GKE Sensitive RBAC Change Followed by Workload Modification

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GKE Sensitive RBAC Change Followed by Workload Modification

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Detects when the same GKE identity creates or modifies a Role or ClusterRole with high-risk permissions (wildcard access, RBAC escalation verbs, or access to secrets / privileged APIs) and also creates or patches a DaemonSet, Deployment, or CronJob within five minutes. This correlation is consistent with RBAC-based privilege escalation followed by payload deployment.

Rule type: esql

Rule indices: None

Severity: medium

Risk score: 47

Runs every: 5m

Searches indices from: now-11m (Date Math format, see also Additional look-back time)

Maximum alerts per execution: 100

References:

Tags:

  • Domain: Cloud
  • Domain: Kubernetes
  • Data Source: GCP
  • Data Source: Google Cloud Platform
  • Use Case: Threat Detection
  • Tactic: Privilege Escalation
  • Tactic: Persistence
  • Resources: Investigation Guide

Version: 1

Rule authors:

  • Elastic

Rule license: Elastic License v2

Investigation guide

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Triage and analysis

Investigating GKE Sensitive RBAC Change Followed by Workload Modification

This ES|QL rule correlates two successful GKE audit behaviors from the same client.user.email within five minutes:

  1. Role or ClusterRole create/update/patch that grants high-risk permissions (wildcards, escalate / bind / impersonate, secret read, or privileged API resources such as pods/exec and serviceaccounts/token)
  2. DaemonSet, Deployment, or CronJob create or patch after the sensitive RBAC change

Esql.rbac_to_workload_minutes is the gap from the latest sensitive RBAC event to the earliest workload modification in the lookback window.

Possible investigation steps

  • Review Esql.event_action_values and Esql.gcp_audit_resource_name_values for the Role/ClusterRole and workload objects touched.
  • Inspect Esql.user_agent_original_values and Esql.source_ip_values for unexpected clients or networks.
  • Check for RoleBinding or ClusterRoleBinding activity around the same identity and time window.
  • Correlate with secret access, pod exec, or token creation from the same actor.

False positive analysis

  • GitOps pipelines that manage both RBAC manifests and workloads in one sync cycle.
  • Platform bootstrap that patches built-in roles and reconciles addon workloads.

Response and remediation

  • Roll back unauthorized Role/ClusterRole and workload changes, revoke the actor’s credentials, and tighten who can mutate RBAC and sensitive workloads.

Setup

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The GCP Fleet integration with GKE audit logs enabled is required. Request body capture on RBAC resources is required so Role and ClusterRole rule verbs and resources are present in gcp.audit.request.

Rule query

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from logs-gcp.audit-* metadata _id, _index, _version
| where data_stream.dataset == "gcp.audit"
    and service.name == "k8s.io"
    and event.outcome == "success"
    and client.user.email is not null
    and source.ip is not null
    and not to_string(source.ip) in ("127.0.0.1", "::1")
    and not client.user.email in (
      "system:addon-manager",
      "system:apiserver",
      "system:kube-controller-manager"
    )
    and (
      (
        event.action in (
          "io.k8s.authorization.rbac.v1.roles.create",
          "io.k8s.authorization.rbac.v1.roles.update",
          "io.k8s.authorization.rbac.v1.roles.patch",
          "io.k8s.authorization.rbac.v1.clusterroles.create",
          "io.k8s.authorization.rbac.v1.clusterroles.update",
          "io.k8s.authorization.rbac.v1.clusterroles.patch"
        )
        and not (
          client.user.email == "system:serviceaccount:kube-system:clusterrole-aggregation-controller"
          and event.action == "io.k8s.authorization.rbac.v1.clusterroles.patch"
        )
        and (
          KQL("""gcp.audit.request.rules.verbs:("*" or "escalate" or "bind" or "impersonate")""")
          or KQL("""gcp.audit.request.rules.verbs:("*" or "create" or "patch" or "update") and gcp.audit.request.rules.resources:("*" or "clusterroles" or "clusterrolebindings" or "roles" or "rolebindings" or "pods/exec" or "serviceaccounts/token" or "nodes/proxy" or "daemonsets")""")
          or KQL("""gcp.audit.request.rules.verbs:("*" or "get" or "list") and gcp.audit.request.rules.resources:("*" or "secrets")""")
        )
      )
      or event.action in (
        "io.k8s.apps.v1.daemonsets.create",
        "io.k8s.apps.v1.daemonsets.patch",
        "io.k8s.apps.v1.deployments.create",
        "io.k8s.apps.v1.deployments.patch",
        "io.k8s.batch.v1.cronjobs.create",
        "io.k8s.batch.v1.cronjobs.patch"
      )
    )
| eval Esql.is_sensitive_rbac = case(event.action like "io.k8s.authorization.rbac.*", 1, 0),
    Esql.is_workload_modification = case(event.action like "io.k8s.apps.*" or event.action like "io.k8s.batch.*", 1, 0),
    Esql.sensitive_rbac_timestamp = case(event.action like "io.k8s.authorization.rbac.*", @timestamp, null),
    Esql.workload_modification_timestamp = case(event.action like "io.k8s.apps.*" or event.action like "io.k8s.batch.*", @timestamp, null)
| stats
    Esql.sensitive_rbac_count = sum(Esql.is_sensitive_rbac),
    Esql.workload_modification_count = sum(Esql.is_workload_modification),
    Esql.latest_sensitive_rbac_timestamp = max(Esql.sensitive_rbac_timestamp),
    Esql.earliest_workload_modification_timestamp = min(Esql.workload_modification_timestamp),
    Esql.event_action_values = values(event.action),
    Esql.gcp_audit_resource_name_values = values(gcp.audit.resource_name),
    Esql.user_agent_original_values = values(user_agent.original),
    Esql.source_ip_values = values(source.ip)
  by client.user.email
| where Esql.sensitive_rbac_count > 0
    and Esql.workload_modification_count > 0
    and Esql.latest_sensitive_rbac_timestamp <= Esql.earliest_workload_modification_timestamp
| eval Esql.rbac_to_workload_minutes = date_diff(
    "minute",
    Esql.latest_sensitive_rbac_timestamp,
    Esql.earliest_workload_modification_timestamp
  )
| where Esql.rbac_to_workload_minutes <= 5
| keep
    client.user.email,
    Esql.*

Framework: MITRE ATT&CKTM