Detection rules › Splunk

Kubernetes Create or Update Privileged Pod

Status
production
Severity
low
Group by
"objectRef.name", "objectRef.namespace", "objectRef.resource", "requestObject.kind", "requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration", "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 or update of privileged pods in Kubernetes. It identifies this activity by monitoring Kubernetes Audit logs for pod configurations that include root privileges. This behavior is significant for a SOC as it could indicate an attempt to escalate privileges, exploit the kernel, and gain full access to the host's namespace and devices. If confirmed malicious, this activity could lead to unauthorized access to sensitive information, data breaches, and service disruptions, posing a severe threat to the environment.

MITRE ATT&CK coverage

TacticTechniques
ExecutionT1204 User Execution

Rules detecting the same action

Other rules on this platform that filter on the same API call or operation.

Rule body splunk

name: Kubernetes Create or Update Privileged Pod
id: 3c6bd734-334d-4818-ae7c-5234313fc5da
version: 10
creation_date: '2024-01-30'
modification_date: '2026-05-13'
author: Patrick Bareiss, Splunk
status: production
type: Anomaly
description: The following analytic detects the creation or update of privileged pods in Kubernetes. It identifies this activity by monitoring Kubernetes Audit logs for pod configurations that include root privileges. This behavior is significant for a SOC as it could indicate an attempt to escalate privileges, exploit the kernel, and gain full access to the host's namespace and devices. If confirmed malicious, this activity could lead to unauthorized access to sensitive information, data breaches, and service disruptions, posing a severe threat to the environment.
data_source:
    - Kubernetes Audit
search: |-
    `kube_audit` objectRef.resource=pods verb=create OR verb=update requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration=*\"privileged\":true*
      | fillnull
      | stats count values(user.groups{}) as user_groups
        BY kind objectRef.name objectRef.namespace
           objectRef.resource requestObject.kind responseStatus.code
           sourceIPs{} stage user.username
           userAgent verb requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration
      | rename sourceIPs{} as src_ip, user.username as user
      | `kubernetes_create_or_update_privileged_pod_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 privileged pod created by 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/kubernetes_privileged_pod/kubernetes_privileged_pod.json
          sourcetype: _json
          source: kubernetes
      test_type: unit

Stages and Predicates

Stage 1: search

search (verb="create" OR verb="update") objectRef.resource="pods" requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration="*\\\"privileged\\\":true*" source="kubernetes"

Stage 2: fillnull

fillnull

Stage 3: stats

statsAS user_groups BY kind, "objectRef.name", "objectRef.namespace", "objectRef.resource", "requestObject.kind", "responseStatus.code", "sourceIPs{}", stage, "user.username", userAgent, verb, "requestObject.metadata.annotations.kubectl.kubernetes.io/last-applied-configuration"

Stage 4: rename

rename

Stage 5: search

search

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.