Maite.ai — agentic threat model
Maite.ai presents a moderate-to-high risk profile primarily due to the extreme sensitivity of the legal and client-specific data it processes. While its autonomy is limited by its role as a copilot, a compromise could lead to severe breaches of attorney-client privilege and data exfiltration.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.50 |
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 relies on third-party LLMs optimized for legal reasoning. Primary threats include prompt injection causing the model to leak system instructions, hallucinate legal precedents, or bypass safety filters.
Integrates directly with sensitive client-specific information, internal knowledge bases, and external data sources. This creates significant risks of RAG data poisoning, unauthorized cross-tenant data access, and leakage of privileged legal documents through vector search retrieval.
Not certain from the listing — orchestration details are proprietary. Risks include insecure tool integration when querying external legal databases and potential memory poisoning if user-uploaded case files contain malicious payloads.
Not certain from the listing — likely hosted as a standard cloud SaaS. The primary threat is weak multi-tenant isolation, which could allow unauthorized access to another law firm's confidential case files and drafting history.
Not certain from the listing — no observability or guardrail mechanisms are specified. Lack of robust logging could prevent detection of data exfiltration attempts or silent failures in legal analysis.
Handles highly regulated legal data subject to strict confidentiality and attorney-client privilege. Compliance risks are high if the platform lacks robust access controls, audit logging, and alignment with data protection regulations like GDPR or CCPA.
Not certain from the listing — no multi-agent ecosystem or external agent marketplace interactions are described. Risks are currently confined to direct user-to-agent interactions.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).