Detection: Linux Suspicious Docker Build Command Execution

Description

The following analytic detects docker build being executed on Dockerfiles within the /tmp directory. This is not a typical location for this activity and can indicate an actor adding a container for malicious future actions.

 1
 2| tstats `security_content_summariesonly`
 3  count min(_time) as firstTime max(_time) as lastTime
 4
 5from datamodel=Endpoint.Processes where
 6
 7Processes.process="*docker*"
 8Processes.process="*build*"
 9(
10    (
11        Processes.process="*-f *"
12        Processes.process="* /tmp*"
13    )
14    OR
15    Processes.process_current_directory="/tmp*"
16)
17
18by Processes.process Processes.vendor_product Processes.user_id Processes.process_hash
19   Processes.parent_process_name Processes.parent_process_exec Processes.action Processes.dest
20   Processes.process_current_directory Processes.process_path Processes.process_integrity_level
21   Processes.original_file_name Processes.parent_process Processes.parent_process_path
22   Processes.parent_process_guid Processes.parent_process_id Processes.process_guid
23   Processes.process_id Processes.user Processes.process_name
24
25
26| `drop_dm_object_name(Processes)`
27
28| `security_content_ctime(firstTime)`
29
30| `security_content_ctime(lastTime)`
31
32| `linux_suspicious_docker_build_command_execution_filter`

Data Source

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

Macros Used

Name Value
security_content_ctime convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$)
linux_suspicious_docker_build_command_execution_filter search *
linux_suspicious_docker_build_command_execution_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
T1610 Deploy Container Execution
Installation
DE.AE
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 Finding (Notable) No
Creates Intermediate Finding (Risk Event) Yes
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

Developers or administrators may occasionally use the /tmp directory for testing Docker builds in non-production environments. Filter based on known users or systems where this activity has been approved.

Associated Analytic Story

Intermediate Findings

Message Entity Field Entity Type Risk Score
Suspicious Docker Build Command [$process$] observed on [$dest$] executed by [$user$]. dest system 20

Threat Objects

Field Type
process process
process_name process_name

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Passing N/A N/A N/A
Unit Passing Dataset Syslog:Linux-Sysmon/Operational sysmon:linux
Integration ✅ Passing Dataset Syslog:Linux-Sysmon/Operational sysmon:linux

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: 1