Detection: Windows Java Spawning Shells

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.

Description

The following analytic identifies instances where java.exe or w3wp.exe spawns a Windows shell, such as cmd.exe or powershell.exe. This detection leverages data from Endpoint Detection and Response (EDR) agents, focusing on process and parent process relationships. This activity is significant as it may indicate exploitation attempts, such as those related to CVE-2021-44228 (Log4Shell). If confirmed malicious, attackers could execute arbitrary commands, potentially leading to system compromise, data exfiltration, or further lateral movement within the network.

1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.parent_process_name=java.exe OR Processes.parent_process_name=w3wp.exe `windows_shells` by Processes.dest Processes.user Processes.parent_process_name Processes.process_name Processes.process Processes.process_id Processes.parent_process_id 
3| `drop_dm_object_name(Processes)` 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| `windows_java_spawning_shells_filter`

Data Source

Name Platform Sourcetype Source
CrowdStrike ProcessRollup2 N/A 'crowdstrike:events:sensor' 'crowdstrike'
Sysmon EventID 1 Windows icon Windows 'xmlwineventlog' 'XmlWinEventLog:Microsoft-Windows-Sysmon/Operational'
Windows Event Log Security 4688 Windows icon Windows 'xmlwineventlog' 'XmlWinEventLog:Security'

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
windows_java_spawning_shells_filter search *
windows_java_spawning_shells_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
T1190 Exploit Public-Facing Application Initial Access
T1133 External Remote Services Initial Access
KillChainPhase.DELIVERY
KillChainPhase.INSTALLATION
NistCategory.DE_CM
Cis18Value.CIS_10
APT28
APT29
APT39
APT41
APT5
Agrius
Axiom
BackdoorDiplomacy
BlackTech
Blue Mockingbird
Cinnamon Tempest
Dragonfly
Earth Lusca
Ember Bear
FIN13
FIN7
Fox Kitten
GALLIUM
GOLD SOUTHFIELD
HAFNIUM
INC Ransom
Ke3chang
Kimsuky
Magic Hound
Moses Staff
MuddyWater
Play
Rocke
Sandworm Team
Threat Group-3390
ToddyCat
Volatile Cedar
Volt Typhoon
Winter Vivern
menuPass
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

The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes node of the Endpoint data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.

Known False Positives

Filtering may be required on internal developer build systems or classify assets as web facing and restrict the analytic based on that.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
An instance of $parent_process_name$ spawning $process_name$ was identified on endpoint $dest$ spawning a Windows shell, potentially indicative of exploitation. 40 80 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: 5