Detection rules › Splunk
Kubernetes Pod Created in Default Namespace
The following analytic detects the creation of Kubernetes pods in the default, kube-system, or kube-public namespaces. It leverages Kubernetes audit logs to identify pod creation events within these specific namespaces. This activity is significant for a SOC as it may indicate an attacker attempting to hide their presence or evade defenses. Unauthorized pod creation in these namespaces can suggest a successful cluster breach, potentially leading to privilege escalation, persistent access, or further malicious activities within the cluster.
MITRE ATT&CK coverage
| Tactic | Techniques |
|---|---|
| Execution | T1204 User Execution |
Rules detecting the same action
Other rules on this platform that filter on the same API call or operation.
- Container With A hostPath Mount Created (Sigma)
- Creation Of Pod In System Namespace (Sigma)
- Kubernetes Anonymous User Create/Update/Patch Pods Request (Elastic)
- Kubernetes Container Created with Excessive Linux Capabilities (Elastic)
- Kubernetes Create or Update Privileged Pod (Splunk)
- Kubernetes Pod Created with a Sensitive hostPath Volume (Elastic)
- Kubernetes Pod Created With HostIPC (Elastic)
- Kubernetes Pod Created With HostNetwork (Elastic)
Rule body splunk
name: Kubernetes Pod Created in Default Namespace
id: 3d6b1a81-367b-42d5-a925-6ef90b6b9f1e
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 Kubernetes pods in the default, kube-system, or kube-public namespaces. It leverages Kubernetes audit logs to identify pod creation events within these specific namespaces. This activity is significant for a SOC as it may indicate an attacker attempting to hide their presence or evade defenses. Unauthorized pod creation in these namespaces can suggest a successful cluster breach, potentially leading to privilege escalation, persistent access, or further malicious activities within the cluster.
data_source:
- Kubernetes Audit
search: |-
`kube_audit` objectRef.resource=pods verb=create objectRef.namespace IN ("default", "kube-system", "kube-public")
| fillnull
| stats count
BY objectRef.name objectRef.namespace objectRef.resource
requestReceivedTimestamp requestURI responseStatus.code
sourceIPs{} stage user.groups{}
user.uid user.username userAgent
verb
| rename sourceIPs{} as src_ip, user.username as user
| `kubernetes_pod_created_in_default_namespace_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 Pod Created in Default Namespace by $user$
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
`kube_audit` objectRef.resource=pods verb=create objectRef.namespace IN ("default", "kube-system", "kube-public")
Stage 2: fillnull
| fillnull
Stage 3: stats
| stats count
BY objectRef.name objectRef.namespace objectRef.resource
requestReceivedTimestamp requestURI responseStatus.code
sourceIPs{} stage user.groups{}
user.uid user.username userAgent
verb
Stage 4: rename
| rename sourceIPs{} as src_ip, user.username as user
Stage 5: search
| `kubernetes_pod_created_in_default_namespace_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.
| Field | Kind | Values |
|---|---|---|
objectRef.namespace | in |
|
objectRef.resource | eq |
|
verb | eq |
|