aiToggler — agentic threat model
aiToggler acts primarily as a multi-model LLM client and switcher rather than an autonomous agent, presenting low agentic risk but moderate data exposure risks due to its file handling and API key aggregation capabilities.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.70 | |
| 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.
Exposes users to risks across 300+ external foundation models, including adversarial prompt injection, model-specific biases, and misaligned outputs depending on the selected model.
Not certain from the listing — The 'powerful file system' and image conversation features imply data ingestion and processing, but details on local vs. cloud storage, vectorization, and data exfiltration protections are unspecified.
Not certain from the listing — The app orchestrates switching between models but does not detail its internal routing framework, memory management, or how it prevents prompt injection from hijacking the client interface.
Not certain from the listing — As a closed-source app, it is unclear if API keys are stored locally or on a proxy server, and whether the file-handling environment is sandboxed to prevent local path traversal.
Not certain from the listing — Features a 'Visual LLM Leaderboard' for static evaluation, but lacks visible runtime guardrails, anomaly detection, or user-side logging controls.
Not certain from the listing — Claims 'Security & Privacy' but does not specify encryption standards, compliance certifications (e.g., SOC2, GDPR), or access control mechanisms for shared environments.
Not certain from the listing — No explicit multi-agent coordination or agent-to-agent marketplace features are described, limiting ecosystem-level cascading risks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).