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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesTo stop someone from using your GPU server to mine crypto, first remove unnecessary public access, then secure the inference API and the cloud account or host that can create and control compute. These are related but separate risks: an attacker may misuse an exposed API, or gain access through stolen credentials, vulnerable software, or a misconfiguration and run workloads directly.
Use the hardening plan below in order. Its recommendations synthesize published security guidance; they are not the result of hands-on testing of a particular server or cloud configuration.
1. Find every exposed route into the service
Start with an inventory of what is reachable and what can administer it. An inference API is not the same attack path as a host login or a cloud identity with permission to launch compute.
- List public IP addresses, open host ports, API routes, and services behind load balancers or proxies.
- Check for test, staging, or orphaned production deployments that remain reachable after their original purpose has ended.
- Identify administrative interfaces, remote-management services, container control surfaces, and cloud credentials that can alter or create compute.
- Map which users, services, and tenants can call each model or function, and which identities can change the surrounding infrastructure.
OWASP’s Secure AI/ML Model Ops Cheat Sheet calls out publicly exposed inference endpoints without authentication, rate limiting, or input validation, as well as orphaned deployments and weak runtime isolation.
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2. Reduce public reachability
Keep internal inference endpoints on private networks wherever the workload allows. Restrict inbound traffic to the clients, services, or networks that need it, and do not expose administrative interfaces to the public internet.
If users outside your network must reach the service, put a controlled gateway or proxy in front of it. Enforce connection and request policy there, and keep the underlying inference endpoint inaccessible except to that gateway or other trusted services. The specific network controls vary by provider and deployment platform; the principle is to minimize internet exposure and allow only necessary traffic.
Google Cloud’s guidance on cryptocurrency-mining attacks recommends reducing internet exposure for compute resources and restricting external traffic. SANS Community’s Critical AI Security Guidelines v1.1 likewise advises against making internal training or inference endpoints public-facing unless necessary.
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3. Authenticate callers and limit what they can do
Require authentication and authorization for internal or sensitive endpoints. Give each identity access only to the models, functions, and environments it needs; a credential for one inference service should not automatically authorize unrelated workloads or administration.
Enforce access policy at more than one relevant layer—for example, at the gateway, application, and model endpoint—so a missed or misconfigured check in one place does not expose the model. OWASP AI Exchange puts the rationale plainly: “Apply defence-in-depth: Access control should be enforced at multiple layers of the AI system (API gateway, application layer, model endpoint) so that a single failure does not expose the model.” See its access-control guidance for model inference.
Log successful and failed access attempts, subject to your privacy and data-retention obligations. If anonymous public access is a product requirement, do not treat anonymity as a reason to omit controls: use tighter quotas, bot or anomaly detection, and active monitoring.
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4. Limit API use and machine consumption
Protect against both excessive inference requests and workloads that consume too much of the host. Per-user or per-tenant limits help contain misuse even when the service has to remain reachable.
- At the API layer: Set request, token, concurrency, and spend limits per user or tenant. Bound retries, recursion, and agent or tool-call chain depth where those features are enabled.
- At the workload layer: Set appropriate CPU, memory, GPU, disk, process, and network limits for each service or workload.
- For abnormal spikes: Add a circuit breaker or kill switch that can halt or restrict service when usage, cost, latency, or tool-call activity exceeds expected bounds.
Choose thresholds based on the service’s legitimate workload and operating constraints; the guidance does not establish universal safe limits for every model or deployment.
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An attacker does not need to abuse the inference API if they can obtain credentials or exploit the machine that runs it. Google Cloud identifies mining-attack vectors including “Vulnerabilities in third-party or user-managed software,” “Weak, absent, or compromised credentials,” “Cloud or application misconfigurations,” and “Identity and token abuse.” Those categories apply to the infrastructure path, not only to API calls.
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- Protect administrator identities: Require multifactor authentication (MFA) and review who has permission to create, modify, or access compute.
- Reduce identity privileges: Review cloud IAM grants, scope service credentials to the resources and actions required, and avoid broad or long-lived credentials where a narrower alternative is available.
- Protect secrets: Store credentials safely; rotate or revoke any suspected compromised secret. Audit high-risk identity and permission changes.
- Isolate serving workloads: Harden containers and minimize capabilities. Do not give serving containers access to host paths, container sockets, cloud metadata services, or devices they do not require.
- Separate trust domains: Keep production inference separate from training and evaluation workloads. Do not share accelerators across mutually untrusted tenants unless the platform provides strong hardware-backed partitioning and memory isolation.
Google Cloud’s mining-attack guidance recommends strengthening identity protections, while OWASP’s model-operations guidance covers scoped credentials, runtime isolation, and workload resource limits.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Monitor for signs of abuse
Monitoring should cover both inference behavior and the infrastructure that supplies the compute. A jump in API usage may reveal abuse at the service layer; unfamiliar processes or unexpected outbound connections may point to direct host or account compromise.
- Track request volume, token or spend consumption, latency, and unusual usage patterns.
- Alert on unexpected CPU or GPU consumption, unfamiliar processes, and unexpected outbound network connections.
- Watch for risky IAM changes and attempts to access cloud metadata endpoints.
- Retain enough access and infrastructure logs to investigate activity, while avoiding unnecessary logging of sensitive prompts.
OWASP, Google Cloud, and SANS all address monitoring as part of protecting inference services or cloud workloads. NIST’s SP 800-228, Guidelines for API Protection for Cloud-Native Systems, published in June 2025 and updated March 13, 2026, describes API lifecycle risks and pre-runtime and runtime controls that organizations can adopt incrementally according to risk.
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7. Prepare to contain and recover
Decide in advance who is authorized to disable an exposed endpoint, stop a suspicious workload, and revoke or rotate compromised access. Make sure responders can reach the relevant logs and know how to act without relying on the suspected workload itself.
- Contain the affected workload and access path—for example, disable the endpoint or stop the suspicious compute resource.
- Revoke or rotate credentials that may have been exposed, and review identity and host activity for unauthorized changes.
- Preserve relevant audit evidence for investigation, following your organization’s retention and privacy requirements.
- Restore from trusted images and configuration, then verify access controls and monitoring before returning the service to use.
Exact containment and recovery actions depend on the cloud provider, host, and orchestration platform. The cited guidance supports planning for protection, detection, and mitigation, but does not provide one complete provider-neutral incident playbook.
Choose controls for the deployment you actually run
The right balance depends on who needs access and how much isolation the workload requires. Use these distinctions to make the design decision explicit:
- Public or private: Prefer private reachability for internal services. If public access is necessary, control and monitor the route rather than exposing the host or endpoint indiscriminately.
- Authenticated or intentionally anonymous: Authenticate sensitive and internal callers. For an anonymous public product, compensate with strict quotas and abuse detection.
- Shared or isolated accelerators: Shared GPUs create a trust-boundary question. Use them across mutually untrusted tenants only when the platform offers strong hardware-backed partitioning and memory isolation.
- Gateway, application, or endpoint enforcement: Use layered checks where appropriate instead of relying on one control point.
- Managed or portable controls: Provider-managed controls can simplify implementation, but their settings are platform-specific. Portable practices—least privilege, scoped secrets, resource limits, and monitoring—still matter across environments.
These choices trade operational reach against attack surface. NIST’s API guidance supports risk-based, incremental adoption of protections rather than assuming one control fits every deployment.
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