README.md

Summary

This is a simple mutator largely based on honggfuzz. This is all written in safe Rust and provides abstractions to allow for coverage guided fuzzing as well as taint guided fuzzing.

This provides no harnessing or injection into a target, this is purely a library which can be used to take in streams of bytes and produce mutated streams of bytes.

Usage

First, create a project with basic_mutator as a dependency in your Cargo.toml

[dependencies]
basic_mutator = { git = "https://github.com/gamozolabs/basic_mutator" }

To use this mutator, simply create a Mutator. This is done by calling Mutator::new with a maximum input size, a bool indicating whether or not the input is ASCII-printable only, and a random seed to seed the internal RNG.

A seed should be provided in all cases via the builder syntax, eg, Mutator::new().seed(0xdeadbeef). The mutator has no source of external entropy and thus without a seed will produce the same sequence of mutations.

Once this Mutator has been created, fill mutator.input, a public Vec<u8> member, with the input you want to mutate. It is highly encouraged that you use mutator.input.clear() and mutator.input.extend_from_slice() rather than things like clone(), to re-use the existing mutator.input allocation. This entire library performs no allocations other than expanding the mutator.input up to the maximum size specified. If this input buffer is reused, there will be no allocations occuring during normal operation.

Once the input has been filled in, call mutator.mutate() and provide a number of mutations, and a reference to a type which implements InputDatabase. If you don't have an input database, you can provide &EmptyDatabase, a implementation which will always return an empty database.

Once mutate is complete, the mutate.input now contains a mutated input!

Advanced Usage

This mutator library has support for both coverage guided fuzzing and taint guided fuzzing.

Coverage Guided Fuzzing

If you're using a corpus of inputs, or even better, an input database which grows dynamically based on coverage, you're in luck! When calling mutator.mutate, provide a reference to a type which implements InputDatabase.

This InputDatabase trait requires that you implement two methods. num_inputs() which returns a usize, indicating the number of inputs in the input database, as well as input() which takes a usize index and returns the corresponding input in the database.

When this trait is implemented and a non-empty database is provided, the fuzzer will use spicing strategies to take random pieces from existing inputs and insert or overwrite them into a random location in the input being corrupted.

Taint Guided Fuzzing

In an environment where you have some level of taint tracking, you can use the mutator.accessed vector. This vector is a Vec<usize> and contains indicies of locations where corruptions should occur. It is up to the user to make sure this information is meaningful. A great starting point is to track which bytes of the input are read from the input file or from memory, and only include those indicies in the accessed vector. This will prevent the mutator from mutating parts of the input which never are actually used by a program.

This logic is largely unbounded, and all we do internally is use this accessed vector as a restriction of mutation boundaries. This could be used by a symbolic execution engine which is providing offsets in the input which are used during the calculation of a branch which is being solved/fuzzed.

Example

Basic example

Here's a basic example with no corpus or accessed guidance.

fn simple_example() {
    // Create a mutator for 128-byte ASCII printable inputs
    let mut mutator = Mutator::new().seed(1337)
        .max_input_size(128).printable(true);

    for _ in 0..128 {
        // Update the input
        mutator.input.clear();
        mutator.input.extend_from_slice(b"APPLES ARE DELICIOUS");

        // Corrupt it with 4 mutation passes
        mutator.mutate(4, &EmptyDatabase);
        assert!(mutator.input.len() <= 128);

        // Just print the string
        println!("simple: {}", String::from_utf8_lossy(&mutator.input));
    }
}

Corpus example

Here's a basic example with a simple fixed corpus.

fn corpus_example() {
    // Create a fake database which will be used to select inputs from a fake
    // feedback dattabase
    struct TestDatabase;
    impl InputDatabase for TestDatabase {
        fn num_inputs(&self) -> usize { 2 }
        fn input(&self, idx: usize) -> Option<&[u8]> {
            match idx {
                0 => Some(b"thisisatest"),
                1 => Some(b"wafflesaregood"),
                _ => unreachable!(),
            }
        }
    }

    // Create a mutator for 128-byte ASCII printable inputs
    let mut mutator = Mutator::new().seed(1337)
        .max_input_size(128).printable(true);

    for _ in 0..128 {
        // Update the input
        mutator.input.clear();
        mutator.input.extend_from_slice(b"APPLES ARE DELICIOUS");

        // Corrupt it with 4 mutation passes
        mutator.mutate(4, &TestDatabase);
        assert!(mutator.input.len() <= 128);

        // Just print the string
        println!("feedback: {}", String::from_utf8_lossy(&mutator.input));
    }
}