In different fuzzers, this concept of executing the program under test means each run is now always the same. For instance, for in-memory fuzzers like libFuzzer an execution is a call to an harness function, for hypervisor-based fuzzers like kAFL instead an entire operating system is started from a snapshot for each run.

In our model, an Executor is the entity that defines not only how to execute the target, but all the volatile operations that are related to just a single run of the target.

So the Executor is for instance responsible to inform the program about the input that the fuzzer wants to use in the run, writing to a memory location for instance or passing it as a parameter to the harness function.

In our model, it can also hold a set of Observers connected with each execution.

In Rust, we bind this concept to the Executor trait. A structure implementing this trait must implement HasObservers too if wants to hold a set of Observers.

By default, we implement some commonly used Executors such as InProcessExecutor in which the target is a harness function providing in-process crash detection. Another Executor is the ForkserverExecutor that implements an AFL-like mechanism to spawn child processes to fuzz.

A common pattern when creating an Executor is wrapping an existing one, for instance TimeoutExecutor wraps an executor and install a timeout callback before calling the original run function of the wrapped executor.


Let's begin with the base case; InProcessExecutor. This executor executes the harness program (function) inside the fuzzer process.

When you want to execute the harness as fast as possible, you will most probably want to use this InprocessExecutor.

One thing to note here is, when your harness is likely to have heap corruption bugs, you want to use another allocator so that corrupted heap does not affect the fuzzer itself. (For example, we adopt MiMalloc in some of our fuzzers.). Alternatively you can compile your harness with address sanitizer to make sure you can catch these heap bugs.


Next, we'll take a look at the ForkserverExecutor. In this case, it is afl-cc (from AFLplusplus/AFLplusplus) that compiles the harness code, and therefore, we can't use EDGES_MAP anymore. Hopefully, we have a way to tell the forkserver which map to record the coverage.

As you can see from the forkserver example,

//Coverage map shared between observer and executor
let mut shmem = StdShMemProvider::new().unwrap().new_shmem(MAP_SIZE).unwrap();
//let the forkserver know the shmid
let mut shmem_buf = shmem.as_mut_slice();

Here we make a shared memory region; shmem, and write this to environmental variable __AFL_SHM_ID. Then the instrumented binary, or the forkserver, finds this shared memory region (from the aforementioned env var) to record its coverage. On your fuzzer side, you can pass this shmem map to your Observer to obtain coverage feedbacks combined with any Feedback.

Another feature of the ForkserverExecutor to mention is the shared memory testcases. In normal cases, the mutated input is passed between the forkserver and the instrumented binary via .cur_input file. You can improve your forkserver fuzzer's performance by passing the input with shared memory.

If the target is configured to use shared memory testcases, the ForkserverExecutor will notice this during the handshake and will automatically set up things accordingly. See AFL++'s documentation or the fuzzer example in forkserver_simple/src/program.c for reference.


Finally, we'll talk about the InProcessForkExecutor. InProcessForkExecutor has only one difference from InprocessExecutor; It forks before running the harness and that's it.

But why do we want to do so? well, under some circumstances, you may find your harness pretty unstable or your harness wreaks havoc on the global states. In this case, you want to fork it before executing the harness runs in the child process so that it doesn't break things.

However, we have to take care of the shared memory, it's the child process that runs the harness code and writes the coverage to the map.

We have to make the map shared between the parent process and the child process, so we'll use shared memory again. You should compile your harness with pointer_maps (for libafl_targets) features enabled, this way, we can have a pointer; EDGES_MAP_PTR that can point to any coverage map.

On your fuzzer side, you can allocate a shared memory region and make the EDGES_MAP_PTR point to your shared memory.

let mut shmem;
    shmem = StdShMemProvider::new().unwrap().new_shmem(MAX_EDGES_NUM).unwrap();
let shmem_buf = shmem.as_mut_slice();
    EDGES_PTR = shmem_buf.as_ptr();

Again, you can pass this shmem map to your Observer and Feedback to obtain coverage feedbacks.