the output of cargo clippy --all or ./clippy.sh
run cargo build --no-default-features to check for no_std compatibility (and possibly add #[cfg(feature = "std")]) to hide parts of your code.

Some of the parts in this list may be hard, don't be afraid to open a PR if you cannot fix them by yourself, so we can help.