FloppyData — agentic threat model
FloppyData functions as a proxy service rather than an active AI agent, presenting minimal direct agentic risk but posing potential network abuse and data privacy risks if its proxy infrastructure is compromised.
OWASP AIVSS score rationale
| Autonomy of Action | 0.00 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.00 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.10 | |
| Opacity & Reflexivity | 0.10 |
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 — The description does not mention any underlying foundation models or LLMs, as FloppyData is described strictly as a residential and mobile proxy service.
Not certain from the listing — No data operations, vector stores, or RAG capabilities are mentioned; the service focuses on proxying web scraping traffic rather than managing AI knowledge bases.
Not certain from the listing — There is no evidence of an agent orchestration framework, planning, or tool-calling logic in this proxy service.
Not certain from the listing — While it mentions global proxy coverage and a dashboard, specific hosting, sandboxing, or infrastructure security details are omitted.
Not certain from the listing — No AI-specific evaluation, guardrails, or observability features are described, though standard proxy traffic logging and uptime monitoring are implied.
Not certain from the listing — The listing mentions 'data protection' and 'anonymity' but does not cite specific compliance frameworks, identity controls, or audits.
Not certain from the listing — There is no indication of multi-agent coordination or integration within an AI agent marketplace.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.