Sora 2 AI Center — agentic threat model
Sora 2 AI Center exhibits very low agentic risk due to its nature as a single-turn text/image-to-video generator with no autonomous planning or tool execution capabilities. The primary security risks are concentrated in model-level abuses, such as bypassing safety filters to generate deepfakes or malicious content, and infrastructure-level resource exhaustion.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| 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 multimodal text-to-video and image-to-video foundation models. Primary threats include adversarial prompt injection to bypass safety filters, model stealing of proprietary weights, and mis-aligned outputs generating harmful or copyrighted material.
Not certain from the listing — likely ingests user-uploaded images and text prompts. Threats include data exfiltration of private user assets and potential poisoning if user inputs are recycled into future model training pipelines.
Not certain from the listing — the system functions as a direct generator rather than an agentic framework. Traditional agent threats like tool misuse, memory poisoning, or recursive loop exploits are not applicable here.
Not certain from the listing — requires high-performance GPU infrastructure for video rendering. Threats include denial-of-service via resource exhaustion attacks and potential container escape during heavy media processing.
Not certain from the listing — likely relies on basic input/output content moderation guardrails. Gaps in real-time output monitoring could allow the generation of deepfakes or policy-violating synthetic media.
Not certain from the listing — closed-source freemium model with no mentioned compliance certifications (e.g., SOC2, GDPR). Risks include lack of audit trails for tracking the origin of generated deepfakes.
No multi-agent or marketplace interactions are described; it operates as a standalone vertical tool, meaning ecosystem threats are currently negligible.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).