This folder contains an example fuzzer for libpng, using LLMP for fast multi-process fuzzing and crash detection.
In contrast to other fuzzer examples, this setup uses fuzz_loop_for
, to occasionally respawn the fuzzer executor.
While this costs performance, it can be useful for targets with memory leaks or other instabilities.
If your target is really instable, however, consider exchanging the InProcessExecutor
for a ForkserverExecutor
instead.
It also uses the introspection
feature, printing fuzzer stats during execution.
To show off crash detection, we added a ud2
instruction to the harness, edit harness.cc if you want a non-crashing example.
It has been tested on Linux.
To build this example, run
cargo build --release
This will build the library with the fuzzer (src/lib.rs) with the libfuzzer compatibility layer and the SanitizerCoverage runtime functions for coverage feedback. In addition, it will also build two C and C++ compiler wrappers (bin/libafl_c(libafl_c/xx).rs) that you must use to compile the target.
The compiler wrappers, libafl_cc
and libafl_cxx
, will end up in ./target/release/
(or ./target/debug
, in case you did not build with the --release
flag).
Then download libpng, and unpack the archive:
wget https://github.com/glennrp/libpng/archive/refs/tags/v1.6.37.tar.gz
tar -xvf v1.6.37.tar.gz
Now compile libpng, using the libafl_cc compiler wrapper:
cd libpng-1.6.37
./configure --enable-shared=no --with-pic=yes --enable-hardware-optimizations=yes
make CC="$(pwd)/../target/release/libafl_cc" CXX="$(pwd)/../target/release/libafl_cxx" -j `nproc`
You can find the static lib at libpng-1.6.37/.libs/libpng16.a
.
Now, we have to build the libfuzzer harness and link all together to create our fuzzer binary.
cd ..
./target/release/libafl_cxx ./harness.cc libpng-1.6.37/.libs/libpng16.a -I libpng-1.6.37/ -o fuzzer_libpng -lz -lm
Afterward, the fuzzer will be ready to run.
Note that, unless you use the launcher
, you will have to run the binary multiple times to actually start the fuzz process, see Run
in the following.
This allows you to run multiple different builds of the same fuzzer alongside, for example, with and without ASAN (-fsanitize=address
) or with different mutators.
The first time you run the binary, the broker will open a tcp port (currently on port 1337
), waiting for fuzzer clients to connect. This port is local and only used for the initial handshake. All further communication happens via shared map, to be independent of the kernel. Currently, you must run the clients from the libfuzzer_libpng directory for them to be able to access the PNG corpus.
./fuzzer_libpng
[libafl/src/bolts/llmp.rs:407] "We're the broker" = "We\'re the broker"
Doing broker things. Run this tool again to start fuzzing in a client.
And after running the above again in a separate terminal:
[libafl/src/bolts/llmp.rs:1464] "New connection" = "New connection"
[libafl/src/bolts/llmp.rs:1464] addr = 127.0.0.1:33500
[libafl/src/bolts/llmp.rs:1464] stream.peer_addr().unwrap() = 127.0.0.1:33500
[LOG Debug]: Loaded 4 initial testcases.
[New Testcase #2] clients: 3, corpus: 6, objectives: 0, executions: 5, exec/sec: 0
< fuzzing stats >
As this example uses in-process fuzzing, we added a Restarting Event Manager (setup_restarting_mgr
).
This means each client will start itself again to listen for crashes and timeouts.
By restarting the actual fuzzer, it can recover from these exit conditions.
In any real-world scenario, you should use taskset
to pin each client to an empty CPU core, the lib does not pick an empty core automatically (yet).