Detection: Linux Docker Privilege Escalation

REMOVED DETECTION

This detection has been removed from the Splunk Threat Research content library and is no longer maintained or supported.

Reason: Detection has been deprecated in favor of two scoped detections that aims to reduce overhead and ease management

Removed in version: 5.26.0

Replacement: Linux Docker Shell Execution

Replacement: Linux Docker Root Directory Mount

If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2026-05-13 ID: 2e7bfb78-85f6-47b5-bc2f-15813a4ef2b3 Author: Gowthamaraj Rajendran, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic detects attempts to escalate privileges on a Linux system using Docker. It identifies processes where Docker commands are used to mount the root directory or execute shell commands within a container. This detection leverages Endpoint Detection and Response (EDR) telemetry, focusing on process names, command-line arguments, and parent processes. This activity is significant because it can allow an attacker with Docker privileges to modify critical system files, such as /etc/passwd, to create a superuser. If confirmed malicious, this could lead to full system compromise and persistent unauthorized access.

Search

 1
 2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Processes
 3  WHERE Processes.process IN("*docker*-v*/*:*","*docker*--volume*/*:*")
 4    OR
 5    Processes.process IN("*docker*exec*sh*","*docker*exec*bash*")
 6  BY Processes.action Processes.dest Processes.original_file_name
 7     Processes.parent_process Processes.parent_process_exec Processes.parent_process_guid
 8     Processes.parent_process_id Processes.parent_process_name Processes.parent_process_path
 9     Processes.process Processes.process_exec Processes.process_guid
10     Processes.process_hash Processes.process_id Processes.process_integrity_level
11     Processes.process_name Processes.process_path Processes.user
12     Processes.user_id Processes.vendor_product
13
14| `drop_dm_object_name(Processes)`
15
16| `security_content_ctime(firstTime)`
17
18| `security_content_ctime(lastTime)`
19
20| `linux_docker_privilege_escalation_filter`

Data Source

Name	Platform	Sourcetype	Source
Sysmon for Linux EventID 1	Linux	`'sysmon:linux'`	`'Syslog:Linux-Sysmon/Operational'`

Macros Used

Name	Value
security_content_summariesonly	`summariesonly=`summariesonly_config `allow_old_summaries=`oldsummaries_config `fillnull_value=`fillnull_config``
linux_docker_privilege_escalation_filter	``

linux_docker_privilege_escalation_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
T1548.003	Sudo and Sudo Caching	Privilege Escalation

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`N/A`
Earliest Time	`N/A`
Latest Time	`N/A`
Schedule Window	`N/A`
Creates Finding (Notable)	No
Creates Intermediate Finding (Risk Event)	No

Anomaly detections generate Intermediate Findings (Risk Events). They do not generate a Finding (Notable) directly.

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

False positives are present based on automated tooling or system administrative usage. Filter as needed.

Associated Analytic Story

References

https://gtfobins.github.io/gtfobins/docker/

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	Not Applicable	N/A	N/A	N/A
Unit	Not Applicable	N/A	`N/A`	`N/A`
Integration	Not Applicable	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: 11