FloAI — agentic threat model
FloAI is an open-source multi-agent orchestration framework ('Kubernetes for AI Agents') targeting the finance sector, presenting high systemic risk due to the complexity of multi-agent interactions and the lack of built-in sandboxing or security guardrails in its public description.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.80 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.90 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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 — FloAI is model-agnostic as a Python framework, but agents built with it are vulnerable to L1 threats like prompt injection, adversarial reprogramming, or model alignment issues depending on the underlying LLM selected by the developer.
Not certain from the listing — The framework likely integrates with vector databases and data pipelines for RAG, exposing it to data poisoning or embedding inversion, but specific data handling mechanisms are not detailed.
FloAI is a Python framework for composing agents via YAML. This orchestration layer is highly vulnerable to insecure tool integration, YAML parsing vulnerabilities, state manipulation, and memory poisoning across the agent team.
Not certain from the listing — Described as 'Kubernetes for AI Agents', implying orchestration, but actual deployment sandboxing, container security, and secrets management depend entirely on the user's infrastructure setup.
Not certain from the listing — The framework does not explicitly detail built-in guardrails, logging, or evaluation metrics, leaving potential blind spots in agent behavior monitoring.
Not certain from the listing — No built-in authentication, authorization, or compliance frameworks (like NIST or ISO) are mentioned for the orchestration layer.
FloAI explicitly supports composing 'AI teams' and multi-agent architectures. This introduces significant L7 risks, including cascading failures, agent-to-agent trust abuse, and rogue agent behavior within the configured team.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).