Detection rules › Splunk

Kubernetes Node Port Creation

Status
production
Severity
low
Group by
"objectRef.name", "objectRef.namespace", "objectRef.resource", "requestObject.kind", "requestObject.spec.type", "responseStatus.code", "sourceIPs{}", "user.username", kind, stage, userAgent, verb
Author
Patrick Bareiss, Splunk
Source
github.com/splunk/security_content

The following analytic detects the creation of a Kubernetes NodePort service, which exposes a service to the external network. It identifies this activity by monitoring Kubernetes Audit logs for the creation of NodePort services. This behavior is significant for a SOC as it could allow an attacker to access internal services, posing a threat to the Kubernetes infrastructure's integrity and security. If confirmed malicious, this activity could lead to data breaches, service disruptions, or unauthorized access to sensitive information.

MITRE ATT&CK coverage

TacticTechniques
ExecutionT1204 User Execution

Rule body splunk

name: Kubernetes Node Port Creation
id: d7fc865e-b8a1-4029-a960-cf4403b821b6
version: 10
creation_date: '2023-12-20'
modification_date: '2026-05-13'
author: Patrick Bareiss, Splunk
status: production
type: Anomaly
description: The following analytic detects the creation of a Kubernetes NodePort service, which exposes a service to the external network. It identifies this activity by monitoring Kubernetes Audit logs for the creation of NodePort services. This behavior is significant for a SOC as it could allow an attacker to access internal services, posing a threat to the Kubernetes infrastructure's integrity and security. If confirmed malicious, this activity could lead to data breaches, service disruptions, or unauthorized access to sensitive information.
data_source:
    - Kubernetes Audit
search: |-
    `kube_audit` "objectRef.resource"=services verb=create requestObject.spec.type=NodePort
      | fillnull
      | stats count values(user.groups{}) as user_groups
        BY kind objectRef.name objectRef.namespace
           objectRef.resource requestObject.kind requestObject.spec.type
           responseStatus.code sourceIPs{} stage
           user.username userAgent verb
      | rename sourceIPs{} as src_ip, user.username as user
      | `kubernetes_node_port_creation_filter`
how_to_implement: The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md. When you want to use this detection with AWS EKS, you need to enable EKS control plane logging https://docs.aws.amazon.com/eks/latest/userguide/control-plane-logs.html. Then you can collect the logs from Cloudwatch using the AWS TA https://splunk.github.io/splunk-add-on-for-amazon-web-services/CloudWatchLogs/.
known_false_positives: No false positives have been identified at this time.
references:
    - https://kubernetes.io/docs/tasks/debug/debug-cluster/audit/
drilldown_searches:
    - name: View the detection results for - "$user$"
      search: '%original_detection_search% | search  user = "$user$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$user$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
      earliest_offset: 7d
      latest_offset: "0"
intermediate_findings:
    entities:
        - field: user
          type: user
          score: 20
          message: Kubernetes node port creation from user $user$
threat_objects:
    - field: src_ip
      type: ip_address
analytic_story:
    - Kubernetes Security
asset_type: Kubernetes
mitre_attack_id:
    - T1204
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: cloud
security_domain: network
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/kube_audit_create_node_port_service/kube_audit_create_node_port_service.json
          sourcetype: _json
          source: kubernetes
      test_type: unit

Stages and Predicates

Stage 1: search

`kube_audit` "objectRef.resource"=services verb=create requestObject.spec.type=NodePort

Stage 2: fillnull

| fillnull

Stage 3: stats

| stats count values(user.groups{}) as user_groups
    BY kind objectRef.name objectRef.namespace
       objectRef.resource requestObject.kind requestObject.spec.type
       responseStatus.code sourceIPs{} stage
       user.username userAgent verb

Stage 4: rename

| rename sourceIPs{} as src_ip, user.username as user

Stage 5: search

| `kubernetes_node_port_creation_filter`

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

FieldKindValues
"objectRef.resource"eq
  • services
requestObject.spec.typeeq
  • NodePort
verbeq
  • create