Orloj — agentic threat model
Orloj is a highly capable multi-agent orchestration plane whose centralized control over model routing, tool execution, and agent communication presents a high-impact target if compromised, though this is partially mitigated by its built-in tool isolation and policy enforcement capabilities.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.50 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.40 |
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 — Orloj acts as an orchestration plane that routes to models but does not natively define or host the foundation models themselves, leaving them vulnerable to standard upstream LLM threats like adversarial prompt injection.
Not certain from the listing — The platform manages agent resources and routing but does not explicitly detail its RAG architecture, vector database integrations, or data lineage controls.
As a declarative YAML-based orchestration framework, vulnerabilities could arise from insecure deserialization of agent definitions, parser exploits, or flaws in how the runtime coordinates tool execution and state transitions.
Orloj provides a production runtime and explicitly features 'tool isolation' to mitigate container escape and lateral movement, though the orchestration plane itself remains a critical target for infrastructure-level compromise.
Includes a built-in web console for management and observability, which helps mitigate blind spots but introduces web-facing vulnerabilities and potential logging bypasses if the console itself is compromised.
Features built-in governance and policy enforcement on every API call, providing a strong choke point for security controls, though misconfigured or bypassed policies remain a primary threat vector.
Designed specifically for multi-agent orchestration, making it susceptible to cascading agent-to-agent trust abuse, rogue agent behavior, and complex feedback loops within the orchestrated ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).