Fuzzes network servers/services by intercepting valid network communication data, then replay it with some fuzzing.

FFW can fuzz open source applications and supports feedback driven fuzzing by instrumenting honggfuzz, for both open- and closed source apps.

In comparison with the alternatives, FFW is the most advanced, feature-complete and tested network fuzzer.

Features:

• Fuzzes all kind of network protocol (HTTP, MQTT, SMTP, you name it)
• No modification of the fuzzing target needed (at all)
• Has feedback-driven fuzzing (with compiler support, or hardware based)
• Can fuzz network clients too (wip)
• Fast fuzzing setup (no source code changes or protocol reversing needed!)
• Reasonable fuzzing performance

Easiest way to start is to use the docker image:

• https://github.com/dobin/ffw-docker

By doing so:

docker run -ti --privileged -lxc-conf="aa_profile=unconfined" dobin/ffw:0.1

Examples are located in /ffw-examples.

git clone https://github.com/dobin/ffw.git
cd ffw/

Note: Manually installed dependencies are expected to live in the ffw/ directory (e.g. honggfuzz, radamsa).

If its a fresh Ubuntu, install relevant packages for FFW:

apt-get install python python-pip gdb

For honggfuzz:

apt-get install clang binutils-dev libunwind8-dev

And python dependencies:

pip install -r requirements.txt

$ git clone https://github.com/aoh/radamsa.git
$ cd radamsa
$ make

Default Radamsa directory specified in ffw is ffw/radamsa.

Steps involved in setting up a fuzzing project:

• Create directory structure for that fuzzing project by copying template folder
• Copy target binary to bin/
• Specify all necessary information in the config file fuzzing.py
• Start interceptor-mode to record traffic
• Start test-mode to verify recorded traffic (optional)
• Start fuzz-mode to fuzz
• Start verify-mode to verify crashed from the fuzz mode (optional)
• Start upload-mode to upload verified crashes to the web (optional)

For a step-by-step guide:

• Setup the sample project tutorial
• Setup the feedback-driven fuzzing project tutorial
• Some fuzzing help and infos

Test all:

python -m unittest discover

Test a single module:

python -m unittest test.test_interceptor

Available via https://github.com/denandz/fuzzotron. "Fuzzotron is a simple network fuzzer supporting TCP, UDP and multithreading."

Support network fuzzing, also uses Radamsa. Can use coverage data, but it is experimental.

Con's:

• Does not restart target server
• Unreliable crash detection
• Experimental code coverage

Available via https://github.com/Cisco-Talos/mutiny-fuzzer. "The Mutiny Fuzzing Framework is a network fuzzer that operates by replaying PCAPs through a mutational fuzzer."

Con's:

• No code coverage
• Only one commit (no development?)
• Rudimentary crash detection