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Description

This analytic focuses on identifying the creation or deletion of scheduled tasks using the schtasks.exe utility with the corresponding command-line flags (-create or -delete). This technique has been notably associated with threat actors like Dragonfly and the SUNBURST attack against SolarWinds. The purpose of this analytic is to detect suspicious activity related to scheduled tasks that could indicate malicious intent or unauthorized system manipulation. By monitoring for these specific command-line flags, we can enhance our ability to identify potential threats and prevent attacks similar to the use of scheduled tasks in the BadRabbit Ransomware incident.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Endpoint
  • Last Updated: 2023-12-27
  • Author: Bhavin Patel, Splunk
  • ID: d5af132c-7c17-439c-9d31-13d55340f36c

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1053.005 Scheduled Task Execution, Persistence, Privilege Escalation
T1053 Scheduled Task/Job Execution, Persistence, Privilege Escalation
Kill Chain Phase
  • Installation
  • Exploitation
NIST
  • DE.CM
CIS20
  • CIS 10
CVE 


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| tstats `security_content_summariesonly` count values(Processes.process) as process values(Processes.parent_process) as parent_process min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name=schtasks.exe (Processes.process=*delete* OR Processes.process=*create*) by Processes.user Processes.process_name Processes.parent_process_name Processes.dest 
| `drop_dm_object_name(Processes)` 
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `scheduled_task_deleted_or_created_via_cmd_filter`

Macros

The SPL above uses the following Macros:

:information_source: scheduled_task_deleted_or_created_via_cmd_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
  • Processes.process
  • Processes.parent_process
  • Processes.process_name
  • Processes.user
  • Processes.parent_process_name
  • Processes.dest

How To Implement

The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.

Known False Positives

While it is possible for legitimate scripts or administrators to trigger this behavior, filtering can be applied based on the parent process and application to reduce false positives. Analysts should reference the provided references to understand the context and threat landscape associated with this activity.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
56.0 70 80 A schedule task process $process_name$ with create or delete commandline $process$ in host $dest$

: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.

Reference

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

source | version: 6