coreweave-security-basics

'Secure CoreWeave deployments with RBAC, network policies, and secrets

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coreweave-pack

Claude Code skill pack for CoreWeave (24 skills)

saas packs v1.0.0
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Installation

This skill is included in the coreweave-pack plugin:

/plugin install coreweave-pack@claude-code-plugins-plus

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Instructions

CoreWeave Security Basics

Overview

CoreWeave provides bare-metal GPU cloud on Kubernetes. Security concerns center on compute credential management (kubeconfig, deploy tokens), network isolation between inference workloads, secrets for model registry access (HuggingFace, container registries), and protecting sensitive training data on persistent volumes. A compromised namespace can expose GPU resources, model weights, and customer inference data.

API Key Management


import { KubeConfig, CoreV1Api } from "@kubernetes/client-node";

function createCoreWeaveClient(): CoreV1Api {
  const apiKey = process.env.COREWEAVE_API_KEY;
  if (!apiKey) {
    throw new Error("Missing COREWEAVE_API_KEY — set via secrets manager");
  }
  const kc = new KubeConfig();
  kc.loadFromDefault();
  const api = kc.makeApiClient(CoreV1Api);
  // Never log kubeconfig or API key contents
  console.log("CoreWeave client initialized for namespace:", process.env.CW_NAMESPACE);
  return api;
}

Webhook Signature Verification


import crypto from "crypto";
import { Request, Response, NextFunction } from "express";

function verifyCoreWeaveWebhook(req: Request, res: Response, next: NextFunction): void {
  const signature = req.headers["x-coreweave-signature"] as string;
  const secret = process.env.COREWEAVE_WEBHOOK_SECRET!;
  const expected = crypto.createHmac("sha256", secret).update(req.body).digest("hex");
  if (!signature || !crypto.timingSafeEqual(Buffer.from(signature), Buffer.from(expected))) {
    res.status(401).send("Invalid signature");
    return;
  }
  next();
}

Input Validation


import { z } from "zod";

const WorkloadRequestSchema = z.object({
  namespace: z.string().regex(/^[a-z0-9-]+$/).max(63),
  gpu_type: z.enum(["A100_80GB", "A100_40GB", "H100_80GB", "RTX_A6000"]),
  gpu_count: z.number().int().min(1).max(8),
  image: z.string().regex(/^[a-z0-9.\-/]+:[a-z0-9.\-]+$/),
  model_id: z.string().min(1).max(200),
});

function validateWorkloadRequest(data: unknown) {
  return WorkloadRequestSchema.parse(data);
}

Data Protection


const CW_SENSITIVE_FIELDS = ["kubeconfig", "hf_token", "registry_password", "api_key", "model_weights_url"];

function redactCoreWeaveLog(record: Record<string, unknown>): Record<string, unknown> {
  const redacted = { ...record };
  for (const field of CW_SENSITIVE_FIELDS) {
    if (field in redacted) redacted[field] = "[REDACTED]";
  }
  return redacted;
}

Security Checklist

  • [ ] Kubeconfig stored in secrets manager, never in repos
  • [ ] Kubernetes Secrets used for model tokens (not env vars in YAML)
  • [ ] Network policies restrict inference endpoint access
  • [ ] RBAC limits namespace access per team
  • [ ] Container images scanned for CVEs before deployment
  • [ ] PVCs encrypted at rest for training data
  • [ ] GPU workload namespaces isolated with NetworkPolicy
  • [ ] Deploy tokens scoped per-namespace, not cluster-wide

Error Handling

Vulnerability Risk Mitigation
Leaked kubeconfig Full cluster access, GPU resource theft Secrets manager + RBAC scoping
Open inference endpoints Unauthorized model access NetworkPolicy ingress rules
Unscanned container images CVE exploitation in GPU pods CI image scanning before deploy
Overly broad RBAC Cross-namespace data leakage Per-team namespace RBAC bindings
Unencrypted PVCs Training data exposure Encrypted storage classes

Resources

Next Steps

See coreweave-prod-checklist.

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