Detection rules › Splunk

GitHub Enterprise Register Self Hosted Runner

Status
production
Severity
low
Group by
"actor_location.country_code", action, actor, actor_id, actor_is_bot, aws::userAgent, business, business_id
Author
Patrick Bareiss, Splunk
Source
github.com/splunk/security_content

The following analytic identifies when a self-hosted runner is created in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for actions related to creating new self-hosted runners at the organization or enterprise level. his behavior warrants monitoring because self-hosted runners execute workflow jobs on customer-controlled infrastructure, which could be exploited by attackers to execute malicious code, access sensitive data, or pivot to other systems. While self-hosted runners are a legitimate feature, their creation should be carefully controlled as compromised runners pose significant security risks. The impact includes potential remote code execution, data exfiltration, and lateral movement within the environment if a runner is compromised. SOC teams should investigate unexpected runner creation events to verify they are authorized and properly secured, especially if created by unfamiliar users or in unusual contexts.

MITRE ATT&CK coverage

TacticTechniques
Initial AccessT1195 Supply Chain Compromise
Defense ImpairmentT1685 Disable or Modify Tools

Rules detecting the same action

Other rules on this platform that filter on the same API call or operation.

Rule body splunk

name: GitHub Enterprise Register Self Hosted Runner
id: b27685a2-8826-4123-ab78-2d9d0d419ed0
version: 9
creation_date: '2025-01-15'
modification_date: '2026-05-13'
author: Patrick Bareiss, Splunk
status: production
type: Anomaly
description: The following analytic identifies when a self-hosted runner is created in GitHub Enterprise. The detection monitors GitHub Enterprise audit logs for actions related to creating new self-hosted runners at the organization or enterprise level. his behavior warrants monitoring because self-hosted runners execute workflow jobs on customer-controlled infrastructure, which could be exploited by attackers to execute malicious code, access sensitive data, or pivot to other systems. While self-hosted runners are a legitimate feature, their creation should be carefully controlled as compromised runners pose significant security risks. The impact includes potential remote code execution, data exfiltration, and lateral movement within the environment if a runner is compromised. SOC teams should investigate unexpected runner creation events to verify they are authorized and properly secured, especially if created by unfamiliar users or in unusual contexts.
data_source:
    - GitHub Enterprise Audit Logs
search: |-
    `github_enterprise` action=enterprise.register_self_hosted_runner
      | fillnull
      | stats count min(_time) as firstTime max(_time) as lastTime
        BY actor, actor_id, actor_is_bot,
           actor_location.country_code, business, business_id,
           user_agent, action
      | eval user=actor
      | `security_content_ctime(firstTime)`
      | `security_content_ctime(lastTime)`
      | `github_enterprise_register_self_hosted_runner_filter`
how_to_implement: You must ingest GitHub Enterprise logs using Audit log streaming as described in this documentation https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk using a Splunk HTTP Event Collector.
known_false_positives: No false positives have been identified at this time.
references:
    - https://www.wiz.io/blog/shai-hulud-2-0-ongoing-supply-chain-attack
    - https://www.googlecloudcommunity.com/gc/Community-Blog/Monitoring-for-Suspicious-GitHub-Activity-with-Google-Security/ba-p/763610
    - https://docs.github.com/en/enterprise-cloud@latest/admin/monitoring-activity-in-your-enterprise/reviewing-audit-logs-for-your-enterprise/streaming-the-audit-log-for-your-enterprise#setting-up-streaming-to-splunk
drilldown_searches:
    - name: View the detection results for - "$user$"
      search: '%original_detection_search% | search  user = "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$user$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") | 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"
intermediate_findings:
    entities:
        - field: user
          type: user
          score: 20
          message: $user$ created a self-hosted runner in GitHub Enterprise
threat_objects:
    - field: user_agent
      type: http_user_agent
analytic_story:
    - GitHub Malicious Activity
    - NPM Supply Chain Compromise
asset_type: GitHub
mitre_attack_id:
    - T1685
    - T1195
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: cloud
security_domain: network
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1562.001/github_created_self_hosted_runner/github.json
          source: http:github
          sourcetype: httpevent
      test_type: unit

Stages and Predicates

Stage 1: search

`github_enterprise` action=enterprise.register_self_hosted_runner

Stage 2: fillnull

| fillnull

Stage 3: stats

| stats count min(_time) as firstTime max(_time) as lastTime
    BY actor, actor_id, actor_is_bot,
       actor_location.country_code, business, business_id,
       user_agent, action

Stage 4: eval

| eval user=actor

Stage 5: search

| `security_content_ctime(firstTime)`

Stage 6: search

| `security_content_ctime(lastTime)`

Stage 7: search

| `github_enterprise_register_self_hosted_runner_filter`

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

FieldKindValues
actioneq
  • enterprise.register_self_hosted_runner
sourcetypeeq
  • httpevent