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Investigate a suspected GPU-cluster breach as an identity, Kubernetes control-plane, node, workload, scheduler, GPU, registry, CI/CD, and cloud-account incident—not as a GPU-health problem. Preserve logs before changing anything, correlate Kubernetes API audit events with every authentication source, contain affected namespaces and nodes, revoke or rotate credentials, rebuild hosts whose integrity cannot be proven, and restore only from verified images and configuration.
What unauthorized access looks like in a GPU cluster
Unauthorized access can be obvious, such as an unknown administrator creating a privileged pod, or subtle, such as a valid service-account token being used from an unfamiliar address. A GPU job that consumes more memory than expected is not, by itself, evidence of intrusion. Hardware faults, driver failures, scheduler bugs, and legitimate workload spikes can produce similar symptoms.
Use several evidence classes together:
- Identity: unexpected users, groups, service accounts, certificates, tokens, cloud roles, SSH keys, or CI/CD identities.
- Authorization: new RoleBindings, ClusterRoleBindings, ClusterRoles, admission-policy changes, or requests using dangerous permissions.
- Control plane: unusual Kubernetes API verbs, resources, namespaces, source addresses, user agents, or request times.
- Node and runtime: unknown processes, images, mounts, startup tasks, container-runtime events, or changes to GPU drivers and operator components.
- Workload and scheduler: pods, jobs, queue entries, or GPU allocations that do not match an approved owner or project.
- Cloud and supply chain: suspicious IAM activity, registry pulls, image changes, build-pipeline use, or access to etcd and control-plane hosts.
An incident is more credible when independent records agree—for example, an unfamiliar identity performs a RoleBinding change, launches a host-network pod on a GPU node, and then a new process makes outbound connections from that node.
Set the investigation scope before touching the cluster
Write down the suspected first and last times, affected clusters, cloud accounts, regions, namespaces, node pools, GPU types, and known-good administrators. Preserve the original time zone and synchronize all later records to UTC. Record who is leading the response and who can authorize isolation, credential revocation, and rebuilds.
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Start with read-only collection. Do not delete pods, bindings, images, accounts, or files until their state and metadata have been exported. A concise initial inventory should include:
- API-server, audit, identity-provider, cloud, bastion, SSH, registry, CI/CD, scheduler, node, and container-runtime logs.
- Current and recently deleted workload metadata, including pod specifications, owners, images, digests, service accounts, volumes, security contexts, and node assignments.
- RoleBindings, ClusterRoleBindings, Roles, ClusterRoles, service accounts, admission configuration, network policies, and relevant custom resources.
- Node images, kernel and driver versions, GPU-operator configuration, mounted filesystems, running processes, open network connections, and startup configuration.
Export copies to protected external storage with restricted write access. Keep a written timeline for every command, decision, credential change, isolation step, and restoration action.
Use Kubernetes audit records as the control-plane timeline
Kubernetes describes its audit logger as a beta feature that records actions taken by the API for later analysis after a compromise. Enable auditing in every production cluster and archive audit files on a secure server rather than relying only on a local node. If auditing was not enabled, state that limitation explicitly and compensate with identity, admission, scheduler, runtime, and cloud records.
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- Identity: username, groups, and authentication mechanism, including service-account names.
- Origin: source address, forwarded address where trusted, user agent, and the API server that handled the request.
- Action: verb, API group, resource, subresource, namespace, object name, response status, and request time.
- Privilege indicators: requests for
secrets,nodes/proxy,exec,attach,portforward, or permissions involvingescalateandbind. - Sequence: authentication, permission changes, workload creation, secret reads, node interaction, and cleanup attempts.
Look for a successful request followed by denied requests, because an intruder may probe permissions before finding a usable path. Compare the identity and source address with normal activity for that person, service, or automation account. An unfamiliar user agent is a lead, not proof; automation upgrades and administrative tools can change it legitimately.
Audit policy detail must balance evidence and exposure. High-detail request logging can capture Secret contents. NSA and CISA guidance recommends external append-only logging while reducing detail for Secret requests so the audit system does not become another copy of sensitive data.
Useful read-only checks, run by an authorized administrator and adapted to the cluster version, include:
kubectl get events --all-namespaces --sort-by=.lastTimestamp
kubectl get pods --all-namespaces -o wide
kubectl get rolebindings,clusterrolebindings --all-namespaces
kubectl get serviceaccounts --all-namespacesThese commands are inventory aids, not substitutes for the API audit stream. Kubernetes Events are short-lived and incomplete compared with durable audit records.
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Check every authentication source, not just Kubernetes
Kubernetes has no built-in user database. A cluster may accept identities from OIDC, LDAP, client certificates, static tokens, service-account tokens, cloud IAM integration, bastions, SSH, and CI/CD systems. An audit username is meaningful only when matched to the system that issued or enabled it.
Identity provider and certificate records
Review sign-ins, MFA outcomes, token issuance and revocation, group changes, refresh-token use, certificate requests, and changes to identity-provider applications. Compare source addresses, devices, and times with the API audit record. A successful login from a normal account can still be unauthorized if the token or certificate was stolen.
Service accounts and workload credentials
List service accounts used by affected pods and identify their projected or mounted tokens. Determine which controllers, jobs, and external systems can request those tokens. Disable or rotate credentials that may have been read, even when there is no proof they were used; Secret readability is itself a reason to rotate the affected Secret.
Cloud, bastion, SSH, and pipeline access
Correlate cloud audit trails, instance or node-login records, bastion sessions, SSH-key use, registry authentication, and CI/CD job logs. A compromised build runner can create a legitimate-looking image or obtain a deploy token without ever appearing as a human user in Kubernetes.
Hunt for privilege escalation and persistence
Inspect both current objects and their historical versions from configuration-management or Git systems. Pay special attention to:
- New or modified RoleBindings and ClusterRoleBindings, especially bindings to cluster-admin-like roles.
- ClusterRoles containing
escalateorbind. Kubernetes warns that these permissions can bypass normal privilege protections. - Requests for
nodes/proxy. Access to this subresource can expose privileged kubelet APIs. - Use of
exec,attach, orportforwardinto sensitive pods or nodes. - New service accounts, admission-webhook changes, validating or mutating policy changes, and scheduler or controller configuration.
- Secrets, ConfigMaps, image-pull credentials, webhook credentials, and cloud-identity bindings created or read outside the approved change process.
Check for persistence in DaemonSets, Deployments, StatefulSets, Jobs, CronJobs, init containers, sidecars, admission webhooks, custom resources, and node startup files. Compare live manifests with the last approved revision. Preserve the unauthorized object in exported YAML and capture its owner references, labels, annotations, image digest, and timestamps before removal.
Inspect workloads, nodes, and the container runtime
Workload indicators
Flag pods that use unapproved registries or mutable tags, privileged mode, host networking, host PID or IPC, hostPath mounts, broad Linux capabilities, unusual device access, or service accounts with more permissions than the application requires. Examine outbound destinations, listening ports, downloaded binaries, and changes to mounted configuration. A pod that requests a GPU is not suspicious by itself; the owner, image, command, and allocation must match an approved job.
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Node indicators
On affected nodes, preserve process lists, loaded kernel modules, network connections, scheduled tasks, shell history where policy permits, systemd units, container-runtime state, kubelet logs, and file-integrity evidence. Check for processes outside the container runtime, unexpected privileged containers, modified GPU drivers, and binaries or scripts appearing shortly before the suspected access.
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Runtime records such as container creation, start, stop, image pull, and exec events help distinguish a workload-level compromise from a host compromise. Node evidence must be collected before cordoning, draining, rebooting, or reimaging, because those actions can destroy volatile state.
GPU evidence
Capture vendor telemetry, device-plugin logs, GPU allocation records, process-to-device mappings, memory and utilization history, temperature and error data, and scheduler job identifiers. Tools such as nvidia-smi can show current processes and device state, but a GPU-health tool cannot determine who was authorized to run the process. Correlate telemetry with pod UID, service account, queue record, project, and approved workload manifest.
Correlate Kubernetes allocation with scheduler and accounting records
Kubernetes exposes vendor devices through resources such as nvidia.com/gpu. Compare each allocation with the pod specification, node, namespace, user or service account, scheduler event, queue or project account, and billing or quota record. Investigate allocations with no corresponding job, jobs running outside approved windows, unexplained changes in GPU count, and pods whose image or command differs from the scheduler submission.
In a Slurm-based environment, use controller, accounting-database, node-daemon, job-completion, and prolog or epilog records in the same way. A scheduler entry proves that a scheduler accepted a request; it does not prove that the submitting identity was legitimate. Match scheduler authentication and MFA records to the control-plane or node evidence.
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Preserve evidence safely
Collect audit, identity, cloud, node, runtime, scheduler, registry, and CI/CD logs into protected external storage. Use append-only or write-once controls where available, restrict who can read the collection, and record hashes and collection times. Keep the original files and work from copies.
Preserve both successful and failed authentication and authorization events. Capture clock configuration and time offsets for each system. Export object manifests, ownership metadata, image digests, admission configuration, network policies, and relevant etcd or control-plane access logs. Kubernetes states that write access to the etcd backend is equivalent to root on the entire cluster, so investigate direct etcd access as a top-priority control-plane event.
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Do not rotate or delete credentials before collecting enough evidence to identify their use, unless an active threat requires immediate revocation. When emergency revocation is necessary, record the exact time, actor, affected credential, and reason.
Contain the incident in a controlled sequence
- Protect evidence and establish authority. Start the incident timeline, export logs, snapshot relevant metadata, and assign an incident lead. Preserve the suspected node and workload state long enough to collect volatile evidence unless active harm makes immediate isolation necessary.
- Revoke suspected access. Disable compromised human accounts and service accounts. Revoke or rotate tokens, client certificates, cloud keys, registry credentials, SSH keys, webhook secrets, and CI/CD secrets that may have been exposed. Prefer short credential lifetimes and frequent rotation.
- Stop active abuse. Suspend suspicious jobs and workloads after exporting their manifests and runtime evidence. Remove network paths to command-and-control or data-exfiltration destinations using approved firewall, network-policy, security-group, or egress controls.
- Isolate namespaces and nodes. Apply restrictive network policies, quarantine affected namespaces, cordon nodes whose integrity is uncertain, and prevent new scheduling onto them. Drain only when your evidence plan accounts for the loss of volatile state; use an isolated replacement capacity for critical services.
- Protect the control plane and etcd. Restrict API-server and etcd access to the smallest administrative group, investigate direct etcd connections, and verify that control-plane credentials and backups were not altered. Treat unexplained write access to etcd as a cluster-root incident.
- Remove persistence after collection. Delete unauthorized bindings, accounts, workloads, admission changes, images, startup tasks, and node-level persistence only after preserving evidence. Record object names, hashes, owners, and deletion times.
- Reassess scope. Search every cluster, cloud account, registry, build project, and node pool that shared the affected identity, image, network segment, or secret. Assume a credential reused elsewhere is exposed until proven otherwise.
Rotate credentials without creating a second outage
Create a rotation map listing each credential, owner, consumers, storage location, last rotation, and replacement procedure. Rotate in dependency order: identity-provider and cloud administration credentials first, then cluster-admin and automation identities, registry and CI/CD credentials, service-account tokens, application Secrets, SSH keys, and certificates. Update consumers, verify successful authentication with the replacement, and revoke the old credential only after the cutover is confirmed.
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Rotate any Secret that an attacker could have read, even when audit detail is insufficient to prove exfiltration. Review token lifetimes and eliminate static tokens where the platform supports short-lived projected credentials. Remove unused service accounts and restrict token creation and impersonation permissions.
When to rebuild a GPU node
Reimage or replace a node when you cannot prove host integrity, when privileged access or kernel-level persistence is suspected, when drivers or container-runtime components were modified, or when logs are incomplete after an intrusion. Cleaning individual files is not equivalent to establishing trust in the operating system.
Build the replacement from a trusted, version-pinned image. Reinstall the kernel, container runtime, GPU drivers, device plugins, GPU Operator components, monitoring agents, and workload dependencies from approved sources. Validate package and image hashes, configuration, admission controls, node labels, taints, network policy, and time synchronization before scheduling workloads.
Return the node to service in stages: first a non-sensitive test workload, then a controlled GPU job, then normal capacity. Compare runtime, GPU, network, and scheduler telemetry with the known-good baseline. Do not restore a node merely because its GPU reports healthy.
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Restore from known-good manifests, images identified by immutable digests, and configuration versions that predate the compromise. Recreate Secrets from rotated values rather than restoring potentially exposed Secret data. Validate service accounts, RBAC, admission policy, network policy, image provenance, and resource limits before exposing the workload to users.
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For an initial heightened-monitoring period, alert on unusual API verbs and source addresses, new privileged or host-network pods, RoleBinding and ClusterRoleBinding changes, Secret reads, nodes/proxy access, unexpected GPU allocations, new registry origins, node-driver changes, and cloud or CI/CD activity outside approved windows. Set retention and alert thresholds according to risk, contractual obligations, and applicable compliance requirements; there is no universal retention period or detection threshold that fits every cluster.
Complete a root-cause report that identifies the initial access path, affected identities and assets, evidence gaps, containment decisions, recovery validation, and corrective actions. NVIDIA’s AI-cloud requirements describe detection, containment, investigation, remediation, evidence preservation, and corrective-action tracking as parts of a defined incident-response capability.
GPU-specific security boundaries
GPU Operator driver containers and toolkit components may require privileged: true, host PID or IPC access, host filesystem and hardware access, service restarts, and kernel-module loading. Those capabilities create a substantial trust boundary. Restrict access to the operator namespace to cluster administrators, as the GPU Operator documentation states that only the Kubernetes cluster administrator needs to access or manage it.
Separate sensitive GPU workloads from control-plane services with dedicated node pools, taints and tolerations, restrictive Pod Security admission, and network segmentation. Kubernetes describes node isolation as a way to prevent a container breakout from pivoting into more sensitive applications. Minimize hostPath, host-network, host-PID, and privileged use outside the operator and other explicitly approved system components.
Hardware-remediation systems can be useful for cordoning, draining, or repairing faulty nodes, but they are not identity-monitoring systems. NVSentinel, for example, is documented for GPU and system-fault remediation; it should complement, not replace, API-audit, identity, authorization, and host investigations.
Kubernetes and Slurm: evidence to compare
The response process differs by platform, but the investigation questions are the same. Use these axes when designing controls or comparing environments:
| Axis | Kubernetes-based GPU environment | Slurm-based GPU environment |
|---|---|---|
| Identity and MFA | OIDC or LDAP identity, client certificates, service accounts, cloud IAM, and automation identities. | Login, federation, SSH, certificates, service accounts, and scheduler or portal identities. |
| Control-plane audit | API-server audit events, admission records, and etcd-access logs. | Controller, authentication, allocation, accounting, and management-node logs. |
| Scheduler visibility | Pod, job, queue, event, quota, and device-plugin allocation records. | Submission, allocation, accounting, node-daemon, completion, and prolog or epilog records. |
| Node isolation | Namespaces, Pod Security admission, taints, tolerations, network policy, and dedicated pools. | Partitions, reservations, node features, cgroups, firewall boundaries, and dedicated nodes. |
| Credential rotation | Tokens, certificates, Secrets, cloud roles, registry and CI/CD credentials. | SSH, certificates, scheduler credentials, cloud roles, registry and CI/CD credentials. |
| GPU privilege boundary | Device plugins, GPU Operator components, driver containers, and privileged system pods. | Host drivers, device files, cgroups, node daemons, and job-launch mechanisms. |
| Recovery measure | Rebuild nodes, reinstall drivers and operators, then restore immutable images and manifests. | Reimage nodes, reinstall drivers and daemons, validate partitions and accounting, then restore approved jobs. |
In either platform, external durable logging, image and package verification, network segmentation, short-lived credentials, and a tested rebuild process reduce the time between detection and a trustworthy recovery.
Quick Recap
Common mistakes and their fixes
- Blaming a GPU fault on an attacker: require identity, authorization, and host evidence before calling it an access incident.
- Deleting suspicious pods immediately: export manifests, logs, runtime metadata, and volatile node evidence first unless active harm demands immediate termination.
- Reviewing only Kubernetes audit logs: correlate identity-provider, cloud, bastion, SSH, registry, CI/CD, scheduler, node, and runtime records.
- Rotating one token and stopping: map reuse and delegation, then rotate every credential that could have been exposed.
- Cleaning a host in place: rebuild when integrity cannot be established; a healthy GPU does not prove a trustworthy operating system.
- Giving broad access to the operator namespace: restrict it to cluster administrators and isolate GPU system components from tenant workloads.
- Logging every Secret request at maximum detail: reduce Secret-request detail so audit logs do not store Secret contents while retaining enough metadata for investigation.
- Restoring mutable image tags: use approved immutable digests, verify hashes, and compare configuration with a known-good revision.
Operational checklist
- Define the time window, affected assets, incident lead, and trusted administrators.
- Export audit, identity, cloud, scheduler, registry, CI/CD, node, runtime, and GPU evidence to protected storage.
- Inventory identities, RBAC, admission policies, workloads, images, Secrets, nodes, and direct etcd access.
- Correlate API actions with authentication records and approved GPU jobs.
- Revoke or rotate exposed accounts, tokens, certificates, keys, and Secrets.
- Isolate namespaces, workloads, network paths, and nodes while preserving evidence.
- Remove persistence after collection and search connected clusters, accounts, registries, and pipelines.
- Rebuild untrusted nodes from pinned, verified sources.
- Restore from known-good manifests, images, and rotated configuration.
- Validate recovery, increase monitoring, document root cause, and track corrective actions to completion.
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