This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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The following analytic detects the use of the PlistBuddy utility on macOS to create or modify property list (.plist) files. It leverages OSQuery to monitor process events, specifically looking for commands that interact with LaunchAgents and set properties like RunAtLoad. This activity is significant because PlistBuddy can be used to establish persistence mechanisms, as seen in malware like Silver Sparrow. If confirmed malicious, this could allow an attacker to maintain persistence, execute arbitrary commands, and potentially escalate privileges on the compromised system.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2024-05-22
  • Author: Michael Haag, Splunk
  • ID: 20ba6c32-c733-4a32-b64e-2688cf231399




ID Technique Tactic
T1543.001 Launch Agent Persistence, Privilege Escalation
T1543 Create or Modify System Process Persistence, Privilege Escalation
Kill Chain Phase
  • Installation
  • Exploitation
  • DE.CM
  • CIS 10
`osquery_process` "columns.cmdline"="*LaunchAgents*" OR "columns.cmdline"="*RunAtLoad*" OR "columns.cmdline"="*true*" 
|  `suspicious_plistbuddy_usage_via_osquery_filter`


The SPL above uses the following Macros:

:information_source: suspicious_plistbuddy_usage_via_osquery_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • columns.cmdline

How To Implement

OSQuery must be installed and configured to pick up process events (info at https://osquery.io) as well as using the Splunk OSQuery Add-on https://splunkbase.splunk.com/app/4402. Modify the macro and validate fields are correct.

Known False Positives

Some legitimate applications may use PlistBuddy to create or modify property lists and possibly generate false positives. Review the property list being modified or created to confirm.

Associated Analytic Story


Risk Score Impact Confidence Message
25.0 50 50 tbd

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.


Test Dataset

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

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