ID | Technique | Tactic |
---|
Detection: Kubernetes AWS detect suspicious kubectl calls
EXPERIMENTAL DETECTION
This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.
Description
The following analytic detects anonymous and unauthenticated requests to a Kubernetes cluster. It identifies this behavior by monitoring API calls from users who have not provided any token or password in their request, using data from kube_audit
logs. This activity is significant for a SOC as it indicates a severe misconfiguration, allowing unfettered access to the cluster with no traceability. If confirmed malicious, an attacker could gain access to sensitive data or control over the cluster, posing a substantial security risk.
Search
1`kube_audit` user.username="system:anonymous" user.groups{} IN ("system:unauthenticated")
2| fillnull
3| stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb
4| rename sourceIPs{} as src_ip, user.username as user
5|`kubernetes_aws_detect_suspicious_kubectl_calls_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
Kubernetes Audit | Kubernetes | '_json' |
'kubernetes' |
N/A |
Macros Used
Name | Value |
---|---|
kube_audit | source="kubernetes" |
kubernetes_aws_detect_suspicious_kubectl_calls_filter | search * |
kubernetes_aws_detect_suspicious_kubectl_calls_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
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 Risk Event | True |
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
Kubectl calls are not malicious by nature. However source IP, verb and Object can reveal potential malicious activity, specially anonymous suspicious IPs and sensitive objects such as configmaps or secrets
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
tbd | 25 | 50 | 50 |
Detection Testing
Test Type | Status | Dataset | Source | Sourcetype |
---|---|---|---|---|
Validation | Not Applicable | N/A | N/A | N/A |
Unit | ❌ Failing | N/A | N/A |
N/A |
Integration | ❌ Failing | N/A | N/A |
N/A |
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: 3