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gpu_run

Run any GPU-Bridge AI service. 30 services available: LLM inference (sub-second), image generation (FLUX, SD3.5), video generation, video enhancement (up to 4K), speech-to-text (Whisper, <1s), TTS (40+ voices), music generation, voice cloning, embeddings, document reranking (Jina), OCR, PDF/docum...

Part of the Mcp Server server.

gpu_run can trigger actions in Mcp Server, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents invoke gpu_run to trigger processes or run actions in Mcp Server. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.

gpu_run can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.

Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "gpu_run": {
      "limits": [
        {
          "counter": "gpu_run_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Mcp Server policy for all 5 tools.

Get this rule live on your own Mcp Server server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access gpu_run gives an agent. Each links to the full case and the policy that stops it:

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so gpu_run only ever does what you allow.

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Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the gpu_run tool do? +

Run any GPU-Bridge AI service. 30 services available: LLM inference (sub-second), image generation (FLUX, SD3.5), video generation, video enhancement (up to 4K), speech-to-text (Whisper, <1s), TTS (40+ voices), music generation, voice cloning, embeddings, document reranking (Jina), OCR, PDF/document parsing, NSFW detection, image captioning, visual Q&A, background removal, face restoration, upscaling, stickers, and more. Use gpu_catalog to see all available services.. It is categorised as a Execute tool in the Mcp Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on gpu_run? +

Register the Mcp Server MCP server in PolicyLayer and add a rule for gpu_run: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Server. Nothing to install.

What risk level is gpu_run? +

gpu_run is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit gpu_run? +

Yes. Add a rate_limit block to the gpu_run rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block gpu_run completely? +

Set action: deny in the PolicyLayer policy for gpu_run. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides gpu_run? +

gpu_run is provided by the Mcp Server MCP server (mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Mcp Server tool call.

Deterministic rules across all 5 Mcp Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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