Detection: Splunk Unauthorized Notification Input by User

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: 4b7f368f-4322-47f8-8363-2c466f0b7030 Author: Rod Soto Type: Hunting Product: Splunk Enterprise Security

Description

This hunting search provides information to track possible exploitation of a lower privilege user able to push notifications that may include malicious code as notifications for all users in Splunk.

1
2| rest /services/messages 
3| table title message severity timeCreated_iso published splunk_server author 
4| `splunk_unauthorized_notification_input_by_user_filter`

Data Source

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

Macros Used

Name Value

| splunk_unauthorized_notification_input_by_user_filter | search * |

splunk_unauthorized_notification_input_by_user_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
T1548 Abuse Elevation Control Mechanism Defense Evasion
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_10

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 Splunk rest data.

Known False Positives

This search will produce false positives which may include benign notifications from other Splunk entities, attention to suspicious or anomalous elements in notifications helps identify actual exploitation of this vulnerability.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Please review messages at $splunk_server for possible unauthorized notification input. 20 20 100
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 Not Applicable N/A N/A N/A
Unit ✅ Passing Dataset _json _json
Integration ✅ Passing Dataset _json _json

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