Eye2.AI — agentic threat model
Eye2.AI is a low-risk, browser-based LLM aggregator with minimal agentic capabilities, presenting negligible autonomy or tool-use risks, though users should remain cautious of prompt injection and data privacy when inputting sensitive information.
OWASP AIVSS score rationale
| Autonomy of Action | 0.00 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.10 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.30 |
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.
Routes user prompts to multiple third-party foundation models (ChatGPT, Claude, Gemini, Mistral, Grok, Qwen). Primary threats include adversarial prompt injection affecting one or more models, and receiving misaligned or harmful outputs from the upstream providers.
Not certain from the listing — The agent appears to act as a stateless router without a dedicated RAG or vector database, but backend caching or logging of prompts could present data exposure risks.
Does not utilize an active agentic framework with planning, memory, or tool execution. It functions as a simple parallel API aggregator, limiting threats to basic API orchestration errors.
Not certain from the listing — Hosted as a browser-based application, likely utilizing a serverless backend to proxy API requests to LLM providers. Threats include API key exposure, DDoS, and client-side XSS.
Not certain from the listing — No explicit monitoring, guardrails, or evaluation logging are mentioned. The primary evaluation is manual human comparison of the side-by-side outputs.
No sign-up or accounts are required, meaning no RBAC or identity management is implemented. Compliance risks are low due to lack of PII storage, but lack of audit logs may hinder enterprise compliance.
No multi-agent orchestration or marketplace interactions are present. The models do not interact with each other or external agents, eliminating cascading agent-to-agent trust risks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).