Windows Registry Payload Injection

Name:Windows Registry Payload Injection
id:c6b2d80f-179a-41a1-b95e-ce5601d7427a
version:7
date:2025-04-15
author:Steven Dick
status:production
type:TTP
Description:The following analytic detects suspiciously long data written to the Windows registry, a behavior often linked to fileless malware or persistence techniques. It leverages Endpoint Detection and Response (EDR) telemetry, focusing on registry events with data lengths exceeding 512 characters. This activity is significant as it can indicate an attempt to evade traditional file-based defenses, making it crucial for SOC monitoring. If confirmed malicious, this technique could allow attackers to maintain persistence, execute code, or manipulate system configurations without leaving a conventional file footprint.
Data_source:

• -Sysmon EventID 13

search:| tstats `security_content_summariesonly` count from datamodel=Endpoint.Registry where Registry.registry_value_data=* by _time span=1h Registry.dest Registry.registry_path Registry.registry_value_name Registry.process_guid Registry.registry_value_data Registry.registry_key_name Registry.registry_hive Registry.status Registry.action Registry.process_id Registry.user Registry.vendor_product
| `drop_dm_object_name(Registry)`
| eval reg_data_len = len(registry_value_data)
| where reg_data_len > 512
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `windows_registry_payload_injection_filter`

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:Unknown, possible custom scripting.
References:
  -https://www.mandiant.com/resources/blog/tracking-evolution-gootloader-operations
  -https://www.trendmicro.com/vinfo/us/security/news/cybercrime-and-digital-threats/kovter-an-evolving-malware-gone-fileless
  -https://attack.mitre.org/techniques/T1027/011/
drilldown_searches:
 name:'View the detection results for - "$dest$"'
 search:'%original_detection_search% | search dest = "$dest$"'
 earliest_offset:'$info_min_time$'
 latest_offset:'$info_max_time$'
 name:'View risk events for the last 7 days for - "$dest$"'
 search:'| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") starthoursago=168 | 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:'$info_min_time$'
 latest_offset:'$info_max_time$'
tags:
  analytic_story:
    - 'Unusual Processes'
  asset_type:Endpoint
  mitre_attack_id:
    - 'T1027.011'
  product:
    - 'Splunk Enterprise'
    - 'Splunk Enterprise Security'
    - 'Splunk Cloud'
  security_domain:endpoint

tests:
 name:'True Positive Test'
 attack_data:
  data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/malware/gootloader/partial_ttps/windows-sysmon.log
  source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
  sourcetype: XmlWinEventLog
manual_test:None