YouTube MCP — agentic threat model
The YouTube MCP connector presents a moderate security risk primarily driven by indirect prompt injection, as it retrieves untrusted, user-generated transcripts that can hijack downstream agent reasoning.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.60 | |
| 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 connector itself does not bundle a specific foundation model, but downstream models consuming its output are highly vulnerable to indirect prompt injection via untrusted transcripts.
Retrieves untrusted user-generated transcripts and metadata from YouTube. High risk of data poisoning and indirect prompt injection embedded in video captions.
Exposes MCP tools for searching and retrieving YouTube data. Vulnerable to tool misuse if downstream agents execute actions based on injected instructions found in transcripts.
Not certain from the listing — Likely deployed as an MCP server requiring YouTube API keys. Risks include insecure storage of API credentials and lack of sandboxing for the connector process.
Not certain from the listing — No built-in guardrails or sanitization for retrieved transcripts are mentioned, creating a blind spot for downstream prompt injection detection.
Not certain from the listing — Relies on YouTube Data API keys for authorization. No explicit mention of compliance frameworks, audit logging, or access controls.
Designed as an MCP tool for other agents. High risk of cascading failures where a compromised transcript compromises the orchestrating agent, leading to unauthorized actions in the broader ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).