Remote monitoring and metrics visualization

AFL++ can send out metrics as StatsD messages. For remote monitoring and visualization of the metrics, you can set up a tool chain. For example, with Prometheus and Grafana. All tools are free and open source.

This enables you to create nice and readable dashboards containing all the information you need on your fuzzer instances. There is no need to write your own statistics parsing system, deploy and maintain it to all your instances, and sync with your graph rendering system.

Compared to the default integrated UI of AFL++, this can help you to visualize trends and the fuzzing state over time. You might be able to see when the fuzzing process has reached a state of no progress and visualize what are the “best strategies” for your targets (according to your own criteria). You can do so without logging into each instance individually.

This is an example visualization with Grafana. The dashboard can be imported with this JSON template.

AFL++ metrics and StatsD

StatsD allows you to receive and aggregate metrics from a wide range of applications and retransmit them to a backend of your choice.

From AFL++, StatsD can receive the following metrics:

• cur_item
• cycle_done
• cycles_wo_finds
• edges_found
• execs_done
• execs_per_sec
• havoc_expansion
• max_depth
• corpus_favored
• corpus_found
• corpus_imported
• corpus_count
• pending_favs
• pending_total
• slowest_exec_ms
• total_crashes
• saved_crashes
• saved_hangs
• var_byte_count
• corpus_variable

Depending on your StatsD server, you will be able to monitor, trigger alerts, or perform actions based on these metrics (for example: alert on slow exec/s for a new build, threshold of crashes, time since last crash > X, and so on).

Setting environment variables in AFL++

1. To enable the StatsD metrics collection on your fuzzer instances, set the environment variable AFL_STATSD=1. By default, AFL++ will send the metrics over UDP to 127.0.0.1:8125.

2. To enable tags for each metric based on their format (banner and afl_version), set the environment variable AFL_STATSD_TAGS_FLAVOR. By default, no tags will be added to the metrics.

   The available values are the following:

   • dogstatsd
   • influxdb
   • librato
   • signalfx

   For more information on environment variables, see env_variables.md.

   Note: When using multiple fuzzer instances with StatsD it is strongly recommended to set up AFL_STATSD_TAGS_FLAVOR to match your StatsD server. This will allow you to see individual fuzzer performance, detect bad ones, and see the progress of each strategy.

3. Optional: To set the host and port of your StatsD daemon, set AFL_STATSD_HOST and AFL_STATSD_PORT. The default values are localhost and 8125.

Installing and setting up StatsD, Prometheus, and Grafana

The easiest way to install and set up the infrastructure is with Docker and Docker Compose.

Depending on your fuzzing setup and infrastructure, you may not want to run these applications on your fuzzer instances. This setup may be modified before use in a production environment; for example, adding passwords, creating volumes for storage, tweaking the metrics gathering to get host metrics (CPU, RAM, and so on).

For all your fuzzing instances, only one instance of Prometheus and Grafana is required. The statsd exporter converts the StatsD metrics to Prometheus. If you are using a provider that supports StatsD directly, you can skip this part of the setup."

You can create and move the infrastructure files into a directory of your choice. The directory will store all the required configuration files.

To install and set up Prometheus and Grafana:

1. Install Docker and Docker Compose:

   curl -fsSL https://get.docker.com -o get-docker.sh
   sh get-docker.sh
   

2. Create a docker-compose.yml containing the following:

   version: '3'
   
   networks:
     statsd-net:
       driver: bridge
   
   services:
     prometheus:
       image: prom/prometheus
       container_name: prometheus
       volumes:
         - ./prometheus.yml:/prometheus.yml
       command:
         - '--config.file=/prometheus.yml'
       restart: unless-stopped
       ports:
         - "9090:9090"
       networks:
         - statsd-net
   
     statsd_exporter:
       image: prom/statsd-exporter
       container_name: statsd_exporter
       volumes:
         - ./statsd_mapping.yml:/statsd_mapping.yml
       command:
         - "--statsd.mapping-config=/statsd_mapping.yml"
       ports:
         - "9102:9102/tcp"
         - "8125:9125/udp"
       networks:
         - statsd-net
   
     grafana:
       image: grafana/grafana
       container_name: grafana
       restart: unless-stopped
       ports:
           - "3000:3000"
       networks:
         - statsd-net
   

3. Create a prometheus.yml containing the following:

   global:
     scrape_interval:      15s
     evaluation_interval:  15s
   
   scrape_configs:
     - job_name: 'fuzzing_metrics'
       static_configs:
         - targets: ['statsd_exporter:9102']
   

4. Create a statsd_mapping.yml containing the following:

   mappings:
   - match: "fuzzing.*"
     name: "fuzzing"
     labels:
         type: "$1"
   

5. Run docker-compose up -d.

Running AFL++ with StatsD

To run your fuzzing instances:

AFL_STATSD_TAGS_FLAVOR=dogstatsd AFL_STATSD=1 afl-fuzz -M test-fuzzer-1 -i i -o o [./bin/my-application] @@
AFL_STATSD_TAGS_FLAVOR=dogstatsd AFL_STATSD=1 afl-fuzz -S test-fuzzer-2 -i i -o o [./bin/my-application] @@
...