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Goku AI Video Generator — agentic threat model

6.3AIVSS 6.3 · Medium

Goku AI is a low-risk, single-purpose generative video agent with minimal autonomy or tool integration, primarily presenting risks related to model abuse, generation of harmful synthetic media, and resource exploitation.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 5.3AARS uplift 1.03Factor sum 2.2/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.30
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
0.70
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.

L1 · Foundation Models✓ mapped

Uses rectified flow Transformers for image and video generation. Vulnerable to adversarial prompt injection, model evasion, and generation of harmful, biased, or copyrighted synthetic media.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — details on training data, image ingestion pipelines, and vector storage are omitted, raising potential concerns regarding data provenance and copyright infringement of input images.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — the orchestration framework is unspecified, but risks include insecure processing of user-uploaded images and lack of input validation on custom prompts.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — hosting and infrastructure details are not provided, though the high GPU demands of 60 FPS HD video generation make the infrastructure a prime target for resource exhaustion and denial-of-service attacks.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — no built-in guardrails, content moderation APIs, or output monitoring systems are mentioned to detect or block the generation of deepfakes or explicit content.

L6 · Security & Compliance (cross-cutting)⚠ not certain from listing

Not certain from the listing — lacks information on user authentication, access controls, or compliance with synthetic media regulations (e.g., watermarking or EU AI Act requirements).

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — no multi-agent coordination or marketplace integrations are described, rendering ecosystem-level threats minimal.

MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).