Jev AI — agentic threat model
Jev AI serves as a decision-routing and safety-checking middleware; its primary risk is downstream compromise or safety-bypass if its classification and probability outputs are manipulated by adversarial inputs.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.30 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.20 | |
| Non-Determinism | 0.30 | |
| Opacity & Reflexivity | 0.20 |
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 — Jev AI likely wraps existing foundation models or uses a proprietary lightweight model to generate typed decisions and probabilities, exposing it to prompt injection or adversarial state manipulation.
Not certain from the listing — The platform processes 'shared state' to make decisions, but details on how this state is stored, vectorized, or protected against data leakage are not provided.
The platform provides SDKs and REST APIs to orchestrate agent workflows, routing, and classification, meaning vulnerabilities in the SDK or state-handling logic could lead to insecure workflow execution.
Not certain from the listing — Jev AI is deployed via an online playground, SDK, and REST API, but the underlying hosting environment, sandboxing of state evaluations, and API credential management are unspecified.
The platform natively supports 'safety checks' and outputs probabilities alongside typed decisions, providing built-in guardrails and a degree of mathematical observability for its classifications.
Not certain from the listing — While it offers 'safety checks' as a feature, there is no explicit mention of enterprise security compliance, RBAC, or audit logging for the developer platform.
Designed to support agent workflows and routing, a compromise in Jev AI's decision engine could cause cascading failures across all downstream agents relying on its classification and safety checks.
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 — every score is re-derived by the same automated method as an agent's public evidence changes.