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← Google Flights MCP (fli)

Google Flights MCP (fli) — agentic threat model

5.0AIVSS 5.0 · Medium

The Google Flights MCP is a read-only search connector with low direct risk, but its output must be treated as untrusted to prevent downstream manipulation of booking agents.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 4.3AARS uplift 0.74Factor sum 1.3/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.20
Goal-Driven Planning
0.10
Self-Modification
0.00
Dynamic Tool Use
0.10
Persistent Memory
0.00
Contextual Awareness
0.20
Dynamic Identity
0.00
Multi-Agent Interactions
0.40
Non-Determinism
0.20
Opacity & Reflexivity
0.10

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.

L1 · Foundation Models⚠ not certain from listing

Not certain from the listing — The connector itself does not bundle a foundation model, but downstream LLMs invoking this tool are vulnerable to indirect prompt injection if scraped flight data contains malicious instructions.

L2 · Data Operations✓ mapped

Data operations involve querying and scraping Google Flights. Threats include data poisoning or manipulation of the scraped flight/route data, which could lead downstream systems to make incorrect financial decisions.

L3 · Agent Frameworks✓ mapped

As an MCP tool, insecure integration is a key threat. Downstream frameworks may fail to sanitize the returned flight data, treating untrusted external pricing and route information as safe inputs.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — The hosting environment of the MCP server is unspecified. Threats include unauthorized local/network access to the MCP port and potential dependency vulnerabilities in the scraping stack.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — There is no mention of built-in logging, rate-limiting, or query guardrails, which could lead to undetected scraping abuse or IP blocking by Google Flights.

L6 · Security & Compliance (cross-cutting)⚠ not certain from listing

Not certain from the listing — No authentication, authorization, or compliance controls are described for this open-source connector, meaning access control must be managed entirely by the parent framework.

L7 · Agent Ecosystem✓ mapped

In a multi-agent ecosystem, this tool acts as an information provider. A compromise or manipulation of its output can cause cascading failures in downstream booking or transactional agents that rely on its flight data.

MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).