BoardGameGeek MCP — agentic threat model
The BoardGameGeek MCP is a low-risk, read-only connector designed to retrieve public board game data and user profiles, presenting minimal agentic risk due to its lack of write capabilities or state modification.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.20 | |
| 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 relies on an external, unspecified LLM via the Model Context Protocol (MCP). The primary risk is indirect prompt injection if malicious user-generated content from BoardGameGeek profiles or descriptions is parsed by the model.
Data operations are strictly read-only, pulling public board game metadata and user collections from BoardGameGeek. There is no vector database or local knowledge base to poison, though external text must be treated as untrusted.
The framework exposes read-only tools for BGG data retrieval and filtering. Tool misuse is limited to denial-of-service or rate-limiting on the BGG API, as there are no write or execution tools available.
Not certain from the listing — The deployment environment depends on the host running the MCP server. Standard risks include insecure local transport of MCP messages or exposure of the host system if the MCP server itself is compromised.
Not certain from the listing — No built-in logging, guardrails, or evaluation metrics are mentioned. Monitoring is likely delegated to the parent MCP host or client application.
The tool accesses public BGG data, requiring no sensitive user credentials or write permissions. Compliance risks are minimal, though user privacy must be respected when fetching public profile and collection data.
As an MCP tool, it can be integrated into larger multi-agent workflows. While it cannot initiate actions, other agents could use its retrieved data to make decisions, potentially propagating poisoned external text.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).