Medium Risk

submit_feedback

FEEDBACK: Submit feedback, bug reports, or feature requests to Luther Systems Use this tool to forward user feedback directly to the Luther Systems team. This includes bug reports, feature requests, questions, or general feedback about InsideOut. The agent itself can also use this tool to report ...

Part of the InsideOut (Riley) server.

submit_feedback can modify InsideOut (Riley) 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 submit_feedback to create or modify resources in InsideOut (Riley). 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 submit_feedback 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 InsideOut (Riley).

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

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

See the full InsideOut (Riley) policy for all 24 tools.

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These attack patterns abuse exactly the kind of access submit_feedback 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 submit_feedback 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 submit_feedback tool do? +

FEEDBACK: Submit feedback, bug reports, or feature requests to Luther Systems Use this tool to forward user feedback directly to the Luther Systems team. This includes bug reports, feature requests, questions, or general feedback about InsideOut. The agent itself can also use this tool to report issues it encounters during operation. REQUIRES: session_id, category, message OPTIONAL: user_email (for follow-up), user_name, source (default: 'mcp'), initiator ('user' or 'agent') Categories: bug_report, feature_request, general_feedback, question, security The 'initiator' field tracks who triggered the report: - 'user' — the user explicitly reported the issue or requested feedback submission - 'agent' — Riley detected an issue and initiated the feedback flow Examples: - User says 'the deploy button is broken' → submit_feedback(category='bug_report', message='...', initiator='user') - User says 'I wish it had dark mode' → submit_feedback(category='feature_request', message='...', initiator='user') - Deployment failed with Terraform error → submit_feedback(category='bug_report', message='Deployment failed: Terraform apply error on aws_alb resource — timeout waiting for ALB provisioning', initiator='agent'). It is categorised as a Write tool in the InsideOut (Riley) MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on submit_feedback? +

Register the InsideOut (Riley) MCP server in PolicyLayer and add a rule for submit_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 InsideOut (Riley). Nothing to install.

What risk level is submit_feedback? +

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

Can I rate-limit submit_feedback? +

Yes. Add a rate_limit block to the submit_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 submit_feedback completely? +

Set action: deny in the PolicyLayer policy for submit_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 submit_feedback? +

submit_feedback is provided by the InsideOut (Riley) MCP server (oci:docker.io/luthersystems/insideout-mcp:v0.36.3). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every InsideOut (Riley) tool call.

Deterministic rules across all 24 InsideOut (Riley) tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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