4.2 KiB
title, excerpt, categories, last_modified_at, toc, toc_label, tags
| title | excerpt | categories | last_modified_at | toc | toc_label | tags | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Supernova Webshell | Web Shell |
|
2021-01-06 | true |
|
⚠️ WARNING THIS IS A EXPERIMENTAL analytic
We have not been able to test, simulate, or build datasets for this object. Use at your own risk. This analytic is NOT supported.
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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
| ID | Technique | Tactic |
|---|---|---|
| T1505.003 | Web Shell | Persistence |
Kill Chain Phase
- Exploitation
NIST
- PR.DS
- ID.RA
- PR.PT
- PR.IP
- DE.CM
CIS20
- CIS 4
- CIS 13
- CIS 18
CVE
Search
| 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:
ℹ️ 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 field
- _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 |
ℹ️ 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
- https://www.splunk.com/en_us/blog/security/detecting-supernova-malware-solarwinds-continued.html
- https://www.guidepointsecurity.com/blog/supernova-solarwinds-net-webshell-analysis/
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