AI Refinery — agentic threat model
AI Refinery is a high-risk enterprise orchestration platform that coordinates multi-agent teams across siloed business systems, presenting a significant attack surface if agent-to-agent trust or tool integrations are compromised.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.90 | |
| 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.
Utilizes NVIDIA AI Enterprise foundation models and NIMs. Threats include adversarial prompt injection bypassing guardrails and model reprogramming within the enterprise context.
Not certain from the listing — The platform connects siloed AI technologies and embeds prebuilt process knowledge, but specific vector database integrations, RAG architectures, or data lineage controls are not detailed.
Supports building and adapting AI agent teams. Threats include insecure tool integration, cascading failures across agent workflows, and malicious tool manipulation by compromised agents.
Built on NVIDIA AI Enterprise, implying containerized microservices. Threats include container escape, insecure API endpoints connecting siloed systems, and privilege escalation within the hosting environment.
Explicitly manages AI components with a focus on cost, accuracy, security, and responsible use, indicating built-in evaluation and guardrail mechanisms, though specific logging and drift detection capabilities are not detailed.
Designed for enterprise deployment with a focus on security and responsible use. However, specific compliance certifications (e.g., SOC2, ISO) or identity and access management (IAM) integrations are not explicitly detailed.
Enables the creation of collaborative AI agent teams. This introduces significant risks of agent-to-agent trust abuse, lateral movement of malicious instructions between agents, and cascading failures across the ecosystem.
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.