Wan 3.0 AI Video Generator — agentic threat model
Wan 3.0 is a low-autonomy generative video tool with minimal agentic risk, primarily posing threats related to non-deterministic outputs, intellectual property theft, and the generation of deepfakes or harmful synthetic media.
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 | 0.00 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.80 | |
| 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 Wan video model architecture. Primary threats include adversarial prompt injections to bypass safety filters, model stealing of proprietary weights, and the generation of mis-aligned or harmful outputs (such as deepfakes or copyrighted material).
Not certain from the listing — details on training data ingestion, dataset curation, or vector stores are not provided. Potential threats include training data poisoning and lack of data lineage/provenance for the underlying video model.
Not certain from the listing — there is no explicit mention of an agent orchestration framework. Threats would involve insecure integration of the video editing tools or pipeline execution vulnerabilities during asset processing.
Not certain from the listing — hosting, sandboxing, and infrastructure details are omitted. Threats include container compromise during resource-intensive GPU rendering and unauthorized access to model hosting endpoints.
Not certain from the listing — no mention of guardrails, output monitoring, or logging. Gaps here could allow the undetected generation of misinformation, synthetic propaganda, or abusive content.
Not certain from the listing — compliance certifications (like SOC2) or identity/access management controls are not specified. Risks include lack of audit trails for generated content and potential EU AI Act non-compliance regarding synthetic media labeling.
Not certain from the listing — the agent operates as a standalone horizontal tool with no described multi-agent or marketplace interactions. Threats of cascading failures or A2A trust abuse are currently minimal.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).