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Get Started Free →Secure CoreWeave deployments with RBAC, network policies, and secrets management. Use when hardening GPU workloads, managing model access, or configuring namespace isolation. Trigger with phrases like "coreweave security", "coreweave rbac", "secure coreweave", "coreweave secrets".
.claude/skills/jeremylongshore-coreweave-security-basics/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | 36% | 0% |
| case-01 | ✗→✓ | ▲ Improved | 26% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 11% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-18 | ✗→✓ | ▲ Improved | -26% | 0% |
> Community-contributed. Not affiliated with, endorsed by, or sponsored by CoreWeave, Inc. CoreWeave is a registered trademark of CoreWeave, Inc.
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.
typescriptimport { 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; }
typescriptimport 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(); }
typescriptimport { 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); }
typescriptconst 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; }
| 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 |
See coreweave-prod-checklist.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-10 | fail→pass | 10,603 | 8,454 | -20% | 1 | 1 | 0% | 1,964 | 2,673 | +36% | 0 | 0 | — |
case-01 | fail→pass | 11,825 | 9,493 | -20% | 1 | 1 | 0% | 2,462 | 3,106 | +26% | 0 | 0 | — |
case-02 | fail→pass | 13,312 | 9,925 | -25% | 1 | 1 | 0% | 2,765 | 3,064 | +11% | 0 | 0 | — |
case-03 | fail→fail | 34,722 | 12,000 | -65% | 1 | 1 | 0% | 3,276 | 3,683 | +12% | 0 | 0 | — |
case-04 | fail→fail | 12,318 | 8,406 | -32% | 1 | 1 | 0% | 2,487 | 2,722 | +9% | 0 | 0 | — |
case-05 | pass→pass | 9,983 | 7,575 | -24% | 1 | 1 | 0% | 2,265 | 2,832 | +25% | 0 | 0 | — |
case-06 | fail→fail | 4,312 | 5,714 | +33% | 1 | 1 | 0% | 780 | 2,207 | +183% | 0 | 0 | — |
case-07 | fail→fail | 6,469 | 6,489 | +0% | 1 | 1 | 0% | 1,087 | 2,218 | +104% | 0 | 0 | — |
case-08 | fail→fail | 8,664 | 13,545 | +56% | 1 | 1 | 0% | 1,641 | 3,058 | +86% | 0 | 0 | — |
case-09 | fail→fail | 5,381 | 3,399 | -37% | 1 | 1 | 0% | 994 | 1,730 | +74% | 0 | 0 | — |
case-11 | fail→fail | 20,514 | 10,089 | -51% | 1 | 1 | 0% | 4,432 | 3,171 | -28% | 0 | 0 | — |
case-12 | pass→pass | 10,399 | 8,830 | -15% | 1 | 1 | 0% | 1,918 | 2,572 | +34% | 0 | 0 | — |
case-13 | pass→pass | 9,998 | 7,437 | -26% | 1 | 1 | 0% | 1,841 | 2,296 | +25% | 0 | 0 | — |
case-14 | pass→pass | 17,177 | 10,921 | -36% | 1 | 1 | 0% | 2,940 | 3,045 | +4% | 0 | 0 | — |
case-15 | pass→pass | 8,903 | 4,621 | -48% | 1 | 1 | 0% | 1,487 | 1,436 | -3% | 0 | 0 | — |
case-16 | pass→pass | 11,748 | 9,766 | -17% | 1 | 1 | 0% | 2,147 | 2,818 | +31% | 0 | 0 | — |
case-17 | fail→pass | 10,121 | 3,948 | -61% | 1 | 1 | 0% | 1,798 | 1,630 | -9% | 0 | 0 | — |
case-18 | fail→pass | 13,343 | 3,593 | -73% | 1 | 1 | 0% | 2,252 | 1,676 | -26% | 0 | 0 | — |
case-19 | pass→pass | 12,257 | 7,791 | -36% | 1 | 1 | 0% | 2,009 | 2,246 | +12% | 0 | 0 | — |
case-20 | pass→pass | 13,735 | 9,778 | -29% | 1 | 1 | 0% | 2,326 | 2,812 | +21% | 0 | 0 | — |
case-21 | pass→pass | 9,117 | 3,668 | -60% | 1 | 1 | 0% | 1,487 | 1,640 | +10% | 0 | 0 | — |
case-22 | pass→pass | 6,006 | 6,922 | +15% | 1 | 1 | 0% | 1,314 | 2,607 | +98% | 0 | 0 | — |
case-23 | fail→fail | 13,682 | 13,732 | +0% | 1 | 1 | 0% | 2,973 | 3,910 | +32% | 0 | 0 | — |
case-24 | pass→pass | 20,477 | 6,791 | -67% | 1 | 1 | 0% | 1,476 | 2,476 | +68% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 24 cases were attempted. The headline lift of +21 percentage points is the difference between those two pass rates over the 24 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.