Potentially malicious code on commandline

SPLUNK detection rule by Michael Hart, Splunk · Apache-2.0

The following analytic detects potentially malicious command lines using a pretrained machine learning text classifier. It identifies unusual keyword combinations in command lines, such as "streamreader," "webclient," "mutex," "function," and "computehash," which are often associated with adversarial PowerShell code execution for C2 communication. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command lines longer than 200 characters. This activity is significant as it can indicate an attempt to execute malicious scripts, potentially leading to unauthorized code execution, data exfiltration, or further system compromise.

name: Potentially malicious code on commandline
id: 9c53c446-757e-11ec-871d-acde48001122
version: 7
creation_date: '2022-01-19'
modification_date: '2026-05-13'
author: Michael Hart, Splunk
status: removed
type: Anomaly
description: The following analytic detects potentially malicious command lines using a pretrained machine learning text classifier. It identifies unusual keyword combinations in command lines, such as "streamreader," "webclient," "mutex," "function," and "computehash," which are often associated with adversarial PowerShell code execution for C2 communication. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on command lines longer than 200 characters. This activity is significant as it can indicate an attempt to execute malicious scripts, potentially leading to unauthorized code execution, data exfiltration, or further system compromise.
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 values(Processes.original_file_name) as original_file_name 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_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_name) as process_name values(Processes.process_path) as process_path values(Processes.user) as user  values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product from datamodel="Endpoint.Processes" by Processes.parent_process_name Processes.process_name Processes.process Processes.user Processes.dest | `drop_dm_object_name(Processes)` | where len(process) > 200 | `potentially_malicious_code_on_cmdline_tokenize_score` | apply unusual_commandline_detection | eval score=''predicted(unusual_cmdline_logits)'', process=orig_process | fields - unusual_cmdline* predicted(unusual_cmdline_logits) orig_process | where score > 0.5 | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `potentially_malicious_code_on_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: This model is an anomaly detector that identifies usage of APIs and scripting constructs that are correllated with malicious activity.  These APIs and scripting constructs are part of the programming langauge and advanced scripts may generate false positives.
references:
    - https://attack.mitre.org/techniques/T1059/003/
    - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md
drilldown_searches:
    - name: View the detection results for - "$dest$" and "$user$"
      search: '%original_detection_search% | search  dest = "$dest$" user = "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$dest$" and "$user$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$", "$user$") 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: $info_max_time$
rba:
    message: Unusual command-line execution with command line length greater than 200 found on $dest$ with commandline value - [$process$]
    risk_objects:
        - field: dest
          type: system
          score: 20
        - field: user
          type: user
          score: 20
    threat_objects: []
tags:
    analytic_story:
        - Suspicious Command-Line Executions
    asset_type: Endpoint
    mitre_attack_id:
        - T1059.003
    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/attack_techniques/T1059.001/malicious_cmd_line_samples/windows-sysmon.log
            source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
            sourcetype: XmlWinEventLog
deprecation_info:
    reason: Detection is deprecated as these do not work with the latest Splunk AI Toolkit(5.7.0) and Python for Scientific Computing for Linux 64-bit(4.3.0).
    removed_in_version: 5.26.0
    replacement_content: []

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6091c33e-c5a9-4208-91b4-c3ef519a0a3a

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Executable code, not a declarative rule

This submission is arbitrary [[ currentRule.format ? currentRule.format.toUpperCase() : '' ]] code, not a declarative detection rule — review it as you would any script before running it, and note that it cannot be safely aggregated or auto-executed alongside YARA/Sigma/Suricata/CRS rules.

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Format & documentation suggestions

Not a security issue — this rule is valid and was accepted as-is. These are optional fields/conventions its own format recommends, mainly affecting its quality score and how well it documents itself for other reviewers.

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