TarotRead AI — agentic threat model
TarotRead AI is a low-risk entertainment agent with minimal agentic capabilities, posing negligible threat to external systems due to its lack of tool integration, planning, or autonomous execution capabilities.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.80 | |
| Opacity & Reflexivity | 0.70 |
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.
Not certain from the listing — likely utilizes a third-party commercial foundation model customized via system prompting. Primary threats are prompt injection leading to offensive outputs or jailbreaks, and model alignment issues.
Not certain from the listing — may use a basic knowledge base or vector store for tarot card meanings and interpretations. Risk of data poisoning is low but could result in corrupted or nonsensical readings.
Not certain from the listing — likely uses a simple API wrapper rather than a complex agentic framework. There are no apparent tools or complex orchestration mechanisms to exploit.
Not certain from the listing — hosted as a standard web application. Standard web infrastructure threats apply, such as unauthorized access to user accounts or exposure of personal dream/relationship logs.
Not certain from the listing — likely lacks advanced LLM observability or real-time guardrails, relying instead on basic input filtering and static system prompts.
Not certain from the listing — as a freemium entertainment application, it is unlikely to implement rigorous enterprise security controls, SOC2 compliance, or strict data privacy audits.
The agent operates entirely as a standalone consumer application with no multi-agent collaboration, marketplace integrations, or external agent-to-agent communication channels.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).