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To stop someone from using your GPU server to mine crypto, first restrict who can reach the inference service, then limit what authenticated users and workloads can consume. Protecting the API alone is not enough: attackers can also use stolen credentials, vulnerable software, or cloud misconfigurations to gain direct access to compute. The controls below apply to self-hosted and cloud-hosted services; implementation details vary by provider and orchestrator.
1. Identify every exposed path
Start with an inventory of what is reachable and what can create or control compute. An inference API and the host or cloud account are related risks, but they are not the same access path.
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- List public IP addresses, open host ports, API routes, and any gateway or proxy in front of the service.
- Find test, abandoned, or orphaned deployments, along with admin and debugging interfaces.
- Identify credentials and service identities that can invoke models, access the host, or create and manage compute.
- Check whether serving containers can reach host paths, container sockets, cloud metadata services, or devices they do not need.
OWASP’s Secure AI/ML Model Ops Cheat Sheet highlights risks including publicly exposed inference endpoints without authentication, rate limiting, or input validation, orphaned production deployments, and weak runtime isolation.
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Keep internal inference endpoints on private networking wherever the use case allows. Restrict inbound traffic to the clients, services, or networks that actually need access, and do not expose administrative interfaces to the public internet.
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If clients must connect from outside your network, route traffic through controlled infrastructure such as a gateway or proxy. Apply authentication and request policies there, while retaining protections at the service itself. Google Cloud recommends reducing internet exposure for compute resources, and SANS advises against making internal training or inference endpoints public-facing unless necessary. The specific network settings differ by platform.
3. Authenticate, authorize, and apply least privilege
Require authentication and authorization for internal or sensitive endpoints. Give each identity only the access it needs—for example, to specific models, functions, or environments—and avoid a shared credential with broad access.
Enforce access control at more than one relevant layer, such as the gateway, application, and model endpoint. OWASP AI Exchange puts the reason 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. Log successful and failed access attempts, subject to your privacy obligations.
If anonymous public access is an intentional product requirement, it is not a reason to leave the service unbounded: use stricter quotas, abuse and anomaly detection, and monitoring. SANS recognizes that public-facing applications may have different authentication needs, while still emphasizing control of inference access and API use in Critical AI Security Guidelines v1.1.
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4. Cap both API usage and machine resources
API limits help prevent a client from consuming unlimited inference capacity, but they do not constrain every way a compromised workload can use a machine. Set controls at both the service and runtime levels.
- Per user or tenant: cap request volume, token consumption, concurrency, and spend.
- For agents and tool use: bound retries, recursion, and chain depth to prevent runaway request or tool-call loops.
- Per workload: limit CPU, memory, GPU, disk, process count, and network use according to the workload’s needs.
- For abnormal behavior: define a circuit breaker or kill switch for unusual usage, cost, latency, or tool-call spikes.
Choose limits that fit legitimate demand and test their operational effect before relying on them to contain abuse. OWASP’s model-operations guidance covers request and spend caps as well as workload resource limits.
5. Harden cloud identity, host, and runtime
Protect identities and secrets
Require multifactor authentication (MFA) for administrators. Review cloud IAM grants, remove unnecessary permissions, and audit high-risk permission changes. Use narrowly scoped service credentials rather than broad or long-lived credentials; store secrets securely, and rotate or revoke credentials suspected of compromise. Keep inference credentials scoped to the endpoint and environment that need them.
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Google Cloud’s cryptocurrency-mining attack guidance identifies “Vulnerabilities in third-party or user-managed software,” “Weak, absent, or compromised credentials,” “Cloud or application misconfigurations,” and “Identity and token abuse” as attack vectors.
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Limit what a serving workload can reach
Harden containers and minimize their capabilities. A serving container should not have access to host paths, container sockets, cloud metadata services, or devices it does not require. Separate production inference from training and evaluation workloads. Do not share accelerators across mutually untrusted tenants unless the isolation boundary, including hardware-backed partitioning and memory isolation, is strong enough for that trust model.
Cloud and application controls are complementary: a locked-down endpoint does not prevent an attacker with a compromised cloud identity from using compute directly, and strong account security does not make an anonymously reachable API safe.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Monitor for signs of abuse
Monitor service behavior and the infrastructure that runs it. Establish a baseline that lets your team recognize meaningful deviations, and route alerts to someone who can act on them.
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- On the host or in the cloud: alert on unexpected compute consumption, unfamiliar processes, unusual outbound connections, risky IAM changes, and attempts to access metadata endpoints.
- For investigation: retain logs sufficient to trace access and changes, while avoiding unnecessary retention of sensitive prompts.
OWASP, Google Cloud, and SANS all recommend monitoring relevant service or infrastructure activity; Google Cloud’s mining-attack guidance also emphasizes reducing exposure and strengthening identity protections.
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7. Prepare to contain and recover
Decide in advance who is authorized and equipped to disable an exposed endpoint, stop a suspicious workload, and revoke or rotate compromised access. Make sure the team can review audit evidence. Provider and orchestration details determine the exact response steps, so document and rehearse the procedure for your own environment.
If compromise is suspected, contain the affected workload and access path, investigate identity and host activity, and restore service from trusted images and configuration. NIST’s SP 800-228, Guidelines for API Protection for Cloud-Native Systems describes API lifecycle risks and recommends pre-runtime and runtime controls, with incremental, risk-based adoption. It notes: “Hence, a secure deployment of APIs is critical for overall enterprise security.”
Choose controls for the service’s trust boundary
The right design depends on who needs access and how much trust the workloads share. Use these choices to guide implementation rather than treating any single control as a substitute for the others.
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| Decision | Safer default | When the alternative is necessary |
|---|---|---|
| Private or public reachability | Keep internal inference private and allow only necessary clients or networks. | If public access is required, place it behind controlled infrastructure and apply request policy. |
| Authenticated or anonymous access | Authenticate users and services, then authorize only required actions. | If anonymous access is a product requirement, compensate with tight quotas, abuse detection, and monitoring. |
| Shared or isolated GPU/runtime | Separate workloads with different trust levels. | Share accelerators only when the isolation boundary is appropriate for the tenants and workload. |
| Where access controls are enforced | Use multiple relevant layers: gateway, application, and model endpoint. | Do not rely on a single layer when another reachable path could bypass it. |
| Provider-specific or portable controls | Use provider-managed protections where they fit, alongside portable application and runtime controls. | Implementation and exact settings vary by provider and orchestration platform. |
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