THIS IS A EXPERIMENTAL DETECTION
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.
The following analytic identifies the use of a SQLite3 querying the MacOS preferences to identify the original URL the pkg was downloaded from. This particular behavior is common with MacOS adware-malicious software. Upon triage, review other processes in parallel for suspicious activity. Identify any recent package installations.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-02-22
- Author: Michael Haag, Splunk
- ID: e1997b2e-655f-4561-82fd-aeba8e1c1a86
Kill Chain Phase
- Actions on Objectives
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=sqlite3 Processes.process=*LSQuarantine* by Processes.dest Processes.user Processes.parent_process Processes.process_name Processes.process Processes.process_id Processes.parent_process_id | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `suspicious_sqlite3_lsquarantine_behavior_filter`
The SPL above uses the following Macros:
suspicious_sqlite3_lsquarantine_behavior_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the
Endpoint datamodel in the
Known False Positives
Associated Analytic Story
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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