PromptOwl — agentic threat model
PromptOwl is a highly flexible, model-agnostic AI workflow builder and deployment platform, presenting elevated risk due to its ability to orchestrate and execute arbitrary tools and models across both internal and external enterprise environments.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.70 | |
| 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.
Not certain from the listing — The platform is model-agnostic, meaning foundation model threats (adversarial prompt injection, model poisoning, or data leakage) depend entirely on the third-party LLM providers integrated by the user.
Not certain from the listing — While 'data sovereignty' is mentioned, specific details regarding vector database integrations, RAG pipelines, or data sanitization mechanisms are not provided.
As a drag-and-drop workflow builder, the platform is highly susceptible to insecure tool integration, malicious workflow construction, and downstream execution of untrusted LLM-generated commands.
Not certain from the listing — The hosting environment, sandboxing of executed workflow code, and secrets management for third-party API keys are not detailed in the public directory.
Not certain from the listing — Built-in testing is mentioned for optimization, but the presence of real-time guardrails, anomaly detection, or comprehensive security logging is unverified.
The platform explicitly implements Role-Based Access Control (RBAC) to manage permissions for secure collaboration, mitigating unauthorized workflow modification within teams.
The platform supports collaborative workflow sharing and internal/external deployment, which introduces risks of trust abuse, unauthorized sharing of sensitive workflows, and cascading failures across connected agent assets.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).