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splunk_escuCorrelation

Windows Post Exploitation Risk Behavior

The following analytic identifies four or more distinct post-exploitation behaviors on a Windows system. It leverages data from the Risk data model in Splunk Enterprise Security, focusing on multiple risk events and their associated MITRE ATT&CK tactics and techniques. This activity is significant as it indicates potential malicious actions following an initial compromise, such as persistence, privilege escalation, or data exfiltration. If confirmed malicious, this behavior could allow attackers to maintain control, escalate privileges, and further exploit the compromised environment, leading to significant security breaches and data loss.

MITRE ATT&CK

discoverycredential-access

Detection Query

| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk
  WHERE All_Risk.analyticstories IN ("*Windows Post-Exploitation*")
  BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic
| `drop_dm_object_name(All_Risk)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| where source_count >= 4
| `windows_post_exploitation_risk_behavior_filter`

Author

Teoderick Contreras, Splunk

Created

2026-02-25

Tags

Windows Post-Exploitation
Raw Content
name: Windows Post Exploitation Risk Behavior
id: edb930df-64c2-4bb7-9b5c-889ed53fb973
version: 7
date: '2026-02-25'
author: Teoderick Contreras, Splunk
status: production
type: Correlation
data_source: []
description: The following analytic identifies four or more distinct post-exploitation behaviors on a Windows system. It leverages data from the Risk data model in Splunk Enterprise Security, focusing on multiple risk events and their associated MITRE ATT&CK tactics and techniques. This activity is significant as it indicates potential malicious actions following an initial compromise, such as persistence, privilege escalation, or data exfiltration. If confirmed malicious, this behavior could allow attackers to maintain control, escalate privileges, and further exploit the compromised environment, leading to significant security breaches and data loss.
search: |-
    | tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count FROM datamodel=Risk.All_Risk
      WHERE All_Risk.analyticstories IN ("*Windows Post-Exploitation*")
      BY All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic
    | `drop_dm_object_name(All_Risk)`
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | where source_count >= 4
    | `windows_post_exploitation_risk_behavior_filter`
how_to_implement: Splunk Enterprise Security is required to utilize this correlation. In addition, modify the source_count value to your environment. In our testing, a count of 4 or 5 was decent in a lab, but the number may need to be increased base on internal testing. In addition, based on false positives, modify any analytics to be anomaly and lower or increase risk based on organization importance.
known_false_positives: False positives will be present based on many factors. Tune the correlation as needed to reduce too many triggers.
references:
    - https://github.com/carlospolop/PEASS-ng/tree/master/winPEAS/winPEASbat
drilldown_searches:
    - name: View the detection results for - "$risk_object$"
      search: '%original_detection_search% | search  risk_object = "$risk_object$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$risk_object$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$risk_object$") 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: $info_max_time$
tags:
    analytic_story:
        - Windows Post-Exploitation
    asset_type: Endpoint
    mitre_attack_id:
        - T1012
        - T1049
        - T1069
        - T1016
        - T1003
        - T1082
        - T1115
        - T1552
    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/T1552/windows_post_exploitation/windows_post_exploitation_risk.log
          source: wpe
          sourcetype: stash