Create, update, or delete a GenieACS preset. Presets define automatic configuration rules that are applied to CPE devices matching a precondition filter. Use action="put" to create or overwrite a preset, providing the full JSON body with weight, precondition, and configurations. Use action="delet...
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AI agents use manage_preset to create or modify resources in Genieacs. 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 manage_preset 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 Genieacs.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"manage_preset": {
"limits": [
{
"counter": "manage_preset_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Genieacs policy for all 12 tools.
These attack patterns abuse exactly the kind of access manage_preset gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Create, update, or delete a GenieACS preset. Presets define automatic configuration rules that are applied to CPE devices matching a precondition filter. Use action="put" to create or overwrite a preset, providing the full JSON body with weight, precondition, and configurations. Use action="delete" to remove a preset by name. A preset body should contain: weight (integer priority), precondition (a stringified MongoDB-style JSON query, e.g. "{\"_tags\":\"office\"}"), and configurations (array of objects with type "value", "provision", "add_object", or "delete_object"). Example body: {"weight":0,"precondition":"{\"_tags\":\"test\"}","configurations":[{"type":"provision","name":"myScript"}]}. Preset names cannot contain dots. Use genieacs://presets/list to view existing presets before making changes. Limitations: changes take effect on the next CPE inform, not immediately.. It is categorised as a Write tool in the Genieacs MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Genieacs MCP server in PolicyLayer and add a rule for manage_preset: 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 Genieacs. Nothing to install.
manage_preset is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the manage_preset 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.
Set action: deny in the PolicyLayer policy for manage_preset. 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.
manage_preset is provided by the Genieacs MCP server (genieacs-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 12 Genieacs tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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