Medium Risk

recommendations.get_fallback_plan

Generate a fallback plan for a model/region/endpoint.

Risk signalsAccepts URL/endpoint input (endpoint) · High parameter count (14 properties)

Part of the AIStatusDashboard server.

recommendations.get_fallback_plan can modify AIStatusDashboard 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 recommendations.get_fallback_plan to create or modify resources in AIStatusDashboard. 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 recommendations.get_fallback_plan 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 AIStatusDashboard.

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

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

See the full AIStatusDashboard policy for all 7 tools.

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These attack patterns abuse exactly the kind of access recommendations.get_fallback_plan 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 recommendations.get_fallback_plan 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 recommendations.get_fallback_plan tool do? +

Generate a fallback plan for a model/region/endpoint.. It is categorised as a Write tool in the AIStatusDashboard MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on recommendations.get_fallback_plan? +

Register the AIStatusDashboard MCP server in PolicyLayer and add a rule for recommendations.get_fallback_plan: 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 AIStatusDashboard. Nothing to install.

What risk level is recommendations.get_fallback_plan? +

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

Can I rate-limit recommendations.get_fallback_plan? +

Yes. Add a rate_limit block to the recommendations.get_fallback_plan 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 recommendations.get_fallback_plan completely? +

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

recommendations.get_fallback_plan is provided by the AIStatusDashboard MCP server (https://aistatusdashboard.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every AIStatusDashboard tool call.

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

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