The following analytic identifies the creation of a new Automation Runbook Webhook within an Azure tenant. Azure Automation is a cloud-based automation platform that allows administrators to automate Azure management tasks and orchestrate actions across external systems within Azure. Azure Automation script files called Runbooks that can be written in PowerShell or Python. One of the ways administrators can configure a Runbook to be executed is through HTTP Webhooks. Webhooks leverage custom unauthenticated URLs that are exposed to the Internet. An adversary who has obtained privileged access to an Azure tenant may create a Webhook to trigger the execution of an Automation Runbook with malicious code that can create users or execute code on a VM. This provides a persistent foothold on the environment.
- Type: TTP
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2022-08-23
- Author: Mauricio Velazco, Splunk
- ID: e98944a9-92e4-443c-81b8-a322e33ce75a
Kill Chain Phase
- CIS 3
- CIS 5
- CIS 16
1 2 3 `azure_audit` operationName.localizedValue="Create or Update an Azure Automation webhook" status.value=Succeeded | stats values(object) by _time, caller, claims.ipaddr, resourceGroupName, object_path | `azure_runbook_webhook_created_filter`
The SPL above uses the following Macros:
azure_runbook_webhook_created_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
You must install the latest version of Splunk Add-on for Microsoft Cloud Services from Splunkbase (https://splunkbase.splunk.com/app/3110/#/details). You must be ingesting Azure Audit events into your Splunk environment. Specifically, this analytic leverages the Azure Activity log category.
Known False Positives
Administrators may legitimately create Azure Runbook Webhooks. Filter as needed.
Associated Analytic Story
|63.0||70||90||A new Azure Runbook Webhook $object$ was created by $caller$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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