Searching Content


A powerful DFIR technique is searching bulk data for patterns. Some examples include:

  • Searching for CC data in process memory
  • Searching for URLs in process memory
  • Searching binaries for malware signatures
  • Searching registry for patterns

Bulk searching helps to identify evidence without needing to parse file formats

YARA - The swiss army knife

YARA is a powerful keyword scanner that allows to search unstructured binary data based on user provided rules. YARA is optimized to scan for many rules simultaneously, making is an excellent choice for detecting suspicious binaries using common patterns.

Velociraptor supports YARA scanning of bulk data (via accessors) and memory using the yara() and proc_yara() plugins.

An example of a YARA rule is shown below.

rule X {
       $a = “hello” nocase
       $b = “Goodbye” wide
       $c = /[a-z]{5,10}[0-9]/i

       $a and ($b or $c)

The rule consists of a strings section and a condition section. Strings represent a set of keywords which might include ASCII or UTF16 encoded strings, as well as regular expressions. You can refer to the Yara rules reference page to learn about how to construct rules.

The yara() VQL plugin can accept an optional accessor parameter. If the accessor is specified, the plugin will read chunks of data from the accessor and apply the YARA rules on the string in memory. This allows you to apply YARA rules on any data that is available via an accessor including raw strings (using the data accessor), registry values (using the registry accessor) or NTFS parsed data (using the ntfs accessor) for example.

While this is convenient, it means that rules that examine the entire file will not work as expected. For example, the YARA pe module looks at the PE header, but when the file is read in chunks, only the first chunk contains the PE header. Similarly YARA rules that contain an expression checking a file offset will not work because the rules are applied to buffers in memory.

When an accessor is not specified, the yara() plugin assumes the filename refers to a filesystem path, and simply allows the YARA library to scan the file as is. The YARA library uses mmap() to map the entire file into memory and can therefore optimize the scan across the entire file.

It is therefore much faster to not specify an accessor to the yara() plugin if you just need to scan files on disk.

Example: drive by download

You suspect a user was compromised by a drive by download (i.e. they clicked and downloaded malware delivered by mail, ads etc).

You think the user used the Edge browser but for this example, assume you have no idea of the internal structure of the browser cache/history etc. Write an artifact to extract potential URLs from the Edge browser directory.

LET YaraRule = '''
rule URL {
  strings: $a = /https?:\\/\\/[a-z0-9\\/+&#:\\?.-]+/i
  condition: any of them

SELECT * FROM foreach(
   SELECT FullPath FROM glob(globs='''C:\Users\*\AppData\Local\Microsoft\Edge\**''')
}, query={
   SELECT str(str=Strings.Data) AS Hit,
          String.Offset AS Offset,
   FROM yara(files=FullPath, rules=YaraRule)

URL scanning
URL scanning

YARA best practice

You can get yara rules from many sources (threat intel, blog posts etc) or you can write your own. Rules may be very specific, in which case a hit may represent a valuable signal. If the YARA rule is too loose, the likelihood of a false positive increases, and further postprocessing will be required to verify the hits.

Try to collect additional context around the hits to eliminate false positives. You can use other plugins to help verify other aspects of each hit before reporting it, thereby eliminating false positives.

Yara scanning is relatively expensive since we need to read data from disk! consider more targeted glob expressions to limit the number of disk reads Velociraptor will need to do to evaluate the query. If you find you do need to scan a lot of data, consider specifying client side throttling when launching the collection or hunt (using the Ops/Sec mechanism) - usually YARA scanning is not time critical.

Uploading files

One of the unique capabilities of Velociraptor is uploading file content from the endpoint. While the actual mechanism of uploading the file to the server is abstracted away, triggering a file upload from VQL is a simple matter of calling the upload() function. This makes it trivial to upload files based on any criteria of the query.

The upload() function simply requires an accessor and a filename to read the file out, and the file is uploaded to the server automatically. Optionally the function may also take a name parameter which renames the file as sent to the server.

Example: Collect all executables in users’ home directory

This is a common use of compbining a glob() plugin with an upload() function:

SELECT upload(path=FullPath) AS Upload
FROM glob(globs='''C:\Users\*\Downloads\*''')