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.

Updated Date: 2024-08-15 ID: 28c81306-5c47-11ec-bfea-acde48001122 Author: Michael Haag, Splunk Type: TTP Product: Splunk Enterprise Security

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.

Search

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	Supported App
CrowdStrike ProcessRollup2	N/A	`'crowdstrike:events:sensor'`	`'crowdstrike'`	N/A

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

Axiom

BackdoorDiplomacy

BlackTech

Blue Mockingbird

Cinnamon Tempest

Dragonfly

Earth Lusca

FIN13

FIN7

Fox Kitten

GALLIUM

GOLD SOUTHFIELD

HAFNIUM

Ke3chang

Kimsuky

Magic Hound

Moses Staff

MuddyWater

Rocke

Sandworm Team

Threat Group-3390

ToddyCat

Volatile Cedar

Volt Typhoon

menuPass

APT18

APT28

APT29

APT41

Akira

Chimera

Dragonfly

FIN13

FIN5

GALLIUM

GOLD SOUTHFIELD

Ke3chang

Kimsuky

LAPSUS$

Leviathan

OilRig

Sandworm Team

Scattered Spider

TeamTNT

Threat Group-3390

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: 4