Detection: Splunk Unauthorized Experimental Items Creation

Updated Date: 2024-10-17 ID: 84afda04-0cd6-466b-869e-70d6407d0a34 Author: Rod Soto, Chase Franklin Type: Hunting Product: Splunk Enterprise Security

Description

This hunting search provides information on finding possible creation of unauthorized items against /experimental endpoint.

1`splunkda` */experimental/* method=POST 
2| stats count min(_time) as firstTime max(_time) as lastTime by clientip method uri_path uri status 
3| `security_content_ctime(firstTime)` 
4| `security_content_ctime(lastTime)` 
5| `splunk_unauthorized_experimental_items_creation_filter`

Data Source

Name Platform Sourcetype Source
Splunk Splunk icon Splunk 'splunkd_ui_access' 'splunkd_ui_access.log'

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
splunk_unauthorized_experimental_items_creation_filter search *
splunk_unauthorized_experimental_items_creation_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
T1189 Drive-by Compromise Initial Access
KillChainPhase.DELIVERY
NistCategory.DE_AE
Cis18Value.CIS_10
APT19
APT28
APT32
APT37
APT38
Andariel
Axiom
BRONZE BUTLER
CURIUM
Daggerfly
Dark Caracal
Darkhotel
Dragonfly
Earth Lusca
Elderwood
Lazarus Group
Leafminer
Leviathan
Machete
Magic Hound
Mustard Tempest
PLATINUM
PROMETHIUM
Patchwork
RTM
Threat Group-3390
Transparent Tribe
Turla
Windigo
Windshift
Winter Vivern

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 Risk Event False
This configuration file applies to all detections of type hunting.

Implementation

Requires access to internal indexes.

Known False Positives

Not all requests are going to be malicious, there will be false positives, however operator must find suspicious items that might have been created by an unauthorized user.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Possible unauthorized creation of experimental items from $clientip$ 5 5 100
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 ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset /opt/splunk/var/log/splunk/splunkd_access.log splunkd_access
Integration ✅ Passing Dataset /opt/splunk/var/log/splunk/splunkd_access.log splunkd_access

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: 2