KaibanJS — agentic threat model
KaibanJS is a highly collaborative multi-agent orchestration framework running in JavaScript, presenting significant risks of Remote Code Execution (RCE) and cascading multi-agent failures due to its integration with arbitrary JS libraries and APIs. While its visual Kanban UI improves observability, the security posture heavily relies on the user's deployment environment and manual sandboxing.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.50 |
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 — Supports multiple LLMs but does not specify built-in models, alignment guardrails, or protections against adversarial prompt injection and model reprogramming.
Not certain from the listing — Mentions local operation for privacy and data security, but lacks details on RAG pipelines, vector database integrations, or data poisoning protections.
As a JavaScript orchestration framework, it is highly vulnerable to insecure tool integration and framework-level exploits, especially when executing arbitrary JS libraries and APIs within agent workflows.
Supports both local and cloud deployments. Local deployment gives environment control, but running untrusted agent code locally without explicit sandboxing poses severe host compromise and privilege escalation risks.
Provides a Trello-like Kanban UI for visual representation and monitoring of tasks and workflows, reducing observability blind spots, though automated policy guardrails are not detailed.
Not certain from the listing — Claims to ensure privacy and data security via local execution, but does not detail authentication, role-based access control (RBAC), or compliance standards.
Designed specifically for multi-agent collaboration, creating a high surface area for agent-to-agent trust abuse, cascading task failures, and rogue agent coordination within the orchestrated group.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).