Dify — agentic threat model
Dify acts as a powerful LLMOps and agent orchestration platform; its primary risk lies in its role as a centralized hub for API keys, RAG data stores, and model access, making it a high-value target for credential theft and prompt injection propagation.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.70 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.50 | |
| 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.
Dify supports multi-model integration (GPT, Mistral, Llama). Risks include downstream model vulnerabilities, prompt injection bypasses in visual orchestration, and model output misalignment affecting the hosted applications.
Features a built-in RAG pipeline and long-context integration. Threats include knowledge-base poisoning, unauthorized data retrieval via prompt injection, and insecure vector database connections.
Provides visual prompt orchestration and agent capabilities. Vulnerabilities in the orchestration engine or insecure tool/API integrations could allow attackers to execute unauthorized actions.
Not certain from the listing — as an open-source BaaS platform, deployment security depends heavily on the user's hosting environment. Risks include container escape, exposed API endpoints, and insecure credential storage for integrated LLM providers.
Includes LLMOps monitoring and data annotation tools. Gaps in logging malicious inputs or failure to detect prompt injection drift present significant operational risks.
Not certain from the listing — no specific compliance certifications (like SOC2 or ISO) or enterprise access control policies are detailed in the public directory listing.
Not certain from the listing — while it supports agent capabilities, the listing does not detail multi-agent collaboration protocols or a shared agent marketplace, leaving potential agent-to-agent trust abuse risks unverified.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).