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splunk_escuAnomaly

Kubernetes Cron Job Creation

The following analytic detects the creation of a Kubernetes cron job, which is a task scheduled to run automatically at specified intervals. It identifies this activity by monitoring Kubernetes Audit logs for the creation events of cron jobs. This behavior is significant for a SOC as it could allow an attacker to execute malicious tasks repeatedly and automatically, posing a threat to the Kubernetes infrastructure. If confirmed malicious, this activity could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information.

MITRE ATT&CK

executionpersistenceprivilege-escalation

Detection Query

`kube_audit` verb=create "objectRef.resource"=cronjobs
  | fillnull
  | stats count values(user.groups{}) as user_groups
    BY kind objectRef.name objectRef.namespace
       objectRef.resource requestObject.kind requestObject.spec.schedule
       requestObject.spec.jobTemplate.spec.template.spec.containers{}.image responseStatus.code sourceIPs{}
       stage user.username userAgent
       verb
  | rename sourceIPs{} as src_ip, user.username as user
  | `kubernetes_cron_job_creation_filter`

Author

Patrick Bareiss, Splunk

Created

2026-03-10

Data Sources

Kubernetes Audit

Tags

Kubernetes Security
Raw Content
name: Kubernetes Cron Job Creation
id: 5984dbe8-572f-47d7-9251-3dff6c3f0c0d
version: 8
date: '2026-03-10'
author: Patrick Bareiss, Splunk
status: production
type: Anomaly
description: The following analytic detects the creation of a Kubernetes cron job, which is a task scheduled to run automatically at specified intervals. It identifies this activity by monitoring Kubernetes Audit logs for the creation events of cron jobs. This behavior is significant for a SOC as it could allow an attacker to execute malicious tasks repeatedly and automatically, posing a threat to the Kubernetes infrastructure. If confirmed malicious, this activity could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information.
data_source:
    - Kubernetes Audit
search: |-
    `kube_audit` verb=create "objectRef.resource"=cronjobs
      | fillnull
      | stats count values(user.groups{}) as user_groups
        BY kind objectRef.name objectRef.namespace
           objectRef.resource requestObject.kind requestObject.spec.schedule
           requestObject.spec.jobTemplate.spec.template.spec.containers{}.image responseStatus.code sourceIPs{}
           stage user.username userAgent
           verb
      | rename sourceIPs{} as src_ip, user.username as user
      | `kubernetes_cron_job_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 ("$user$") starthoursago=168  | 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: $info_min_time$
      latest_offset: $info_max_time$
rba:
    message: Kubernetes cron job creation from user $user$
    risk_objects:
        - field: user
          type: user
          score: 20
    threat_objects:
        - field: src_ip
          type: ip_address
tags:
    analytic_story:
        - Kubernetes Security
    asset_type: Kubernetes
    mitre_attack_id:
        - T1053.007
    product:
        - Splunk Enterprise
        - Splunk Enterprise Security
        - Splunk Cloud
    security_domain: network
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1053.007/kubernetes_audit_cron_job_creation/kubernetes_audit_cron_job_creation.json
          sourcetype: _json
          source: kubernetes