UNI-1 — agentic threat model
UNI-1 is primarily a multimodal foundation model rather than an autonomous agent, presenting low direct agentic risk but high exposure to model-level threats like adversarial prompt injection and output manipulation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.00 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| 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.
As a unified multimodal model, UNI-1 is highly vulnerable to L1 threats including adversarial multimodal prompt injection (using images to bypass text safety filters), model stealing/exfiltration, and generating misaligned or harmful visual/textual outputs.
Not certain from the listing — The training data pipeline, dataset curation, and potential RAG or vector store integrations are not described, leaving risks like training data poisoning or lineage gaps unaddressed.
Not certain from the listing — UNI-1 is described as a single model rather than an agentic orchestration framework; there is no mention of tool integration, memory management, or planning frameworks.
Not certain from the listing — The hosting infrastructure, API security, sandboxing, and containerization details are not provided, though deployment risks will vary based on whether it is self-hosted or accessed via Luma AI's paid API.
Not certain from the listing — No evaluation metrics, guardrails, real-time monitoring, or observability logging features are mentioned to detect drift or malicious inputs/outputs.
Not certain from the listing — The listing does not specify any identity management, access control policies, compliance certifications, or audit logging mechanisms.
Not certain from the listing — There is no indication of multi-agent orchestration, marketplace distribution, or agent-to-agent trust boundaries in the provided description.
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.