ID | Technique | Tactic |
---|---|---|
T1552.007 | Container API | Credential Access |
Detection: Kubernetes Abuse of Secret by Unusual User Name
Description
The following analytic detects unauthorized access or misuse of Kubernetes Secrets by unusual user names. It leverages Kubernetes Audit logs to identify anomalies in access patterns by analyzing the source of requests based on user names. This activity is significant for a SOC as Kubernetes Secrets store sensitive information like passwords, OAuth tokens, and SSH keys, making them critical assets. If confirmed malicious, this activity could lead to unauthorized access to sensitive systems or data, potentially resulting in significant security breaches and exfiltration of sensitive information.
Search
1`kube_audit` objectRef.resource=secrets verb=get
2| search NOT `kube_allowed_user_names`
3| fillnull
4| stats count by objectRef.name objectRef.namespace objectRef.resource requestReceivedTimestamp requestURI responseStatus.code sourceIPs{} stage user.groups{} user.uid user.username userAgent verb
5| rename sourceIPs{} as src_ip, user.username as user
6| `kubernetes_abuse_of_secret_by_unusual_user_name_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
Kubernetes Audit | Kubernetes | '_json' |
'kubernetes' |
N/A |
Macros Used
Name | Value |
---|---|
kube_allowed_user_names | user.username=admin |
kubernetes_abuse_of_secret_by_unusual_user_name_filter | search * |
kubernetes_abuse_of_secret_by_unusual_user_name_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
unknown
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
Access of Kubernetes secret $objectRef.name$ from unusual user name $user$ | 49 | 70 | 70 |
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: 2