Medium Risk

save_recipient

Save a payment recipient's bank details for future transfers. The user provides the details once, then you can just say 'pay Maria' in the future.

Part of the Agentpay server.

save_recipient can modify Agentpay 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 save_recipient to create or modify resources in Agentpay. 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 save_recipient 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 Agentpay.

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

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

See the full Agentpay policy for all 17 tools.

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

Save a payment recipient's bank details for future transfers. The user provides the details once, then you can just say 'pay Maria' in the future.. It is categorised as a Write tool in the Agentpay MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on save_recipient? +

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

What risk level is save_recipient? +

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

Can I rate-limit save_recipient? +

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

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

save_recipient is provided by the Agentpay MCP server (agentpay-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 Agentpay tool call.

Deterministic rules across all 17 Agentpay 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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