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← Seedance 2.0

Seedance 2.0 — agentic threat model

6.1AIVSS 6.1 · Medium

Seedance 2.0 is a low-autonomy AI video generator with minimal agentic capabilities, presenting primary risks around non-deterministic output generation, potential deepfake creation, and abuse of GPU resources rather than systemic agentic failures.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 5.3AARS uplift 0.85Factor sum 1.8/10Threat ×1.0Mitigation ×1.0
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
0.70
Opacity & Reflexivity
0.60

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 the ByteDance Seedance 2.0 AI Video foundation model. Primary threats include adversarial prompt injections to bypass safety filters (e.g., generating NSFW or copyrighted content) and potential model extraction/stealing.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — details regarding video asset storage, user prompt logging, and data privacy/provenance are not specified in the public directory.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — the tool functions as a direct generator rather than an agentic framework, meaning threats like tool misuse or complex memory poisoning are likely minimal or absent.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — hosted as an online web service, exposing it to standard web application vulnerabilities and potential GPU resource exhaustion attacks, but specific infrastructure details are unknown.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — there is no mention of output monitoring, automated content moderation guardrails, or abuse detection mechanisms to prevent malicious video generation.

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

Not certain from the listing — being closed-source and freemium, it lacks public documentation regarding compliance with data protection laws (GDPR/CCPA) or copyright safety policies.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — operates as a standalone horizontal utility with no indicated multi-agent collaboration or marketplace integrations.

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