You can run a local instance of ClusterFuzz to test core functionality. Note that some features (like crash and fuzzer statistics) are disabled in local instances due to lack of Google Cloud emulators.
You can start a local server by running the following command:
# If you run the server for the first time or want to reset all data. python butler.py run_server --bootstrap # In all the other cases, do not use "--bootstrap" flag. python butler.py run_server
It may take a few seconds to start. Once you see an output line like
[INFO] Listening at: http://0.0.0.0:9000, you can see the web interface by navigating to http://localhost:9000.
Note: The local instance may use ports other than 9000, such as 9008, for things like uploading files. Your local instance may break if the needed ports are unavailable, or if you can only access some of the needed ports from your browser (for example, if you have port forwarding or firewall rules when accessing from another host).
You can run a ClusterFuzz bot in your local instance by running the following command:
python butler.py run_bot --name my-bot /path/to/my-bot # rename my-bot to anything
This creates a copy of the ClusterFuzz source under
/path/to/my-bot/clusterfuzz and uses it to run the bot. Most bot artifacts like logs, fuzzers, and corpora are created inside the
If you plan to fuzz native GUI applications, we recommend you run this command in a virtual framebuffer like Xvfb. Otherwise, you’ll see GUI dialogs while fuzzing.
You can see logs on your local instance by running the following command:
cd /path/to/my-bot/clusterfuzz/bot/logs tail -f bot.log
Note: Until you set up your fuzzing jobs, you’ll see a harmless error in the logs:
Failed to get any fuzzing tasks.
We simulate Google Cloud Storage using your local filesystem. By default, this is stored at
local/storage/local_gcs in your ClusterFuzz checkout.
You can override the default location by doing the following:
- Use the
--storage-pathflag with the
- Specify the same path using the
--server-storage-pathflag with the
In the location you specify, objects are stored in
<bucket>/objects/<object path> and metadata is stored in