Detection: GitHub Enterprise Register Self Hosted Runner

Updated Date: 2026-05-13 ID: b27685a2-8826-4123-ab78-2d9d0d419ed0 Author: Patrick Bareiss, Splunk Type: Anomaly Product: Splunk Enterprise Security

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.

Search

 1`github_enterprise` action=enterprise.register_self_hosted_runner
 2  
 3| fillnull
 4  
 5| stats count min(_time) as firstTime max(_time) as lastTime
 6    BY actor, actor_id, actor_is_bot,
 7       actor_location.country_code, business, business_id,
 8       user_agent, action
 9  
10| eval user=actor
11  
12| `security_content_ctime(firstTime)`
13  
14| `security_content_ctime(lastTime)`
15  
16| `github_enterprise_register_self_hosted_runner_filter`

Data Source

Name	Platform	Sourcetype	Source
GitHub Enterprise Audit Logs	Other	`'httpevent'`	`'http:github'`

Macros Used

Name	Value
github_enterprise	`source=http:github sourcetype=httpevent`
github_enterprise_register_self_hosted_runner_filter	`search *`

github_enterprise_register_self_hosted_runner_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic
T1195	Supply Chain Compromise	Initial Access
T1685	Disable or Modify Tools	Defense Impairment

Exploitation

Delivery

DE.AE

CIS 13

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`0 * * * *`
Earliest Time	`-70m@m`
Latest Time	`-10m@m`
Schedule Window	`auto`
Creates Finding (Notable)	No
Creates Intermediate Finding (Risk Event)	Yes

Anomaly detections generate Intermediate Findings (Risk Events). They do not generate a Finding (Notable) directly.

Implementation

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.

Associated Analytic Story

Intermediate Findings

Message	Entity Field	Entity Type	Risk Score
$user$ created a self-hosted runner in GitHub Enterprise	user	user	20

Threat Objects

Field	Type
user_agent	http_user_agent

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	✅ Passing	N/A	N/A	N/A
Unit	✅ Passing	Dataset	`http:github`	`httpevent`
Integration	✅ Passing	Dataset	`http:github`	`httpevent`

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

Source: GitHub | Version: 9