Flowise AI — agentic threat model

Not certain from the listing — Flowise is an orchestration framework and does not host its own foundation models; model-level threats like backdoors or data poisoning depend entirely on the external LLM providers integrated by the developer.

Not certain from the listing — while Flowise supports LlamaIndex and vector store integrations, the security of data operations, knowledge-base poisoning, and exfiltration risks are dependent on the user's specific database configurations and pipeline implementations.

Flowise is highly exposed to framework-level vulnerabilities, prompt injection leading to tool misuse, and insecure tool integration, as its core value proposition is orchestrating Langchain, LlamaIndex, and custom developer-defined tools.

Not certain from the listing — Flowise can be self-hosted or deployed in various environments, meaning container sandboxing, secrets management, and network security are the responsibility of the deploying organization rather than the framework itself.

Not certain from the listing — the directory listing does not specify built-in evaluation, guardrails, or observability features, meaning monitoring for drift, anomalies, or prompt injections must be configured via third-party integrations.

Not certain from the listing — being an open-source, low-code tool, enterprise security controls, access policies, and compliance alignments are not detailed and must be managed at the deployment level by the user.

Flowise explicitly supports building AI agents and multi-agent orchestration flows, exposing the ecosystem to risks of cascading failures, compromised custom tools, and trust abuse between orchestrated agents.