Detection rules › Splunk

Windows Modify Registry With MD5 Reg Key Name

Status
production
Severity
medium
Group by
TargetObject, computer_name, details, event_type, process_guid, process_id, registry_hive, registry_path, registry_status, registry_value_name, registry_value_type, user, vendor_product
Author
Teoderick Contreras, Splunk
Source
github.com/splunk/security_content

The following analytic detects potentially malicious registry modifications characterized by MD5-like registry key names. It leverages the Endpoint data model to identify registry entries under the SOFTWARE path with 32-character hexadecimal names, a technique often used by NjRAT malware for fileless storage of keylogs and .DLL plugins. This activity is significant as it can indicate the presence of NjRAT or similar malware, which can lead to unauthorized data access and persistent threats within the environment. If confirmed malicious, attackers could maintain persistence and exfiltrate sensitive information.

MITRE ATT&CK coverage

TacticTechniques
PersistenceT1112 Modify Registry
Defense ImpairmentT1112 Modify Registry

Event coverage

ProviderEventTitle
SysmonEvent ID 13RegistryEvent (Value Set)

Rule body splunk

name: Windows Modify Registry With MD5 Reg Key Name
id: 4662c6b1-0754-455e-b9ff-3ee730af3ba8
version: 12
creation_date: '2023-09-25'
modification_date: '2026-05-13'
author: Teoderick Contreras, Splunk
status: production
type: TTP
description: The following analytic detects potentially malicious registry modifications characterized by MD5-like registry key names. It leverages the Endpoint data model to identify registry entries under the SOFTWARE path with 32-character hexadecimal names, a technique often used by NjRAT malware for fileless storage of keylogs and .DLL plugins. This activity is significant as it can indicate the presence of NjRAT or similar malware, which can lead to unauthorized data access and persistent threats within the environment. If confirmed malicious, attackers could maintain persistence and exfiltrate sensitive information.
data_source:
    - Sysmon EventID 13
search: '| tstats `security_content_summariesonly`  count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where  Registry.registry_path = "*\\SOFTWARE\\*" Registry.registry_value_data = "Binary Data" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product | `drop_dm_object_name(Registry)` | eval dropped_reg_path = split(registry_path, "\\") | eval dropped_reg_path_split_count = mvcount(dropped_reg_path) | eval validation_result= if(match(registry_value_name,"^[0-9a-fA-F]{32}$"),"md5","nonmd5") | where validation_result = "md5" AND dropped_reg_path_split_count <= 5 | table dest user registry_path registry_value_name registry_value_data registry_key_name reg_key_name dropped_reg_path_split_count validation_result | `security_content_ctime(lastTime)` | `security_content_ctime(firstTime)` | `windows_modify_registry_with_md5_reg_key_name_filter`'
how_to_implement: To successfully implement this search you need to be ingesting information on process that include the name of the Filesystem responsible for the changes from your endpoints into the `Endpoint` datamodel in the `Filesystem` node.
known_false_positives: No false positives have been identified at this time.
references:
    - https://malpedia.caad.fkie.fraunhofer.de/details/win.njrat
drilldown_searches:
    - name: View the detection results for - "$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 ("$dest$") | 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"
finding:
    title: A md5 registry value name $registry_value_name$ is created on $dest$
    entity:
        field: dest
        type: system
        score: 50
analytic_story:
    - NjRAT
asset_type: Endpoint
mitre_attack_id:
    - T1112
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: endpoint
security_domain: endpoint
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1112/njrat_md5_registry_entry/njrat_reg_binary.log
          source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
          sourcetype: XmlWinEventLog
      test_type: unit

Stages and Predicates

Stage 1: tstats

| tstats `security_content_summariesonly`  count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry where  Registry.registry_path = "*\\SOFTWARE\\*" Registry.registry_value_data = "Binary Data" by Registry.action Registry.dest Registry.process_guid Registry.process_id Registry.registry_hive Registry.registry_path Registry.registry_key_name Registry.registry_value_data Registry.registry_value_name Registry.registry_value_type Registry.status Registry.user Registry.vendor_product

Stage 2: search

| `drop_dm_object_name(Registry)`

Stage 3: eval

| eval dropped_reg_path = split(registry_path, "\\")

Stage 4: eval

| eval dropped_reg_path_split_count = mvcount(dropped_reg_path)

Stage 5: eval

| eval validation_result= if(match(registry_value_name,"^[0-9a-fA-F]{32}$"),"md5","nonmd5")
validation_result =
ifmatch(registry_value_name, "^[0-9a-fA-F]{32}$")"md5"
else"nonmd5"

Stage 6: where

| where validation_result = "md5" AND dropped_reg_path_split_count <= 5

Stage 7: table

| table dest user registry_path registry_value_name registry_value_data registry_key_name reg_key_name dropped_reg_path_split_count validation_result

Stage 8: search

| `security_content_ctime(lastTime)`

Stage 9: search

| `security_content_ctime(firstTime)`

Stage 10: search

| `windows_modify_registry_with_md5_reg_key_name_filter`

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

FieldKindValues
Registry.registry_patheq
  • "*\\SOFTWARE\\*"
Registry.registry_value_dataeq
  • "Binary Data" corpus 5 (splunk 3, sigma 2)
dropped_reg_path_split_countle
  • 5
validation_resulteq
  • "md5"