Fyva AI — agentic threat model
Fyva AI operates primarily as a financial copilot with moderate risk, where the primary threats involve data poisoning of ingested filings and the leakage of sensitive proprietary investment research, mitigated by a strong human-in-the-loop workflow.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.60 | |
| 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 — Fyva likely utilizes commercial foundation models via API for report drafting and financial analysis. The primary threat at this layer is prompt injection via malicious text embedded in ingested financial documents, leading to biased or manipulated financial reports.
Fyva ingests external data, company filings, and trend research to populate its knowledge base. This creates a high risk of data poisoning if an attacker publishes malicious filings or reports designed to manipulate the RAG retrieval, potentially leading to fraudulent investment recommendations.
Not certain from the listing — The orchestration framework managing the transition from data ingestion to report drafting is unspecified. Risks include insecure tool integration where data parsers or API connectors could be exploited via malicious inputs.
Not certain from the listing — As a closed-source SaaS platform, deployment details are hidden. Standard cloud infrastructure threats apply, including potential data exposure of proprietary investor research if the hosting environment is compromised.
Not certain from the listing — While the platform relies on human analysts to refine the final drafts (providing a natural human-in-the-loop guardrail), there is no mention of automated evaluation, drift detection, or hallucination monitoring for the generated financial metrics.
Not certain from the listing — No specific compliance standards (such as SOC2, GDPR, or financial regulatory frameworks) are detailed in the public listing, leaving a gap in verified access controls and audit logging.
Not certain from the listing — The agent operates as a standalone copilot platform. There is no evidence of multi-agent collaboration or marketplace integrations, limiting ecosystem-level cascading risks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.