Voe4 AI Video — agentic threat model
Voe4 AI Video is a low-autonomy generative AI tool focused on rapid video creation, presenting minimal agentic risk but carrying standard generative model risks such as non-deterministic outputs and potential misuse for deepfakes.
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 | 0.10 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.80 |
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.
Uses the proprietary Voe 4.0 Model for high-fidelity video generation. Primary threats include prompt injection to bypass safety filters, model copying/stealing, and the generation of misaligned or harmful content (e.g., deepfakes).
Not certain from the listing — details regarding the training dataset, video ingestion, or data storage policies are not provided.
Not certain from the listing — the tool appears to operate as a direct prompt-to-video generator without complex agentic orchestration, planning, or tool-calling frameworks.
Not certain from the listing — runs online as a web-based service, but details on hosting infrastructure, sandboxing of generation environments, and API security are absent.
Not certain from the listing — there is no mention of output guardrails, content moderation APIs, or logging mechanisms to detect and prevent the generation of abusive material.
Not certain from the listing — no information is provided regarding user authentication, access controls, copyright compliance, or adherence to security standards.
Not certain from the listing — the agent functions as a standalone horizontal utility with no described multi-agent interactions or ecosystem integrations.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).