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diff --git a/docs/rpc_statsd.md b/docs/rpc_statsd.md index 898ad099..288d56cb 100644 --- a/docs/rpc_statsd.md +++ b/docs/rpc_statsd.md @@ -1,143 +1,155 @@ -# Remote monitoring with StatsD +# Remote monitoring and metrics visualization -StatsD allows you to receive and aggregate metrics from a wide range of applications and retransmit them to the backend of your choice. -This enables you to create nice and readable dashboards containing all the information you need on your fuzzer instances. -No need to write your own statistics parsing system, deploy and maintain it to all your instances, sync with your graph rendering system... +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. -The available metrics are : +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](resources/grafana-afl++.json). + +## 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_path - cycle_done - cycles_wo_finds +- edges_found - execs_done - execs_per_sec -- paths_total +- havoc_expansion +- max_depth - paths_favored - paths_found - paths_imported -- max_depth -- cur_path +- paths_total - pending_favs - pending_total -- variable_paths +- slowest_exec_ms +- total_crashes - unique_crashes - unique_hangs -- total_crashes -- slowest_exec_ms -- edges_found - var_byte_count -- havoc_expansion +- variable_paths -Compared to the default integrated UI, these metrics give you the opportunity to visualize trends and fuzzing state over time. -By doing so, you might be able to see when the fuzzing process has reached a state of no progress, visualize what are the "best strategies" -(according to your own criteria) for your targets, etc. And doing so without requiring to log into each instance manually. +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). -An example visualisation may look like the following: - +## Setting environment variables in AFL++ -*Notes: The exact same dashboard can be imported with [this JSON template](resources/grafana-afl++.json).* +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. -## How to use +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. -To enable the StatsD reporting on your fuzzer instances, you need to set the environment variable `AFL_STATSD=1`. + The available values are the following: + - `dogstatsd` + - `influxdb` + - `librato` + - `signalfx` + + For more information on environment variables, see [docs/env_variables.md](docs/env_variables.md). -Setting `AFL_STATSD_TAGS_FLAVOR` to the provider of your choice will assign tags / labels to each metric based on their format. -The possible values are `dogstatsd`, `librato`, `signalfx` or `influxdb`. -For more information on these env vars, check out `docs/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. -The simplest way of using this feature is to use any metric provider and change the host/port of your StatsD daemon, -with `AFL_STATSD_HOST` and `AFL_STATSD_PORT`, if required (defaults are `localhost` and port `8125`). -To get started, here are some instructions with free and open source tools. -The following setup is based on Prometheus, statsd_exporter and Grafana. -Grafana here is not mandatory, but gives you some nice graphs and features. +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`. -Depending on your setup and infrastructure, you may want to run these applications not on your fuzzer instances. -Only one instance of these 3 application is required for all your fuzzers. +## Installing and setting up StatsD, Prometheus, and Grafana -To simplify everything, we will use Docker and docker-compose. -Make sure you have them both installed. On most common Linux distributions, it's as simple as: +The easiest way to install and set up the infrastructure is with Docker and Docker Compose. -```sh -curl -fsSL https://get.docker.com -o get-docker.sh -sh get-docker.sh -``` +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). -Once that's done, we can create the infrastructure. -Create and move into the directory of your choice. This will store all the configurations files required. - -First, create a `docker-compose.yml` containing the following: -```yml -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 -``` +For all your fuzzing instances, only one instance of Prometheus and Grafana is required. The [statsd exporter](https://registry.hub.docker.com/r/prom/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." -Then `prometheus.yml` -```yml -global: - scrape_interval: 15s - evaluation_interval: 15s +You can create and move the infrastructure files into a directory of your choice. The directory will store all the required configuration files. -scrape_configs: - - job_name: 'fuzzing_metrics' - static_configs: - - targets: ['statsd_exporter:9102'] -``` +To install and set up Prometheus and Grafana: -And finally `statsd_mapping.yml` -```yml -mappings: -- match: "fuzzing.*" - name: "fuzzing" - labels: - type: "$1" -``` +1. Install Docker and Docker Compose: -Run `docker-compose up -d`. + ```sh + curl -fsSL https://get.docker.com -o get-docker.sh + sh get-docker.sh + ``` -Everything should now be setup, you are now able to run your fuzzers with +2. Create a `docker-compose.yml` containing the following: + ```yml + 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: + + ```yml + 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: + ```yml + 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 @@ +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] @@ ... -``` - -This setup may be modified before use in a production environment. Depending on your needs: adding passwords, creating volumes for storage, -tweaking the metrics gathering to get host metrics (CPU, RAM ...). +``` \ No newline at end of file |