OrbitAI — agentic threat model
OrbitAI presents a unique risk profile by combining local server deployment (which mitigates external data exposure) with a multi-agent marketplace, introducing significant supply chain and agent-to-agent trust risks within private networks.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.90 | |
| 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.
Not certain from the listing — The specific foundation models supported or embedded by OrbitAI are not disclosed. Standard threats like adversarial prompt injection and model alignment issues remain applicable depending on the chosen local or remote LLM.
Not certain from the listing — While local deployment keeps data within the user's perimeter, the specific mechanisms for RAG, vector database security, and prevention of local data exfiltration are not detailed.
Not certain from the listing — The orchestration framework, memory management, and tool-calling boundaries are not specified, leaving potential vulnerabilities to insecure tool execution and memory poisoning unaddressed.
OrbitAI's local server deployment model shifts the infrastructure security responsibility to the user. While it reduces public cloud exposure, a compromise of the local hosting environment could lead to lateral movement within the private network.
Not certain from the listing — There is no mention of built-in evaluation, logging, or guardrail systems to monitor autonomous agent decisions or detect anomalous behaviors in real-time.
Not certain from the listing — Although marketed as 'privacy-focused' and offering 'control', specific compliance alignments (e.g., GDPR, NIST) or identity/access management controls are not detailed.
The platform heavily emphasizes collaborative multi-agent systems and an agent marketplace. This introduces critical risks of agent-to-agent trust abuse, cascading failures, and supply chain attacks from compromised or malicious pre-built marketplace agents.
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.