Critical Risk →

tfruns

INSPECTION: List all Terraform deployment runs for a session Returns job IDs, statuses, types (apply/destroy), and timestamps for every run. Use this to see deployment history, find job IDs for log inspection, or check which deployments succeeded or failed. REQUIRES: session_id from convoopen res...

Risk signalsBulk/mass operation — affects multiple targets

Part of the InsideOut (Riley) server.

tfruns can permanently delete data in InsideOut (Riley), with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents may call tfruns to permanently remove or destroy resources in InsideOut (Riley). Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.

Without a policy, an AI agent could call tfruns in a loop, permanently destroying resources in InsideOut (Riley). There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.

Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.

policy.json
{
  "version": "1",
  "default": "deny",
  "hide": [
    "tfruns"
  ]
}

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

Get this rule live on your own InsideOut (Riley) 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 tfruns 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 tfruns only ever does what you allow.

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Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.

What does the tfruns tool do? +

INSPECTION: List all Terraform deployment runs for a session Returns job IDs, statuses, types (apply/destroy), and timestamps for every run. Use this to see deployment history, find job IDs for log inspection, or check which deployments succeeded or failed. REQUIRES: session_id from convoopen response (format: sess_v2_...).. It is categorised as a Destructive tool in the InsideOut (Riley) MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.

How do I enforce a policy on tfruns? +

Register the InsideOut (Riley) MCP server in PolicyLayer and add a rule for tfruns: 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 tfruns? +

tfruns is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.

Can I rate-limit tfruns? +

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

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

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

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