Logon Script Event Trigger Execution
Description
This search is to detect a suspicious modification of registry entry to persist and gain privilege escalation upon booting up of compromised host. This technique was seen in several APT and malware where it modify UserInitMprLogonScript registry entry to its malicious payload to be executed upon boot up of the machine.
- Type: TTP
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Endpoint
- Last Updated: 2021-09-27
- Author: Teoderick Contreras, Splunk
- ID: 4c38c264-1f74-11ec-b5fa-acde48001122
Annotations
ATT&CK
Kill Chain Phase
- Exploitation
NIST
CIS20
CVE
Search
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| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Registry where Registry.registry_path IN ("*\\Environment\\UserInitMprLogonScript") by Registry.dest Registry.user Registry.registry_path Registry.registry_key_name Registry.registry_value_name
| `security_content_ctime(lastTime)`
| `security_content_ctime(firstTime)`
| `drop_dm_object_name(Registry)`
| `logon_script_event_trigger_execution_filter`
Macros
The SPL above uses the following Macros:
logon_script_event_trigger_execution_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
- Registry.dest
- Registry.user
- Registry.registry_path
- Registry.registry_key_name
- Registry.registry_value_name
How To Implement
To successfully implement this search, you must be ingesting data that records registry activity from your hosts to populate the endpoint data model in the registry node. This is typically populated via endpoint detection-and-response product, such as Carbon Black or endpoint data sources, such as Sysmon. The data used for this search is typically generated via logs that report reads and writes to the registry.
Known False Positives
unknown
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
80.0 | 80 | 100 | modified/added/deleted registry entry $Registry.registry_path$ in $dest$ |
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: 1