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splunk_escuAnomaly

Excessive distinct processes from Windows Temp

The following analytic identifies an excessive number of distinct processes executing from the Windows\Temp directory. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths and counts within a 20-minute window. This behavior is significant as it often indicates the presence of post-exploit frameworks like Koadic and Meterpreter, which use this technique to execute malicious actions. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, and maintain persistence within the environment, posing a severe threat to system integrity and security.

MITRE ATT&CK

execution

Detection Query

| tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_process_count min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process 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_name) as parent_process_name 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_name) as process_name values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\Temp\\*" by Processes.dest Processes.user _time span=20m |  where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_filter`

Author

Michael Hart, Mauricio Velazco, Splunk

Created

2026-03-10

Data Sources

Sysmon EventID 1Windows Event Log Security 4688CrowdStrike ProcessRollup2

Tags

Meterpreter
Raw Content
name: Excessive distinct processes from Windows Temp
id: 23587b6a-c479-11eb-b671-acde48001122
version: 9
date: '2026-03-10'
author: Michael Hart, Mauricio Velazco, Splunk
status: production
type: Anomaly
description: The following analytic identifies an excessive number of distinct processes executing from the Windows\Temp directory. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths and counts within a 20-minute window. This behavior is significant as it often indicates the presence of post-exploit frameworks like Koadic and Meterpreter, which use this technique to execute malicious actions. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, and maintain persistence within the environment, posing a severe threat to system integrity and security.
data_source:
    - Sysmon EventID 1
    - Windows Event Log Security 4688
    - CrowdStrike ProcessRollup2
search: '| tstats `security_content_summariesonly` distinct_count(Processes.process) as distinct_process_count min(_time) as firstTime max(_time) as lastTime values(Processes.action) as action values(Processes.original_file_name) as original_file_name values(Processes.parent_process) as parent_process 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_name) as parent_process_name 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_name) as process_name values(Processes.process_path) as process_path values(Processes.user_id) as user_id values(Processes.vendor_product) as vendor_product from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\Temp\\*" by Processes.dest Processes.user _time span=20m |  where distinct_process_count > 37 | `drop_dm_object_name(Processes)` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `excessive_distinct_processes_from_windows_temp_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: Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold.  Filter as needed.
references:
    - https://www.offensive-security.com/metasploit-unleashed/about-meterpreter/
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 ("$dest$") 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: Multiple processes were executed out of windows\temp within a short amount of time on $dest$.
    risk_objects:
        - field: dest
          type: system
          score: 20
    threat_objects: []
tags:
    analytic_story:
        - Meterpreter
    asset_type: Endpoint
    mitre_attack_id:
        - T1059
    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/excessive_distinct_processes_from_windows_temp/windows-xml.log
          source: XmlWinEventLog:Security
          sourcetype: XmlWinEventLog