umami-analytics-mcp — agentic threat model
The umami-analytics-mcp agent exhibits moderate agentic risk due to its access to analytics API keys and administrative capabilities, which are strongly mitigated by a security-first, least-privilege design requiring explicit opt-in flags for destructive actions.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.30 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.20 |
Scored with the canonical OWASP AIVSS formula (AIVSS calculator reference); agentic risk factors estimated from the agent’s described capabilities.
MAESTRO 7-layer threat model
Per-layer threats for this agent. Layers tagged “not certain from listing” are general, caveated commentary where the public description didn’t pin that layer.
Not certain from the listing — The agent acts as an MCP server and does not specify a bound foundation model. Standard LLM risks like prompt injection could be used to trigger unauthorized tool execution if the model is manipulated.
The agent interacts with Umami analytics databases (v3/Cloud). Threat surface includes potential exfiltration of sensitive web traffic analytics, user behavior data, or metadata if the agent is compromised.
Orchestrated via the Model Context Protocol (MCP). The primary threat is tool misuse or unauthorized execution of write/admin/destructive actions, which is mitigated by default least-privilege configurations and explicit opt-in flags.
The server holds Umami API keys and credentials. Insecure storage of these secrets or running the MCP server in an un-sandboxed environment could lead to credential theft or local privilege escalation.
Not certain from the listing — There is no explicit mention of built-in evaluation, logging, or guardrail frameworks to monitor the agent's decision-making or detect anomalous tool calls.
Strong security-first design focusing on least privilege. It implements explicit opt-in flags to enable write, admin, or destructive actions, preventing accidental or unauthorized state changes.
As an MCP server, it is designed to integrate into broader agentic ecosystems. A compromised orchestrator agent could abuse trust to execute authorized analytics queries or administrative tasks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).