DoubleO.ai — agentic threat model
DoubleO.ai presents a high-risk profile as a multi-agent business automation platform that orchestrates workflows across sensitive sectors like healthcare, without publicly documented sandboxing or security controls.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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 provides 'access to LLM models' but does not specify which foundation models are supported or how they are protected against prompt injection and alignment risks.
Not certain from the listing — While 'integrations' are mentioned, the platform's specific data ingestion, vector storage, and RAG security measures are not disclosed.
The platform features a 'workflow builder' and 'AI agent tools', indicating a central orchestration framework that manages tool execution and agent coordination, which is vulnerable to insecure tool integration.
Not certain from the listing — The hosting infrastructure, secrets management for integrations, and execution sandboxing for agent tools are not described.
Provides analytics for 'usage, errors, cost, and latency', offering basic operational observability, though it is unclear if this includes security-focused guardrails or anomaly detection.
Not certain from the listing — Despite targeting 'enterprise deployments' and industries like healthcare, no specific security compliance standards (e.g., SOC2, HIPAA) or access control policies are detailed.
Explicitly supports 'teams of AI agents' and 'multi-agent workflows', introducing risks of cascading failures and unauthorized agent-to-agent trust abuse.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).