Detection rules › Splunk

Windows Command Shell DCRat ForkBomb Payload

Status
production
Severity
medium
Group by
_time, computer_name, original_file_name, parent_command_line, parent_process_name, process_name, user
Author
Teoderick Contreras, Splunk
Source
github.com/splunk/security_content

The following analytic detects the execution of a DCRat "forkbomb" payload, which spawns multiple cmd.exe processes that launch notepad.exe instances in quick succession. This detection leverages Endpoint Detection and Response (EDR) data, focusing on the rapid creation of cmd.exe and notepad.exe processes within a 30-second window. This activity is significant as it indicates a potential DCRat infection, a known Remote Access Trojan (RAT) with destructive capabilities. If confirmed malicious, this behavior could lead to system instability, resource exhaustion, and potential disruption of services.

MITRE ATT&CK coverage

Event coverage

ProviderEventTitle
SysmonEvent ID 1Process creation

Rule body splunk

name: Windows Command Shell DCRat ForkBomb Payload
id: 2bb1a362-7aa8-444a-92ed-1987e8da83e1
version: 14
creation_date: '2022-07-28'
modification_date: '2026-05-13'
author: Teoderick Contreras, Splunk
status: production
type: TTP
description: The following analytic detects the execution of a DCRat "forkbomb" payload, which spawns multiple cmd.exe processes that launch notepad.exe instances in quick succession. This detection leverages Endpoint Detection and Response (EDR) data, focusing on the rapid creation of cmd.exe and notepad.exe processes within a 30-second window. This activity is significant as it indicates a potential DCRat infection, a known Remote Access Trojan (RAT) with destructive capabilities. If confirmed malicious, this behavior could lead to system instability, resource exhaustion, and potential disruption of services.
data_source:
    - Sysmon EventID 1
    - CrowdStrike ProcessRollup2
search: |-
    | tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.action) as action values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product dc(Processes.parent_process_id) as parent_process_id_count dc(Processes.process_id) as process_id_count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
      WHERE Processes.parent_process_name= "cmd.exe" (Processes.process_name = "notepad.exe"
        OR
        Processes.original_file_name= "notepad.exe") Processes.parent_process = "*.bat*"
      BY Processes.parent_process_name Processes.process_name Processes.original_file_name
         Processes.parent_process Processes.dest Processes.user
         _time span=30s
    | where parent_process_id_count>= 10 AND process_id_count >=10
    | `drop_dm_object_name(Processes)`
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | `windows_command_shell_dcrat_forkbomb_payload_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: No false positives have been identified at this time.
references:
    - https://cert.gov.ua/article/405538
    - https://malpedia.caad.fkie.fraunhofer.de/details/win.dcrat
    - https://www.mandiant.com/resources/analyzing-dark-crystal-rat-backdoor
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: Multiple cmd.exe processes with child process of notepad.exe executed on $dest$
    entity:
        field: dest
        type: system
        score: 50
analytic_story:
    - Compromised Windows Host
    - DarkCrystal RAT
asset_type: Endpoint
mitre_attack_id:
    - T1059.003
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/malware/dcrat/dcrat_forkbomb/sysmon.log
          source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
          sourcetype: XmlWinEventLog
      test_type: unit

Stages and Predicates

Stage 1: tstats

| tstats `security_content_summariesonly` values(Processes.user) as user values(Processes.action) as action values(Processes.parent_process_exec) as parent_process_exec values(Processes.parent_process_guid) as parent_process_guid values(Processes.parent_process_id) as parent_process_id values(Processes.parent_process_path) as parent_process_path values(Processes.process) as process values(Processes.process_exec) as process_exec values(Processes.process_guid) as process_guid values(Processes.process_hash) as process_hash values(Processes.process_id) as process_id values(Processes.process_integrity_level) as process_integrity_level values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product dc(Processes.parent_process_id) as parent_process_id_count dc(Processes.process_id) as process_id_count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
  WHERE Processes.parent_process_name= "cmd.exe" (Processes.process_name = "notepad.exe"
    OR
    Processes.original_file_name= "notepad.exe") Processes.parent_process = "*.bat*"
  BY Processes.parent_process_name Processes.process_name Processes.original_file_name
     Processes.parent_process Processes.dest Processes.user
     _time span=30s

Stage 2: where

| where parent_process_id_count>= 10 AND process_id_count >=10

Stage 3: search

| `drop_dm_object_name(Processes)`

Stage 4: search

| `security_content_ctime(firstTime)`

Stage 5: search

| `security_content_ctime(lastTime)`

Stage 6: search

| `windows_command_shell_dcrat_forkbomb_payload_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
Processes.original_file_nameeq
  • "notepad.exe"
Processes.parent_processeq
  • "*.bat*" corpus 2 (sigma 1, splunk 1)
Processes.parent_process_nameeq
  • "cmd.exe" corpus 15 (elastic 10, splunk 4, kusto 1)
Processes.process_nameeq
  • "notepad.exe" corpus 4 (elastic 2, splunk 2)
parent_process_id_countge
  • 10
process_id_countge
  • 10