Vulnerability Management for GPU and AI Infrastructure

GPU vulnerability management is a lifecycle for the whole AI computing path, not a search for a component whose name contains GPU. A weakness in a driver, kernel module, container runtime, orchestration layer, model server, or cloud control plane can have very different prerequisites and owners. Start by identifying the layer, then prove that the affected version is deployed, reachable, and connected to a meaningful impact path. Treat the result as an evidence-backed vulnerability decision, not as a severity label copied from an advisory.

Start with the layer, not the label

A practical inventory separates the stack into hardware and firmware where relevant; host operating system and kernel; GPU driver; runtime or toolkit; libraries; container runtime; GPU container integration; Kubernetes and its device plugin or operator; inference and training software; management planes; cloud-managed GPU services; and the AI application itself. A vulnerability in a user-space library does not automatically mean the host driver is affected. Conversely, a kernel or management-plane defect can matter even when the model code is unchanged.

Keep driver, runtime, and framework versions as separate fields. CUDA, ROCm, a driver branch, a container image tag, and a framework release are not interchangeable identifiers. Record the exact package or image digest, host installation, and vendor advisory that establishes each version. The AI vulnerability intelligence triage guide explains why asset identity, configuration, reachability, and evidence must be tested separately.

AI / GPU VULNERABILITY STACK MAP

Cloud or management plane ↓ Orchestrator and scheduler ↓ Container runtime ↓ GPU operator or device integration ↓ Host OS and kernel ↓ GPU driver and firmware ↓ Runtime, toolkit, and libraries ↓ Inference or training framework ↓ AI workload

Layer	Primary owner	Version evidence	Typical exposure	Remediation mechanism
Cloud or management plane	Cloud/platform team or provider	Provider advisory, API version, account configuration	Public control APIs, operator roles, tenant administration	Provider fix, policy change, credential rotation, or service migration
Orchestrator	Platform team	Cluster version, node image, admission and scheduler configuration	API server, kubelet, workload placement, privileged operators	Upgrade, node replacement, policy tightening, or isolation
Container runtime	Platform team	Runtime package and image build records	Local workload boundary and host interaction	Runtime/host patch, rebuild, and configuration review
GPU operator/device plugin	Platform or ML platform	Operator image digest, plugin version, node configuration	Device discovery, mounts, sockets, and privileged setup	Operator upgrade, node drain, and controlled rollout
Host OS/kernel	Infrastructure team	OS image, kernel build, patch level	System calls, modules, devices, local privilege	Patch, reboot, node replacement, or isolation
Driver/firmware	Hardware or infrastructure owner	Vendor driver branch, firmware inventory	GPU device access and host integration	Vendor package, firmware update, or workload relocation
Runtime/libraries	ML platform owner	Lockfile, SBOM, image digest, toolkit manifest	Process-level and model-serving code paths	Dependency/image rebuild and compatibility testing
Framework/application	Application or ML team	Release, lockfile, service image	API, model loading, tools, and tenant data	Application release, feature disablement, or policy change

The map is useful because it assigns a question to each layer: what is installed, who can reach it, and who can change it? A package scanner may see the container but miss a host kernel module. A provider inventory may show a managed GPU service but not disclose the underlying driver. Preserve that uncertainty instead of marking an asset safe by assumption.

Inventory beyond package manifests

Capture the GPU vendor and model, driver version, firmware where relevant, host OS and kernel, runtime or toolkit, critical libraries, container image digest, runtime, orchestrator, GPU operator or device plugin, inference or training service, and cloud ownership. For Kubernetes, include node labels, device-plugin configuration, admission policy, and which namespaces can request GPUs. Kubernetes documents GPU scheduling through vendor device plugins; the operational fact that a plugin is installed is not proof that every workload has the same privilege or exposure.

For managed services, record the service name, region, contract owner, provider advisory, customer-visible version or image, supported mitigation, and date of provider confirmation. The customer may be unable to inspect a driver, but can still test endpoint exposure, identity scope, network paths, feature flags, and provider remediation evidence.

Applicability and reachability

For every advisory, write down the affected component, affected versions, fixed versions, deployment mode, required configuration, and whether the issue is host-side or guest-side. Confirm the running artifact rather than trusting a repository tag. Check backports and vendor patches: a distribution may include a fix without using the upstream version string.

Then ask who can reach the vulnerable function. Possibilities include an internet user, an authenticated tenant workload, a local process in the same pod, a training job, an inference request, a privileged operator, or only a maintenance account. A driver issue on an isolated node and a management API exposed to tenant administrators are different risks even if an advisory assigns them the same base score. Use the current CISA KEV and AI systems guidance when known exploitation changes urgency, but do not treat KEV inclusion as proof that your version is affected.

Self-hosted, virtualized, and managed ownership

Bare metal makes the organization responsible for the host, driver, kernel, runtime, and orchestration. A GPU VM adds a hypervisor and cloud control-plane boundary; the customer may own the guest driver while the provider owns the host and passthrough implementation. Containers still depend on the host kernel and runtime. Kubernetes adds operators, device plugins, scheduling, and namespace policy. Fully managed inference shifts more patching to the provider, but customer configuration, credentials, network exposure, and tenant authorization remain customer concerns.

Write the shared-responsibility boundary into the ticket. A provider bulletin may require an instance replacement rather than a package update. A customer mitigation may be to disable a feature, move jobs to a patched node pool, or restrict an endpoint while the provider completes work. Do not claim remediation until the responsible party and evidence are explicit.

Multi-tenant and management-plane risk

Shared GPU platforms can place jobs, teams, or tenants on common nodes. Evaluate isolation at the host and kernel, container, VM, runtime, scheduler, and device-assignment boundaries. Do not infer a universal GPU-memory leak from a generic advisory; require evidence about the affected product, concurrent workloads, permissions, and configuration. A conventional Kubernetes, identity, reverse-proxy, registry, or cloud API vulnerability may be the most important AI-stack issue because it can reach credentials or scheduling control. Vulnerability intelligence should connect those ordinary components to AI assets, as the triage workflow does.

Patch the stack, not one version string

GPU compatibility creates operational coupling among kernel, driver, toolkit, libraries, image, framework, operator, and node policy. Test the intended fix in a representative node and workload. Verify startup, device discovery, model loading, inference or training, isolation policy, and rollback. A patched driver paired with an old privileged operator image can leave the relevant path exposed; rebuilding an image without replacing a vulnerable host kernel has the inverse problem.

Mitigation is not remediation

When a fix is not immediately available, reduce exposure by removing the node, restricting network paths, disabling an affected feature, limiting workload placement, removing unnecessary privileges, replacing an image, isolating a tenant, or moving work to a provider-supported service. Label the ticket MITIGATED and record the residual risk. Only label it PATCHED or REMEDIATED after the affected layer is updated or the provider confirms its fix and the deployment is rechecked.

GPU VULNERABILITY TRIAGE MATRIX

Layer	Evidence	Exposure question	Likely owner	Remediation	Closure evidence
Driver or kernel	Host inventory, advisory, reboot state	Can an untrusted workload reach the affected device or syscall?	Infrastructure	Patch and reboot/replace node	Running version, node health, retest
Runtime or library	SBOM, lockfile, image digest	Is the vulnerable code loaded by inference, training, or parser jobs?	ML platform	Rebuild image and dependencies	Digest, scan, functional test
Operator/device plugin	Deployment manifest, image, privileges	Does setup expose sockets, devices, or host paths?	Platform	Upgrade and review security context	Manifest diff, admission result, node test
Orchestrator	Cluster and policy inventory	Can a tenant or job reach control-plane functions?	Platform/IAM	Upgrade, authorize, isolate namespaces	Authorization tests and audit evidence
Management plane	Provider bulletin, API logs	Who can invoke the affected administrative operation?	Cloud/platform	Provider fix, scope roles, rotate keys	Provider confirmation and access review
Managed service	Contract/service status	Does the provider own the affected layer, and what can the customer verify?	Provider/customer owner	Apply provider mitigation or migrate	Dated provider statement, exposure test
Workload	Release and route inventory	Does the AI service expose the vulnerable path to users or tools?	Application team	Release, disable feature, or gateway rule	Deployment revision and security test

Verification and incident response

Closure requires actual deployed evidence: driver and runtime versions, image digest, operator and node state, scanner results, provider confirmation where applicable, and a security or functional retest. Keep the advisory, vector or CVE record, applicability decision, owner, mitigation, and closure date together. A green ticket without runtime evidence is not proof.

If containment or management compromise is suspected, stop affected workloads, isolate the node or account, preserve logs and images, rotate credentials that could have been exposed, inspect neighboring workloads, rebuild from trusted artifacts, patch the affected layer, and verify the clean state. Coordinate with the cloud or hardware provider when the boundary is managed. Avoid claiming that data was or was not exposed until evidence supports that conclusion.

Practical checklist

• Inventory host, driver, firmware, runtime, images, operator, orchestrator, model service, and cloud ownership.
• Match the advisory to component, version, configuration, deployment mode, and required privileges.
• Determine reachability from users, tenants, jobs, operators, and management networks.
• Map shared-responsibility boundaries and assign a named owner.
• Prioritize with applicability, reachability, tenant impact, asset importance, exploit evidence, KEV, and fix availability.
• Test patches as a compatible stack and record rollback conditions.
• Separate mitigation from patched/remediated status.
• Verify the running artifact, node state, policies, logs, and provider statements.
• Retest cross-tenant paths and privileged operations after changes.

Sources

• NVIDIA Product Security
• NVIDIA GPU Operator security considerations
• AMD Product Security
• Kubernetes GPU scheduling
• Kubernetes device plugins
• CISA Known Exploited Vulnerabilities Catalog
• NVD

GPU vulnerability management is complete only when the organization can name the affected layer, prove its exposure, assign the owner, apply a compatible treatment, and verify the running system.