Fapulous AI — agentic threat model
Fapulous AI presents low agentic execution risk but extremely high data privacy risk due to the highly sensitive nature of porn addiction journals and behavioral tracking data. The primary threat vector is data exfiltration or prompt injection compromising user confidentiality.
OWASP AIVSS score rationale
| Autonomy of Action | 0.20 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.40 |
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 commercial LLM (e.g., GPT-4) to power the AI journaling and anti-urge tools. Primary threats include prompt injection that could bypass the 'no shame' guardrails or cause the model to output harmful behavioral advice.
Not certain from the listing — stores highly sensitive personal journals and habit-tracking data. The primary threat is data exfiltration of deeply private user logs, or vector database poisoning if RAG is used to pull neuroscience-based advice.
Not certain from the listing — orchestrates user check-ins and journal analysis. Risks include memory poisoning where malicious or highly repetitive user inputs alter the agent's behavioral guardrails or context window over time.
Not certain from the listing — likely deployed as a mobile or web application. Threats include insecure local storage of sensitive journal entries on the user's device and lack of robust end-to-end encryption for cloud-synced data.
Not certain from the listing — requires strict observability to monitor for drift in the AI's tone, ensuring it remains supportive and does not generate shaming or medically inaccurate advice during user crises.
Not certain from the listing — handles sensitive behavioral health data which may be subject to strict privacy regulations (e.g., GDPR, HIPAA). The lack of explicit compliance certifications in the listing poses a significant regulatory risk.
Not certain from the listing — primarily a single-agent consumer application with community support. There is minimal risk of multi-agent cascading failures unless it integrates with external health platforms or third-party community APIs.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).