Detection: Supernova Webshell

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2024-10-17 ID: 2ec08a09-9ff1-4dac-b59f-1efd57972ec1 Author: John Stoner, Splunk Type: TTP Product: Splunk Enterprise Security

Description

The following analytic detects the presence of the Supernova webshell, used in the SUNBURST attack, by identifying specific patterns in web URLs. The detection leverages Splunk to search for URLs containing "logoimagehandler.ashxcodes*", "logoimagehandler.ashxclazz*", "logoimagehandler.ashxmethod*", and "logoimagehandler.ashxargs*". This activity is significant as it indicates potential unauthorized access and arbitrary code execution on a compromised system. If confirmed malicious, this could lead to data theft, ransomware deployment, or other severe outcomes. Immediate steps include reviewing the web URLs, inspecting on-disk artifacts, and analyzing concurrent processes and network connections.

1
2| 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 
3| `supernova_webshell_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name Value
security_content_summariesonly summariesonly=summariesonly_config allow_old_summaries=oldsummaries_config fillnull_value=fillnull_config``
supernova_webshell_filter search *
supernova_webshell_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
T1505.003 Web Shell Persistence
T1133 External Remote Services Initial Access
KillChainPhase.DELIVERY
KillChainPhase.INSTALLATION
NistCategory.DE_CM
Cis18Value.CIS_13
APT28
APT29
APT32
APT38
APT39
APT5
Agrius
BackdoorDiplomacy
CURIUM
Deep Panda
Dragonfly
Ember Bear
FIN13
Fox Kitten
GALLIUM
HAFNIUM
Kimsuky
Leviathan
Magic Hound
Moses Staff
OilRig
Sandworm Team
Threat Group-3390
Tonto Team
Tropic Trooper
Volatile Cedar
Volt Typhoon
APT18
APT28
APT29
APT41
Akira
Chimera
Dragonfly
Ember Bear
FIN13
FIN5
GALLIUM
GOLD SOUTHFIELD
Ke3chang
Kimsuky
LAPSUS$
Leviathan
OilRig
Play
Sandworm Team
Scattered Spider
TeamTNT
Threat Group-3390
Volt Typhoon
Wizard Spider

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 Notable Yes
Rule Title %name%
Rule Description %description%
Notable Event Fields user, dest
Creates Risk Event True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
tbd 25 50 50
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Not Applicable N/A N/A N/A
Unit ❌ Failing N/A N/A N/A
Integration ❌ Failing N/A N/A N/A

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: 3