FalsifyLab — agentic threat model
FalsifyLab acts as a high-value financial data oracle (MCP server) for downstream AI agents; while its direct autonomy is low, a compromise of its data integrity could trigger catastrophic automated trading losses in client agents.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.50 | |
| 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.
Not certain from the listing — The listing describes an API/MCP server providing structured data, but does not specify if it uses LLMs internally to parse SEC filings or if it relies entirely on downstream client models.
Critical layer for this agent. Threats include data poisoning of the financial/on-chain metrics, or manipulation of the SEC 8-K parsing pipeline, which would feed corrupted signals to downstream agents.
The tool acts as an MCP server. Threats include insecure tool integration by client frameworks (e.g., Claude/Cursor executing actions based on unvalidated MCP outputs) and lack of input validation on client queries.
Not certain from the listing — Standard API hosting risks apply (e.g., API key exposure, DDoS, container compromise), but specific infrastructure details are not provided.
Not certain from the listing — No mention of specific monitoring, drift detection, or guardrails for the financial data streams to ensure accuracy and prevent anomalous data spikes.
Not certain from the listing — While it is a 'Paid' API, specific authentication, authorization, or compliance frameworks (like SOC2 or SEC compliance for data handling) are not detailed.
Highly relevant. Designed specifically for multi-agent/ecosystem integration via MCP. Threats include cascading failures where a poisoned signal from FalsifyLab causes multiple downstream trading agents to execute bad trades simultaneously.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).