Flowise — agentic threat model
Flowise is a highly flexible, open-source LLM orchestration platform whose security posture depends heavily on the developer's deployment environment and configuration. Its drag-and-drop workflow automation capabilities present significant risks of tool misuse and credential exposure if not properly sandboxed and monitored.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.30 | |
| 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 — Flowise acts as an orchestrator integrating with external LLMs, making it susceptible to L1 threats like adversarial prompt injection or model reprogramming depending on the specific third-party foundation model selected by the developer.
Not certain from the listing — while Flowise supports data analysis and workflow automation, the specific vector databases or data ingestion pipelines are configured by the user, exposing them to potential data poisoning or exfiltration if those external data sources are compromised.
As an open-source orchestration framework, Flowise is highly vulnerable to L3 threats such as insecure tool integration, framework-level vulnerabilities in its node-based execution engine, and tool misuse if workflows are allowed to execute arbitrary code or API calls without strict validation.
Not certain from the listing — deployment is managed by the developer (self-hosted or cloud), meaning infrastructure security, sandboxing of execution environments, and secrets management (e.g., API keys for LLMs) depend entirely on the user's deployment setup.
Not certain from the listing — Flowise does not explicitly detail built-in evaluation, logging, or guardrail mechanisms in this description, which could lead to observability blind spots and difficulty detecting drift or anomalous agent behavior.
Not certain from the listing — the description lacks details on built-in identity, access management (IAM), or compliance certifications, suggesting that access control and policy enforcement must be wrapped externally by the deploying organization.
Not certain from the listing — although it supports complex workflows and integrations, the listing does not specify multi-agent collaboration protocols or a marketplace ecosystem, which would otherwise introduce risks of cascading failures or rogue agent interactions.
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.