Medium Risk

forge_generate

Generate an optimized GPU kernel from scratch based on a specification. Use this when you need to create a new high-performance kernel without existing PyTorch code. Forge will generate a PyTorch baseline first, then optimize it into Triton or CUDA. Requires authentication via forge_auth first. C...

Risk signalsHigh parameter count (10 properties)

Part of the Forge server.

forge_generate can modify Forge data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use forge_generate to create or modify resources in Forge. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call forge_generate repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Forge.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "forge_generate": {
      "limits": [
        {
          "counter": "forge_generate_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Forge policy for all 7 tools.

Get this rule live on your own Forge 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 forge_generate gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

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

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

What does the forge_generate tool do? +

Generate an optimized GPU kernel from scratch based on a specification. Use this when you need to create a new high-performance kernel without existing PyTorch code. Forge will generate a PyTorch baseline first, then optimize it into Triton or CUDA. Requires authentication via forge_auth first. Cost: 1 credit per generation.. It is categorised as a Write tool in the Forge MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on forge_generate? +

Register the Forge MCP server in PolicyLayer and add a rule for forge_generate: 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 Forge. Nothing to install.

What risk level is forge_generate? +

forge_generate is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit forge_generate? +

Yes. Add a rate_limit block to the forge_generate 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 forge_generate completely? +

Set action: deny in the PolicyLayer policy for forge_generate. 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 forge_generate? +

forge_generate is provided by the Forge MCP server (@rightnow/forge-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 Forge tool call.

Deterministic rules across all 7 Forge tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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