Make MCP Server — agentic threat model
This agent acts as a high-risk bridge between LLMs and Make's automation platform, enabling arbitrary write actions across connected services. Its security posture depends entirely on external scenario scoping and token-based authentication.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.70 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.50 |
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 MCP server is model-agnostic and does not specify the underlying foundation model, leaving it vulnerable to standard adversarial prompt injection that could trigger unintended Make scenarios.
Not certain from the listing — The server processes structured inputs and outputs but does not explicitly detail RAG or vector database integrations, though connected Make scenarios may access sensitive corporate data stores.
The server exposes Make scenarios as tools, introducing high risk of tool misuse and insecure tool integration if input validation is bypassed, allowing malicious actors to trigger arbitrary write actions.
Supports both local and hosted deployment. Local deployment risks host compromise or lateral movement if the MCP server process is compromised, while hosted deployment relies on the security of the hosting environment.
Not certain from the listing — The description does not mention built-in guardrails, logging, or anomaly detection to monitor scenario execution or detect malicious inputs before they reach Make.
Implements token-based authentication to secure the connection, but relies heavily on external scenario scoping and user-configured input validation to enforce authorization boundaries.
Bridges AI agents to Make's vast app connector library, creating a high risk of cascading failures and trust abuse if a compromised agent triggers scenarios that interact with external third-party APIs.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).