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splunk_escuAnomaly

Kubernetes DaemonSet Deployed

The following analytic detects the creation of a DaemonSet in a Kubernetes cluster. This behavior is identified by monitoring Kubernetes Audit logs for the creation event of a DaemonSet. DaemonSets ensure a specific pod runs on every node, making them a potential vector for persistent access. This activity is significant for a SOC as it could indicate an attempt to maintain persistent access to the Kubernetes infrastructure. If confirmed malicious, it could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information.

MITRE ATT&CK

Detection Query

`kube_audit` "objectRef.resource"=daemonsets verb=create
  | 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
  | rename sourceIPs{} as src_ip, user.username as user
  | `kubernetes_daemonset_deployed_filter`

Author

Patrick Bareiss, Splunk

Created

2026-03-10

Data Sources

Kubernetes Audit

Tags

Kubernetes Security
Raw Content
name: Kubernetes DaemonSet Deployed
id: bf39c3a3-b191-4d42-8738-9d9797bd0c3a
version: 8
date: '2026-03-10'
author: Patrick Bareiss, Splunk
status: production
type: Anomaly
description: The following analytic detects the creation of a DaemonSet in a Kubernetes cluster. This behavior is identified by monitoring Kubernetes Audit logs for the creation event of a DaemonSet. DaemonSets ensure a specific pod runs on every node, making them a potential vector for persistent access. This activity is significant for a SOC as it could indicate an attempt to maintain persistent access to the Kubernetes infrastructure. If confirmed malicious, it could lead to persistent attacks, service disruptions, or unauthorized access to sensitive information.
data_source:
    - Kubernetes Audit
search: |-
    `kube_audit` "objectRef.resource"=daemonsets verb=create
      | 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
      | rename sourceIPs{} as src_ip, user.username as user
      | `kubernetes_daemonset_deployed_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: DaemonSet deployed to Kubernetes by 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:
        - T1204
    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/T1204/kubernetes_audit_daemonset_created/kubernetes_audit_daemonset_created.json
          sourcetype: _json
          source: kubernetes