Detection: MacOS LoginHook Persistence

Updated Date: 2026-04-15 ID: a04832e7-9d1d-49b1-a684-e31bcd775c77 Author: Raven Tait, Splunk Type: TTP Product: Splunk Enterprise Security

Description

Identifies attempts to configure a macOS LoginHook via the defaults utility. LoginHooks enable automatic execution of a script or program upon user login and have historically been abused for persistence. Creation or modification of this setting may indicate an attempt to establish startup execution outside standard LaunchAgent mechanisms.

Search

 1
 2| tstats `security_content_summariesonly`
 3  count min(_time) as firstTime
 4        max(_time) as lastTime
 5
 6from datamodel=Endpoint.Processes where
 7
 8Processes.process = "*defaults *"
 9Processes.process = "*write*"
10Processes.process = "*loginwindow*"
11Processes.process = "*loginhook*"
12
13by Processes.dest Processes.original_file_name Processes.parent_process_id
14   Processes.process Processes.process_exec Processes.process_guid
15   Processes.process_hash Processes.process_id
16   Processes.process_current_directory Processes.process_name
17   Processes.process_path Processes.user
18   Processes.user_id Processes.vendor_product
19
20
21| `drop_dm_object_name(Processes)`
22
23| `security_content_ctime(firstTime)`
24
25| `security_content_ctime(lastTime)`
26
27| `macos_loginhook_persistence_filter`

Data Source

Name	Platform	Sourcetype	Source
Osquery Results	Other	`'osquery:results'`	`'osquery'`

Macros Used

Name	Value
security_content_ctime	`convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)`
macos_loginhook_persistence_filter	`search *`

macos_loginhook_persistence_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic
T1037.002	Login Hook	Persistence

Exploitation

Installation

DE.CM

CIS 10

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`0 * * * *`
Earliest Time	`-70m@m`
Latest Time	`-10m@m`
Schedule Window	`auto`
Creates Notable	Yes
Rule Title	`%name%`
Rule Description	`%description%`
Notable Event Fields	user, dest
Creates Risk Event	True

This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

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 must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models.

Known False Positives

This method is possibly still used by legacy enterprise management scripts. Update filter macro to remove false positives.

Associated Analytic Story

MacOS Post-Exploitation

Risk Based Analytics (RBA)

Risk Message:

Loginhook created on $dest$ by $user$ via $process$

Risk Object	Risk Object Type	Risk Score	Threat Objects
dest	system	50	process
user	user	50	process

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	✅ Passing	N/A	N/A	N/A
Unit	✅ Passing	Dataset	`osquery`	`osquery:results`
Integration	✅ Passing	Dataset	`osquery`	`osquery:results`

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: GitHub | Version: 2