Windows AutoIt3 Execution

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

The following analytic detects the execution of AutoIt3, a scripting language often used for automating Windows GUI tasks and general scripting. It identifies instances where AutoIt3 or its variants are executed by searching for process names or original file names matching 'autoit3.exe'. This activity is significant because attackers frequently use AutoIt3 to automate malicious actions, such as executing malware. If confirmed malicious, this activity could lead to unauthorized code execution, system compromise, or further propagation of malware within the environment.

name: Windows AutoIt3 Execution
id: 0ecb40d9-492b-4a57-9f87-515dd742794c
version: 14
creation_date: '2023-11-16'
modification_date: '2026-05-13'
author: Michael Haag, Splunk
status: production
type: TTP
description: |
    The following analytic detects the execution of AutoIt3, a scripting
    language often used for automating Windows GUI tasks and general scripting.
    It identifies instances where AutoIt3 or its variants are executed by searching for process names
    or original file names matching 'autoit3.exe'.
    This activity is significant because attackers frequently use AutoIt3 to automate malicious actions, such as executing malware.
    If confirmed malicious, this activity could lead to unauthorized code execution,
    system compromise, or further propagation of malware within the environment.
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_name = "autoit*.exe"
      OR
      Processes.original_file_name = "autoit*.exe"
    )
    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_autoit3_execution_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: False positives may be present if the application is legitimately used, filter by user or endpoint as needed.
references:
    - https://github.com/PaloAltoNetworks/Unit42-timely-threat-intel/blob/main/2023-10-25-IOCs-from-DarkGate-activity.txt
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 ("$dest$", "$user$") | 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: Execution of AutoIt3 detected. The source process is $parent_process_name$ and the destination process is $process_name$ on $dest$ by
    entity:
        field: user
        type: user
        score: 50
intermediate_findings:
    entities:
        - field: dest
          type: system
          score: 50
          message: Execution of AutoIt3 detected. The source process is $parent_process_name$ and the destination process is $process_name$ on $dest$ by
threat_objects:
    - field: parent_process_name
      type: parent_process_name
    - field: process_name
      type: process_name
analytic_story:
    - Crypto Stealer
    - Handala Wiper
    - DarkGate Malware
    - Void Manticore
asset_type: Endpoint
mitre_attack_id:
    - T1059
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/T1059/autoit/sysmon.log
          source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
          sourcetype: XmlWinEventLog
      test_type: unit

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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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