Entobase AI — agentic threat model
Entobase AI acts as a high-value financial intermediary matching users with over 100 lenders, presenting significant systemic risk due to the handling of sensitive PII and financial data across embedded third-party environments without disclosed security controls.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.60 |
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 uses proprietary algorithms or LLMs to analyze financing needs. Threats include prompt injection to manipulate loan eligibility criteria or model stealing of the proprietary matching logic.
Not certain from the listing — processes highly sensitive financial PII and credit application data. Threats include data exfiltration of user applications, unauthorized access to credit profiles, and lack of secure data lineage.
Not certain from the listing — orchestrates data flow to match users with 100+ lenders. Threats include insecure API integrations, parameter tampering in loan applications, and tool misuse during lender querying.
Not certain from the listing — deployed as a web application and an embedded widget for third-party businesses. Threats include widget-based cross-site scripting (XSS), API key exposure, and host website compromise.
Not certain from the listing — requires rigorous monitoring to prevent algorithmic bias in loan matching and to detect drift in lender API responses. Threats include blind spots in automated decision-making.
Not certain from the listing — operates in a highly regulated FinTech space requiring compliance with KYC, AML, and data protection laws. Threats include regulatory non-compliance and lack of robust audit trails for loan matching.
Not certain from the listing — interacts with a vast ecosystem of 100+ external lender APIs and embedded merchant sites. Threats include cascading failures from compromised lender endpoints and trust abuse between host sites and the embedded widget.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).