Velociraptor draws its inspiration from two major open source projects:

If you used either of these projects you might wonder how Velociraptor compares to the work that was done before it.

Let’s look at the major design differences and priorities of Velociraptor, GRR, and OSQuery.

Velociraptor vs Google Rapid Response

Google’s Rapid Response (GRR) launched in 2011, and was one of the first tools to allow hunting for forensic artifacts at scale. GRR allowed investigators to quickly query network hosts to check files or registry settings. Rather than passively analyse logs that after they were collected into a central location, GRR allowed security professionals to proactively search for evidence of compromise across many hosts.

One of the challenges of remotely accessing machines at scale is that many endpoints are not online when investigators need to access them. GRR allows for “Flows” to be scheduled in advance so that evidence is automatically collected when the endpoint comes back online.

GRR and Velociraptor both refer to the process of simultaneously collecting the same file or registry key from many machines as a “hunt”.

Velociraptor also provides asynchronous collection of “artifacts” from multiple hosts in a similar way. However, Velociraptor artifacts do more than collect files or registry keys. Velociraptor can perform sophisticated analysis on the endpoint to surface novel adversary techniques and detect malicious activity quickly and with precision.

Another aspect where Velociraptor differs from GRR is in it’s ease of use and deployment. While GRR requires a complex deployment with many moving parts, Velociraptor is a single statically compiled executable written in Go. Velociraptor is also much faster than GRR and has a much lower memory/CPU footprint on the endpoint. A single Velociraptor server can handle over 10,000 endpoint network easily, and can be installed in a few minutes on modest hardware.

GRR primarily collects files and registry keys from the endpoint, with minimal parsing capability on the endpoint, preferring instead of parse files on the server. Velociraptor’s philosophy is to push as much of the parsing and analysis to the endpoint as possible. Velociraptor contains many powerful forensic analysis modules on the endpoint, and uses a powerful query language allowing new parsers to be written. This allows endpoints to send only the most relevant results and reduces unnecessary parsing on the server.

Velociraptor vs OSQuery

OSQuery was really the first popular example of an open source tool that provided a query langauge to allow querying the endpoints. This capability allows users to target specific queries in a flexible way to address new threats or find new IOCs, making it a popular choice among defenders.

The main limitation with OSQuery is that it uses SQL, a language that is designed for databases and not to query dynamic endpoint state. There are limitations in SQL expressions that impact its ability to build concise and flexible queries. While simple SQL is easy for beginners to learn, more sophisticated queries use SQL contracts that are a pretty complex, such as JOIN operators.

Velociraptor’s VQL also allows users to flexibly write new queries to gather new evidence onthe endpoint. However, VQL is deliberately kept very simple, yet powerfully expressive.

Additionally, OSQuery suffers from performance issues. Finding files using queries against the “file” table are notoriously expensive. Velociraptor is typically much faster than OSQuery and uses much less memory.

Finally, OSQuery by itself is not sufficient to monitor a large network since OSQuery does not include any kind of client/server orchestration or GUI. A complete solution requires users to install another tool (such as FleetDM) to use OSQuery, increasing complexity and management overhead.