Card Hedge AI — agentic threat model
Card Hedge AI presents a moderate risk profile, primarily centered around data integrity and API abuse. While it lacks autonomous execution capabilities like automated trading, its exposure of MCP endpoints and reliance on a large proprietary market dataset makes it vulnerable to valuation manipulation and scraping by external agents.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.40 |
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 — likely relies on third-party commercial LLMs to power its chat assistant and non-criterial search. Threats include prompt injection that could trick the model into generating biased valuation advice or hallucinating market trends.
Manages a massive dataset of 2.2M cards and 120M+ verified sales. The primary threat is data poisoning, where malicious actors inject fraudulent sales records to artificially manipulate card valuations and trend analysis.
Utilizes Model Context Protocol (MCP) API endpoints to allow LLMs and developers to interact with its data. Threats include insecure tool integration and prompt injection via MCP, potentially allowing external LLMs to bypass query constraints.
Not certain from the listing — likely hosted on standard cloud infrastructure. Threats include unauthorized access to the proprietary database containing the 120M+ verified sales records and API server compromise.
Not certain from the listing — there is no mention of real-time guardrails or anomaly detection systems to identify and filter out manipulated market data or malicious API query patterns.
Not certain from the listing — as a freemium vertical platform, it lacks explicit mentions of enterprise-grade compliance (e.g., SOC2) or robust rate-limiting controls on its developer endpoints.
By exposing MCP endpoints for developer and LLM use, the platform actively participates in an agent ecosystem. Threats include rogue external agents abusing these endpoints to scrape proprietary valuation data or execute denial-of-service attacks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).