The following analytic identifies the 4 most common Ngrok used domains based on DNS queries under the Network Resolution datamodel. It's possible these domains may be ran against the Web datamodel or ran with a direct query across network/proxy traffic. The sign of someone using Ngrok is not malicious, however, more recenctly it has become an adversary tool.
- Type: Anomaly
- Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Datamodel: Network_Resolution
- Last Updated: 2022-11-16
- Author: Michael Haag, Splunk
- ID: 5790a766-53b8-40d3-a696-3547b978fcf0
Kill Chain Phase
- Command and Control
- CIS 13
1 2 3 4 5 6 | tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Network_Resolution where DNS.query IN ("*.ngrok.com","*.ngrok.io", "ngrok.*.tunnel.com", "korgn.*.lennut.com") by DNS.src DNS.query DNS.answer | `drop_dm_object_name("DNS")` | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `ngrok_reverse_proxy_on_network_filter`
The SPL above uses the following Macros:
ngrok_reverse_proxy_on_network_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
The Network Resolution Datamodel will need to have data mapped to it regarding DNS queries. Modify query as needed to use another source.
Known False Positives
False positives will be present based on organizations that allow the use of Ngrok. Filter or monitor as needed.
Associated Analytic Story
|50.0||50||100||An endpoint, $src$, is beaconing out to the reverse proxy service of Ngrok.|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 1