High Risk →

record_feedback

Record user feedback on a finding — mark it as a true positive (tp), false positive (fp), or won

Part of the Judges Panel server.

record_feedback can trigger actions in Judges Panel, 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 record_feedback to trigger processes or run actions in Judges Panel. 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.

record_feedback 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": {
    "record_feedback": {
      "limits": [
        {
          "counter": "record_feedback_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Judges Panel policy for all 78 tools.

Get this rule live on your own Judges Panel 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 record_feedback 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 record_feedback 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 record_feedback tool do? +

Record user feedback on a finding — mark it as a true positive (tp), false positive (fp), or won. It is categorised as a Execute tool in the Judges Panel MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on record_feedback? +

Register the Judges Panel MCP server in PolicyLayer and add a rule for record_feedback: 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 Judges Panel. Nothing to install.

What risk level is record_feedback? +

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

Can I rate-limit record_feedback? +

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

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

record_feedback is provided by the Judges Panel MCP server (@kevinrabun/judges). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Judges Panel tool call.

Deterministic rules across all 78 Judges Panel tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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