:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

Try in Splunk Security Cloud

Description

This search aims to detect the Supernova webshell used in the SUNBURST attack.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Web
  • Last Updated: 2021-01-06
  • Author: John Stoner, Splunk
  • ID: 2ec08a09-9ff1-4dac-b59f-1efd57972ec1

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1505.003 Web Shell Persistence
T1133 External Remote Services Persistence, Initial Access
Kill Chain Phase
  • Installation
  • Delivery
NIST
  • DE.CM
CIS20
  • CIS 13
CVE 


1
2
3



| tstats `security_content_summariesonly` count from datamodel=Web.Web where web.url=*logoimagehandler.ashx*codes* OR Web.url=*logoimagehandler.ashx*clazz* OR Web.url=*logoimagehandler.ashx*method* OR Web.url=*logoimagehandler.ashx*args* by Web.src Web.dest Web.url Web.vendor_product Web.user Web.http_user_agent _time span=1s 
| `supernova_webshell_filter`

Macros

The SPL above uses the following Macros:

:information_source: supernova_webshell_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • Web.url
  • Web.src
  • Web.dest
  • Web.vendor_product
  • Web.user
  • Web.http_user_agent

How To Implement

To successfully implement this search, you need to be monitoring web traffic to your Solarwinds Orion. The logs should be ingested into splunk and populating/mapped to the Web data model.

Known False Positives

There might be false positives associted with this detection since items like args as a web argument is pretty generic.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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 | version: 1