Medium Risk

backfill_story

Mark a story as verified when the work was completed outside loopctl (e.g. before the project was onboarded). REFUSED for stories that have any loopctl dispatch lineage — non-pending agent_status, assigned_agent_id, implementer_dispatch_id, or verifier_dispatch_id set. Also refused for stories al...

Part of the Loopctl server.

backfill_story can modify Loopctl 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 backfill_story to create or modify resources in Loopctl. 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 backfill_story 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 Loopctl.

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

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

See the full Loopctl policy for all 52 tools.

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

Mark a story as verified when the work was completed outside loopctl (e.g. before the project was onboarded). REFUSED for stories that have any loopctl dispatch lineage — non-pending agent_status, assigned_agent_id, implementer_dispatch_id, or verifier_dispatch_id set. Also refused for stories already :verified (idempotent no-op when the same payload is sent) or :rejected. Records a provenance marker in metadata.backfill plus an audit event and a story.backfilled webhook. REQUIRES reason. Strongly recommend passing evidence_url (http/https, no credentials in userinfo) and pr_number.. It is categorised as a Write tool in the Loopctl MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on backfill_story? +

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

What risk level is backfill_story? +

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

Can I rate-limit backfill_story? +

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

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

backfill_story is provided by the Loopctl MCP server (loopctl-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 Loopctl tool call.

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