Kubernetes Unusual Decision by User Agent

This detection rule identifies unusual request responses within Kubernetes environments by analyzing audit logs for anomalies in user agents and response annotations. The rule leverages the "new_terms" rule type, focusing on deviations from expected API request patterns typically made by system components or trusted users. By monitoring discrepancies in the user_agent.original, kubernetes.audit.user.username, and kubernetes.audit.annotations.authorization_k8s_io/decision fields, the rule aims to uncover potential unauthorized access attempts, misconfigured permissions, or malicious activities exploiting atypical user agent strings. This approach enhances the security posture of Kubernetes clusters by detecting subtle indicators of compromise that might otherwise go unnoticed. The rule is intended to be deployed in production environments where consistent user agent behavior is expected from trusted sources.

Attack Chain

1. Initial Access: An attacker gains initial access to the Kubernetes cluster through compromised credentials or a vulnerable application.
2. Privilege Escalation: The attacker attempts to escalate privileges within the cluster, potentially using a service account with excessive permissions.
3. Discovery: The attacker performs reconnaissance activities to map out the cluster's resources, including pods, services, and secrets. They use kubectl or similar tools to query the API server.
4. Lateral Movement: Using the compromised credentials, the attacker moves laterally within the cluster, accessing different namespaces or nodes.
5. Execution: The attacker executes malicious code within a pod or container, potentially deploying a reverse shell or a cryptominer. This is achieved by sending API requests to create or modify resources.
6. Persistence: The attacker establishes persistence by creating a new service account with elevated privileges or by modifying existing deployments to include backdoors.
7. Exfiltration (Potential): The attacker attempts to exfiltrate sensitive data from the cluster, such as secrets or configuration files.
8. Impact: The attacker achieves their objectives, which could include data theft, denial of service, or complete control of the Kubernetes cluster.

Impact

A successful attack could lead to unauthorized access to sensitive data, disruption of services, or complete compromise of the Kubernetes cluster. While the rule itself has a low severity, the underlying activities it detects can have severe consequences. The number of potential victims depends on the scope and criticality of the affected Kubernetes deployments. If exploited, attackers can gain control of containerized applications and infrastructure, leading to data breaches, financial losses, and reputational damage.

Recommendation

• Deploy the provided Sigma rule to your SIEM to detect unusual Kubernetes API requests based on user agent (see the rule section).
• Regularly review Kubernetes audit logs for unusual user_agent.original values, and correlate them with other security events.
• Implement strict role-based access control (RBAC) policies to minimize the attack surface.
• Configure monitoring and alerting for suspicious activity within the Kubernetes cluster.
• Tune the Sigma rule exceptions to account for legitimate but infrequent user agents (see the rule's false positive analysis section).