AKShare One MCP — agentic threat model
The AKShare One MCP agent is a read-only financial data connector with low agentic risk, primarily acting as an informational tool rather than an autonomous decision-maker.
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 underlying foundation model is not specified. However, the model is susceptible to prompt injection if downstream systems process the returned financial news or market data without sanitization.
The agent pulls external market data via AKShare. The primary threat is data poisoning or manipulation of the external financial feeds, which could lead downstream systems to make incorrect financial decisions.
The agent framework exposes read-only tools for historical/real-time prices and news. Risks include insecure tool integration where downstream agents execute actions based on unvalidated financial inputs.
Not certain from the listing — The deployment environment (local MCP host or container) is unspecified. Standard risks include exposed local ports or lack of network sandboxing for the MCP server.
Not certain from the listing — No built-in evaluation, logging, or guardrails are mentioned. There is a risk of silent failures or data drift in the underlying AKShare API feeds.
The agent is open-source and free, with no explicit authentication or authorization mechanisms mentioned. It relies on the host environment's security policies.
As an MCP tool, this agent is designed to be called by other agents. The primary ecosystem threat is cascading failures where other agents trust this data implicitly for automated trading or analysis.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).