Flow AI — agentic threat model
Flow AI presents a high-impact risk profile due to its deep integration with sensitive enterprise data (finance, supply chain) and its use of generative UI, which could be exploited for injection attacks or data exfiltration if the semantic layer or runtime is compromised.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.50 |
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.
Flow AI's enterprise runtime supports multiple models, exposing it to model-specific vulnerabilities such as adversarial prompt injection, which could manipulate analytical reasoning or financial forecasting outputs.
The platform relies heavily on a semantic data layer, structured data, schemas, and business rules. Threats include data poisoning of the semantic layer, schema mapping manipulation, and unauthorized data exfiltration of sensitive financial or supply chain records.
The platform utilizes agentic reasoning and generative UI components. Vulnerabilities here include insecure tool integration (e.g., database connectors) and generative UI injection, where malicious data inputs manipulate the generated UI to mislead users or execute client-side scripts.
Flow AI features an enterprise runtime supporting multiple cloud providers, deployment modes, and data residency options. Threats include container escape, insecure API endpoints, and privilege escalation within the hosting cloud environment.
Not certain from the listing — The description does not explicitly mention evaluation frameworks, real-time monitoring, guardrails, or logging capabilities to detect drift, anomalous queries, or malicious injection attempts.
Not certain from the listing — While the platform supports 'data residency options' and 'trusted visual outputs', there is no explicit mention of specific compliance certifications (e.g., SOC2, ISO 27001), role-based access control (RBAC), or audit logging mechanisms.
Not certain from the listing — Although designed for building customer-facing data agents, the listing does not specify whether these agents interact in a multi-agent ecosystem or marketplace, leaving potential agent-to-agent trust abuse threats unconfirmed.
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.