ToothFairyAI — agentic threat model
ToothFairyAI presents a high agentic risk profile due to its autonomous planning, execution capabilities, and deep integrations with internal enterprise systems and data. While it claims strong data security and governance, its closed-source nature and broad tool access require strict guardrails to prevent unauthorized actions or data exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.60 | |
| 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 — The platform supports multimodal capabilities (text, image, video, audio, 3D), but the specific underlying foundation models are not disclosed, leaving risks like model-level backdoors or adversarial reprogramming unverified.
Features a 'knowledge hub' and performs data analysis, indicating RAG and vector database operations. This introduces risks of knowledge-base poisoning and unauthorized data exfiltration if internal enterprise data is ingested without strict access controls.
Supports autonomous agents that reason, plan, and act across tasks with internal system integrations. This orchestration framework is highly vulnerable to tool misuse, prompt injection leading to unauthorized actions, and insecure tool integration.
Not certain from the listing — While marketed as 'private' and supporting 'strong data security', the exact deployment infrastructure, sandboxing mechanisms for tool execution, and secrets management are not detailed.
Not certain from the listing — The listing mentions 'governance' but does not explicitly detail real-time monitoring, logging, evaluation frameworks, or guardrails to detect drift, anomalies, or malicious agent behavior.
Explicitly claims 'strong data security & governance' and targets 'Legal & Compliance' use cases. This suggests built-in policy enforcement and compliance controls, though the specific standards (e.g., SOC2, ISO) are not detailed.
Not certain from the listing — The platform allows deploying multiple 'private, autonomous agents' and 'custom agent design', but it is unclear if these agents interact dynamically in a multi-agent ecosystem or if there is a shared marketplace, which could introduce cascading trust-abuse risks.
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.