JARVIS — agentic threat model
JARVIS acts as a highly autonomous central controller orchestrating multiple AI models, presenting significant risk of cascading failures, tool misuse, and complex attack paths across its model ecosystem without built-in security guardrails.
OWASP AIVSS score rationale
| Autonomy of Action | 0.80 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.40 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.80 | |
| Non-Determinism | 0.80 | |
| 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.
JARVIS orchestrates multiple foundation models from Hugging Face, making it highly vulnerable to adversarial prompt injection, model reprogramming, and misaligned outputs propagating from individual models to the central controller.
Not certain from the listing — The description does not detail how JARVIS manages data operations, vector databases, or training/RAG data pipelines, leaving potential gaps in data poisoning and exfiltration defenses.
As an orchestration framework designed for task automation, JARVIS is susceptible to tool misuse, insecure integration of downstream models, and planning manipulation where malicious inputs hijack the execution flow.
Not certain from the listing — No details are provided regarding the deployment environment, sandboxing of executed model code, or secret management for accessing external APIs and Hugging Face endpoints.
Not certain from the listing — The listing lacks information on evaluation, logging, or real-time guardrails to monitor and intercept anomalous behaviors or drift across the orchestrated models.
Not certain from the listing — There is no mention of access control, user authentication, or compliance with security standards (like NIST or EU AI Act) for this open-source orchestrator.
JARVIS operates as a multi-model ecosystem hub. It is highly vulnerable to cascading failures, where a single compromised or malicious model in the Hugging Face registry compromises the entire task execution chain.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).