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.

Updated Date: 2024-05-18 ID: 042a3d32-8318-4763-9679-09db2644a8f2 Author: Rod Soto, Patrick Bareiss, Splunk Type: Anomaly Product: Splunk Enterprise Security

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

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic

NistCategory.DE_AE

Cis18Value.CIS_10

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

This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

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

Kubernetes Security

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
tbd	25	50	50

The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

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