CUA MCP Server — agentic threat model
The CUA MCP Server presents a high-risk profile due to its capability for full macOS desktop control and GUI actuation, which is significantly mitigated by its design requirement for sandboxed VM execution.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.90 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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 — relies on external LLMs via the MCP protocol, making it susceptible to prompt injection driving arbitrary desktop actions.
Not certain from the listing — primarily processes real-time screenshots and input events rather than maintaining a persistent knowledge base or vector store.
The agent framework exposes highly sensitive OS-level tools (mouse, keyboard, screenshot) to an LLM, creating a high-risk surface where prompt injection can lead to unauthorized tool execution.
Runs in sandboxed macOS VMs on Apple Silicon. This sandboxing is a critical control to prevent host compromise, though VM escape remains a theoretical threat.
Not certain from the listing — no explicit mention of logging, guardrails, or observability tools to monitor the desktop actions taken by the agent.
Not certain from the listing — lacks details on authentication, authorization, or policy enforcement mechanisms between the MCP client and server.
Not certain from the listing — designed as an MCP server for a single LLM/client, with no explicit multi-agent coordination or marketplace features described.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).