Detection: Kubernetes Abuse of Secret by Unusual User Name

Updated Date: 2024-09-30 ID: df6e9cae-5257-4a34-8f3a-df49fa0f5c46 Author: Patrick Bareiss, Splunk Type: Anomaly Product: Splunk Enterprise Security

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.

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
Kubernetes Audit Kubernetes icon Kubernetes '_json' 'kubernetes'

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

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1552.007 Container API Credential Access
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.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 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

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

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: 3