GitHub Repo

Detection: Kubernetes Scanning by Unauthenticated IP Address

Updated Date: 2026-05-13 ID: f9cadf4e-df22-4f4e-a08f-9d3344c2165d Author: Patrick Bareiss, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic identifies potential scanning activities within a Kubernetes environment by unauthenticated IP addresses. It leverages Kubernetes audit logs to detect multiple unauthorized access attempts (HTTP 403 responses) from the same source IP. This activity is significant as it may indicate an attacker probing for vulnerabilities or attempting to exploit known issues. If confirmed malicious, such scanning could lead to unauthorized access, data breaches, or further exploitation of the Kubernetes infrastructure, compromising the security and integrity of the environment.

 1`kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403
 2  
 3| iplocation sourceIPs{}
 4  
 5| stats count values(userAgent) as userAgent values(user.username) as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI) as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message) as responseStatus.message values(responseStatus.reason) as responseStatus.reason values(responseStatus.status) as responseStatus.status
 6    BY sourceIPs{} Country City
 7  
 8| where count > 5
 9  
10| rename sourceIPs{} as src_ip, user.username as user
11  
12| `kubernetes_scanning_by_unauthenticated_ip_address_filter`

Data Source

Name Platform Sourcetype Source
Kubernetes Audit Kubernetes icon Kubernetes '_json' 'kubernetes'

Macros Used

Name Value
kube_audit source="kubernetes"
kubernetes_scanning_by_unauthenticated_ip_address_filter search *
kubernetes_scanning_by_unauthenticated_ip_address_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1046 Network Service Discovery Discovery
Exploitation
DE.AE
CIS 13

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Finding (Notable) No
Creates Intermediate Finding (Risk Event) Yes
Anomaly detections generate Intermediate Findings (Risk Events). They do not generate a Finding (Notable) directly.

Implementation

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.

Associated Analytic Story

Intermediate Findings

Message Entity Field Entity Type Risk Score
Kubernetes scanning from ip $src_ip$ user user 20

Threat Objects

Field Type
src_ip ip_address

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation ✅ Passing N/A N/A N/A
Unit ✅ Passing Dataset kubernetes _json
Integration ✅ Passing Dataset kubernetes _json

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

Source: GitHub | Version: 10