MacOS Kextload Usage

 Original Source: [splunk source]
Name:MacOS Kextload Usage
id:9d680775-84a6-4625-a8ea-8182b9427ce4
version:3
date:None
author:Raven Tait, Splunk
status:production
type:TTP
Description:Detects execution of the kextload command on macOS systems. The kextload utility is used to manually load kernel extensions (KEXTs) into the macOS kernel, which can introduce privileged code at the kernel level. While legitimate for driver installation and system administration, misuse may indicate attempts to install unauthorized, malicious, or persistence-enabling kernel extensions.
Data_source:
  • -Osquery Results
search:| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime

from datamodel=Endpoint.Processes where

Processes.process_name = "kextload"

AND NOT

Processes.process IN (
"*-help*",
"* -h *"
)

by Processes.dest Processes.original_file_name Processes.parent_process_id
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id
Processes.process_current_directory Processes.process_name
Processes.process_path Processes.user
Processes.user_id Processes.vendor_product

| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `macos_kextload_usage_filter`


how_to_implement:This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models.
known_false_positives:Administrators installing new drivers could use this application.
References:
  -https://osquery.readthedocs.io/en/stable/deployment/process-auditing/
  -https://www.unix.com/man_page/osx/8/kextload/
drilldown_searches:
 name:'View the detection results for - "$user$" and "$dest$"'
 search:'%original_detection_search% | search user = "$user$" dest = "$dest$"'
 earliest_offset:'$info_min_time$'
 latest_offset:'$info_max_time$'
 name:'View risk events for the last 7 days for - "$user$" and "$dest$"'
 search:'| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
 earliest_offset:'7d'
 latest_offset:'0'
analytic_story:['MacOS Privilege Escalation', 'MacOS Persistence Techniques']

asset_type:Endpoint

mitre_attack_id:['T1543']

product:['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud']

category:endpoint

security_domain:endpoint

tags:

tests:
 name:'True Positive Test'
 attack_data:
  data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1543/osquery_ketxload/osquery.log
  source: osquery
  sourcetype: osquery:results
 test_type:'unit'
manual_test:None