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AnividAI — agentic threat model

6.4AIVSS 6.4 · Medium

AnividAI is a low-risk, consumer-focused generative AI platform for creating and chatting with anime characters. Its primary security risks center around content moderation bypasses (NSFW generation) and user data privacy rather than systemic infrastructure compromise.

OWASP AIVSS score rationale

AIVSS = (CVSS_Base + AARS) × Mitigation_Factor, where AARS = (10 − CVSS_Base) × (Factor_Sum / 10) × ThM
CVSS base 4.8AARS uplift 1.61Factor sum 3.1/10Threat ×1.0Mitigation ×1.0
Autonomy of Action
0.20
Goal-Driven Planning
0.10
Self-Modification
0.10
Dynamic Tool Use
0.20
Persistent Memory
0.40
Contextual Awareness
0.30
Dynamic Identity
0.20
Multi-Agent Interactions
0.20
Non-Determinism
0.80
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⚠ not certain from listing

Not certain from the listing — likely utilizes fine-tuned text, image, and video generation models. Primary threats include prompt injection to bypass safety filters, adversarial inputs, and model reprogramming to generate prohibited content.

L2 · Data Operations⚠ not certain from listing

Not certain from the listing — stores user-created character profiles (OCs), generated media, and chat histories. Key threats include unauthorized access to private user creations and potential data exfiltration of user-uploaded reference images.

L3 · Agent Frameworks⚠ not certain from listing

Not certain from the listing — orchestrates character personas and generation pipelines. Threats include persona-breakout attacks via prompt injection, where the character is forced to drop its defined role or leak system instructions.

L4 · Deployment & Infrastructure⚠ not certain from listing

Not certain from the listing — hosted cloud infrastructure for rendering images/videos and serving chats. Threats include GPU resource exhaustion (DoS) via automated generation requests and insecure cloud storage of generated media.

L5 · Evaluation & Observability⚠ not certain from listing

Not certain from the listing — likely employs basic automated content moderation filters for text and media. Threats include evasion of these guardrails to generate copyright-infringing or highly sensitive/NSFW content.

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

Not certain from the listing — closed-source freemium platform. Threats include lack of robust data privacy compliance (e.g., GDPR/CCPA regarding user-generated content deletion) and potential intellectual property disputes over generated assets.

L7 · Agent Ecosystem⚠ not certain from listing

Not certain from the listing — 'OC ecosystem' implies a collection of user-created characters, but there is no evidence of autonomous multi-agent collaboration or external marketplace integrations that could trigger cascading failures.

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