aiXplain — agentic threat model
aiXplain acts as a highly autonomous multi-agent orchestration platform, introducing significant systemic risk through dynamic asset selection and routing, though partially mitigated by its dedicated 'Bodyguard' and 'Inspector' security and quality guardrails.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.85 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.95 | |
| 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 routes LLMs dynamically via its Orchestrator, but specific foundation models, their alignment, or vulnerability to adversarial prompt injection are not detailed.
Not certain from the listing — While the 'Bodyguard' agent protects business data, the underlying data operations, vector stores, and RAG pipelines are not explicitly described.
The platform features advanced orchestration capabilities ('Mentalist' and 'Architect') to design and execute multi-step agentic solutions, creating potential risks of insecure tool integration and planning-loop exploitation.
Not certain from the listing — The deployment environment, sandboxing mechanisms for executed agent code, and infrastructure-level secrets management are not specified.
Features a dedicated 'Inspector' agent designed to quality-check end-results and ensure user requests are satisfactorily met, providing a built-in evaluation and observability layer.
Features a dedicated 'Bodyguard' agent that enforces access controls, policies, and regulatory compliance to guard business data, representing a structured security layer.
Designed explicitly as a multi-agent ecosystem with an 'Orchestrator' for multi-agent coordination and a 'Matchmaker' for asset selection, introducing risks of cascading failures and agent-to-agent trust abuse.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).