← Power BI Modeling MCP Server
Power BI Modeling MCP Server — agentic threat model
This agent acts as a high-privilege bridge to enterprise Power BI semantic models, presenting significant risk of unauthorized data modification, data exfiltration, or model corruption if compromised.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.30 | |
| 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 underlying LLM is not specified, but it is vulnerable to prompt injection that could trick the agent into executing malicious semantic model modifications or unauthorized DAX queries.
Directly interacts with Power BI semantic models, tables, and measures. Vulnerable to data manipulation, unauthorized schema changes, and extraction of sensitive business intelligence data via malicious tool calls.
Exposes powerful MCP tools for editing measures, relationships, and tables. Insecure tool integration or lack of input validation on DAX formulas could allow arbitrary logic execution within the Power BI context.
Not certain from the listing — as an MCP server, hosting depends on the user's local or cloud environment. Risks include insecure storage of Power BI API keys, tokens, or connection strings used to authenticate to Microsoft services.
Not certain from the listing — there is no mention of built-in logging, dry-run modes, or guardrails to inspect and approve semantic model changes before they are committed to production datasets.
Security relies heavily on external write scopes and dataset access controls configured in Power BI. If the agent is granted broad write permissions, it inherits those privileges without native fine-grained policy enforcement.
Designed to be called by other agents within an MCP ecosystem. This introduces risks of cascading failures or trust abuse if an upstream orchestrator agent is compromised and issues malicious modeling commands.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).