The Velociraptor API

Velociraptor can be fully controlled by external programs using the Velociraptor API. In this page you will learn how to connect to the server using the API and control it using a Python script to schedule collections on hosts and retrieve the results of those collections.

Why an API?

Modern detection and DFIR work consist of many different products and tools all working together. In reality Velociraptor is just a part of a larger ecosystem consisting of network detections, SIEM or other tools. It is therefore important to ensure that Velociraptor integrates well with other tools.

Generally there are two main requirements for Velociraptor integration:

  1. Velociraptor can itself control other systems. This can be achieved using VQL and the execve() or http_client() plugins (See Extending VQL for an example)

  2. Velociraptor can be controlled by external tools. This allows external tools to enrich and automate Velociraptor using an external API

API Server

The Velociraptor API is exposed over a streaming gRPC server. The gRPC protocol allows encrypted and streaming communications between API clients (i.e. calling external programs) and Velociraptor itself.

The communication is encrypted and protected using mutual certificate authentication verified by the built in Velociraptor CA. This means that callers are identified by their client certificates which must be issued by the Velociraptor CA.

The Velociraptor API communications
The Velociraptor API communications

The Velociraptor API itself is very simple, yet extremely powerful! It simply exposes a method called Query. Callers are able to run arbitrary VQL queries and stream the results over the single API call.

Since VQL allows for many tasks, from server administration, post processing of collection results and scheduling of new collections, the API is extremely flexible and powerful.

Protecting the API

The API is extremely powerful so it must be protected! The whole point of an API is to allow a client program (written in any language) to interact with Velociraptor. Since we use certificates to authenticate callers, the client program must present a certificate as part of its connection (This mechanism is built into gRPC).

The server can mint a certificate for the client program to use. This allows it to authenticate and establish a TLS connection with the API server.

By default the API server only listens on - this allows scripts on the local machine to call into the API, but if you want to use an external caller you can change the server’s configuration file by setting the bind_address field under the API section to allowing the API to bind on all interfaces. Following is the relevant excerpt from the configuration file.

  bind_port: 8001
  bind_scheme: tcp
  pinned_gw_name: GRPC_GW

After this change the server will report on the logs that the API server is not listening on all interfaces.

[INFO] 2021-11-07T01:57:26+10:00 Starting gRPC API server on

Creating a client API certificate

You can create a new client api certificate which allows the client program to identify itself with the server. The server will verify that the certificate is signed by the Velociraptor CA prior to accepting connections. The produced YAML file contains private keys, public certificates and the CA’s certificate.

velociraptor --config server.config.yaml config api_client --name Mike --role administrator api.config.yaml

This command can be broken into:

  1. --config server.config.yaml load the server config which contains the CA private keys needed to sign a new minted certificate.
  2. config api_client generate an api_client certificate
  3. --name Mike: Certificates represent identities. The name of the certificate will be used to identify the caller and place ACLs on it.
  4. --role administrator: This option will also assign a role to the new certificate name. The role is used to test permissions of what the caller may do.

You can also change the permission of an existing certificate (or user) by simply granting a different role.

velociraptor --config /etc/velociraptor/server.config.yaml acl grant Mike --role administrator,api

For an API key to be able to connect the key must have the api role as well. This is a minimum role to allow external connections. The administrator role is very powerful and we recommend external programs not be given this role. Instead think what permission the external program requires on the server and select the appropriate role for it.

If a key is compromised you can remove its role using the same command. This prevents the key from being used on the server at all.

velociraptor --config /etc/velociraptor/server.config.yaml acl grant Mike --role ""

At any time you can inspect the roles given to the key using the acl show command.

$ velociraptor --config /etc/velociraptor/server.config.yaml acl show Mike

Python bindings

The Velociraptor API uses gRPC which is an open source, high performance RPC protocol compatible with many languages. The Velociraptor team officially supports python through the pyvelociraptor project, but since gRPC is very portable, many other languages can be used including C++, Java etc. This document will discuss the python bindings specifically as an example.

Install the python bindings

For python we always recommend a virtual environment and Python 3. Once you have Python3 installed, simply install the pyvelociraptor package using pip.

pip install pyvelociraptor

In order to connect to the gRPC port, check the connection string setting in the api configuration file. If you want to connect to the api from a difference host you will need to update the connection string to include the correct IP address or hostname.

name: Mike

To test the API connection you can use the pyvelociraptor commandline tool (which was installed via pip above). Let’s run a simple query:

$ pyvelociraptor --config api_client.yaml  "SELECT * FROM info()"
Sun Nov  7 02:16:44 2021: vql: Starting query execution.
Sun Nov  7 02:16:44 2021: vql: Time 0: Test: Sending response part 0 415 B (1 rows).
[{'Hostname': 'devbox', 'Uptime': 1290254, 'BootTime': 1634925150, 'Procs': 410, 'OS': 'linux', 'Platform': 'ubuntu', 'PlatformFamily': 'debian', 'PlatformVersion': '21.04', 'KernelVersion': '5.11.0-37-generic', 'VirtualizationSystem': '', 'VirtualizationRole': '', 'HostID': '4e7cbddb-e949-4fb9-876a-f4e3e85c9eb4', 'Exe': '/usr/local/bin/velociraptor.bin', 'Fqdn': 'devbox', 'Architecture': 'amd64'}]
Sun Nov  7 02:16:44 2021: vql: Query Stats: {"RowsScanned":1,"PluginsCalled":1,"FunctionsCalled":0,"ProtocolSearch":0,"ScopeCopy":4}

The above query uses the api config file to load the correct key material then sends the query over the network to the API port, forwarding the resulting query logs and result set to print them on the console.

The example is just a sample python program which you can modify as required.

Schedule an artifact collection

Since VQL is already a powerful and flexible language, we do not need any other API handlers to be exposed. In the following section we discuss how VQL can be used to schedule a collection on a client, and relay back the results as a typical example of using the API to control Velociraptor artifact collections.

The trick here is that scheduling a collection on a client in Velociraptor is asynchronous. This makes sense because the client may not even be online at the moment. Scheduling a collection simply returns a flow_id by which we can reference the flow to check on its status later.

For this example, say we want to schedule a Generic.Client.Info collection. We will start off by calling the collect_client() VQL function which returns the flow id of the new collection.

LET collection <= collect_client(
    artifacts='Generic.Client.Info', env=dict())

We can not access the flow’s results immediately though because it might take a few seconds for the client to actually respond. Therefore we need to wait for the flow to complete.

Velociraptor has a server eventing framework that allows VQL to watch for changes in server state using the watch_monitoring() plugin. This plugin is an event plugin (i.e. it blocks and simply returns rows as events occur).

In this example we simply wish to wait until the flow we launched above is complete. When flows complete, an event is sent on the System.Flow.Completion event queue. You can watch this to be notified of flows completing

LET _ <= SELECT * FROM watch_monitoring(artifact='System.Flow.Completion')
WHERE FlowId = collection.flow_id

The above query simply begins watching the queue and each flow that is completed on the system will send an event to the query. We are looking for a specific flow though which was stored in the collection variable above. Therefore we filter the events by the WHERE condition. Finally we wish to quit the query once a single row is found so we specify a LIMIT of 1 row.

Note the LET _ <= statement. This tells VQL to materialize the query and store the result in a dummy variable. This statement causes VQL to pause and wait for the query to complete before evaluating the next query. See Materialized LET expressions for more about this.

After this query exits we know the collection is complete. This may take a few seconds if the machine is online or it could take days or week (or even eternity) to wait for the machine to come back online.

The final step is to read the results of the collection. We can do so in VQL using the source() plugin. This plugin reads collected json result sets from the server and returns them row by row.

SELECT * FROM source(

Note that if an artifact contains multiple sources we need to specify the exact source we want using the full notation artifact name/artifact source

Putting it all together

We can string all these queries together (note VQL does not require ; at the end of a statement like SQL).

$ pyvelociraptor --config api_client.yaml  "LET collection <= collect_client(client_id='C.cdc70ff1039db48a', artifacts='Generic.Client.Info', env=dict())   LET _ <= SELECT * FROM watch_monitoring(artifact='System.Flow.Completion') WHERE FlowId = collection.flow_id LIMIT 1  SELECT * FROM source(client_id=collection.request.client_id, flow_id=collection.flow_id,artifact='Generic.Client.Info/BasicInformation')"

Sun Nov  7 11:32:29 2021: vql: Starting query execution.
Sun Nov  7 11:32:29 2021: vql: Time 0: Test: Sending response part 0 334 B (1 rows).
[{'Name': 'velociraptor', 'BuildTime': '2021-11-07T01:49:52+10:00', 'Labels': None, 'Hostname': 'DESKTOP-BTI2T9T', 'OS': 'windows', 'Architecture': 'amd64', 'Platform': 'Microsoft Windows 10 Enterprise Evaluation', 'PlatformVersion': '10.0.19041 Build 19041', 'KernelVersion': '10.0.19041 Build 19041', 'Fqdn': 'DESKTOP-BTI2T9T', 'ADDomain': 'WORKGROUP'}]
Sun Nov  7 11:32:29 2021: vql: Query Stats: {"RowsScanned":2,"PluginsCalled":2,"FunctionsCalled":2,"ProtocolSearch":0,"ScopeCopy":9}

The above query demonstrates a common use case for the API - notifying an external script of an event occurring on the server. For example external python scripts can be notified when a specific artifact is collected, inspect its results, and upload them to further processing to an external system or escalate alerts for example.

The API connection will simply block until an event occurs allowing you to create a fully automated pipeline based off Velociraptor collections, hunts etc.