Windows Process With NamedPipe CommandLine


Description

The following analytic detects processes with command lines containing named pipes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions. This behavior is significant as it is often used by adversaries, such as those behind the Olympic Destroyer malware, for inter-process communication post-injection, aiding in defense evasion and privilege escalation. If confirmed malicious, this activity could allow attackers to maintain persistence, escalate privileges, or evade defenses, potentially leading to further compromise of the system.

Query · spl

| tstats `security_content_summariesonly`
  count min(_time) as firstTime
        max(_time) as lastTime

from datamodel=Endpoint.Processes where

Processes.process = "*\\\\.\\pipe\\*"
(
    NOT Processes.parent_process_path IN (
        "*:\\Program Files (x86)\\*",
        "*:\\Program Files\\*",
        "*:\\Windows\\System32\\*",
        "*:\\Windows\\SysWOW64\\*"
    )
    OR
    NOT Processes.process_path IN (
        "*:\\Program Files (x86)\\*",
        "*:\\Program Files\\*",
        "*:\\Windows\\System32\\*",
        "*:\\Windows\\SysWOW64\\*"
    )
)

by Processes.action Processes.dest Processes.original_file_name Processes.parent_process
   Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id
   Processes.parent_process_name Processes.parent_process_path Processes.process
   Processes.process_exec Processes.process_guid Processes.process_hash
   Processes.process_id Processes.process_integrity_level Processes.process_name
   Processes.process_path Processes.user Processes.user_id Processes.vendor_product

| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `windows_process_with_namedpipe_commandline_filter`

Implementation guide

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

  • Normal browser application may use this technique. Please update the filter macros to remove false positives.

Analyst notes

Known false positives: Normal browser application may use this technique. Please update the filter macros to remove false positives.

Raw source Windows Process With NamedPipe CommandLine · SPL
Esc
Published by splunk/security_content ↗, licensed under Apache 2.0 ↗. Reproduced here unmodified.
name: Windows Process With NamedPipe CommandLine
id: e64399d4-94a8-11ec-a9da-acde48001122
version: 10
creation_date: '2022-02-23'
modification_date: '2026-07-08'
author: Teoderick Contreras, Splunk
status: production
type: Anomaly
description: |-
    The following analytic detects processes with command lines containing named pipes. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process command-line executions.
    This behavior is significant as it is often used by adversaries, such as those behind the Olympic Destroyer malware, for inter-process communication post-injection, aiding in defense evasion and privilege escalation.
    If confirmed malicious, this activity could allow attackers to maintain persistence, escalate privileges, or evade defenses, potentially leading to further compromise of the system.
data_source:
    - Sysmon EventID 1
    - Windows Event Log Security 4688
    - CrowdStrike ProcessRollup2
search: |-
    | tstats `security_content_summariesonly`
      count min(_time) as firstTime
            max(_time) as lastTime

    from datamodel=Endpoint.Processes where

    Processes.process = "*\\\\.\\pipe\\*"
    (
        NOT Processes.parent_process_path IN (
            "*:\\Program Files (x86)\\*",
            "*:\\Program Files\\*",
            "*:\\Windows\\System32\\*",
            "*:\\Windows\\SysWOW64\\*"
        )
        OR
        NOT Processes.process_path IN (
            "*:\\Program Files (x86)\\*",
            "*:\\Program Files\\*",
            "*:\\Windows\\System32\\*",
            "*:\\Windows\\SysWOW64\\*"
        )
    )

    by Processes.action Processes.dest Processes.original_file_name Processes.parent_process
       Processes.parent_process_exec Processes.parent_process_guid Processes.parent_process_id
       Processes.parent_process_name Processes.parent_process_path Processes.process
       Processes.process_exec Processes.process_guid Processes.process_hash
       Processes.process_id Processes.process_integrity_level Processes.process_name
       Processes.process_path Processes.user Processes.user_id Processes.vendor_product

    | `drop_dm_object_name(Processes)`
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | `windows_process_with_namedpipe_commandline_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: |-
    Normal browser application may use this technique. Please update the filter macros to remove false positives.
references:
    - https://blog.talosintelligence.com/2018/02/olympic-destroyer.html
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"
intermediate_findings:
    entities:
        - field: dest
          type: system
          score: 20
          message: The parent process $parent_process$ spawned a process with the CommandLine $process$ that contains a named pipe on $dest$
analytic_story:
    - Windows Defense Evasion Tactics
asset_type: Endpoint
mitre_attack_id:
    - T1055
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/olympic_destroyer/sysmon.log
          source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
          sourcetype: XmlWinEventLog
      test_type: unit

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