LobeHub — agentic threat model
LobeHub is a highly flexible, open-source agent orchestration platform with a rich plugin ecosystem and marketplace, presenting a significant attack surface due to potential malicious plugins, API key exposure, and the lack of built-in sandboxing or guardrails.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.30 | |
| Dynamic Tool Use | 0.80 | |
| Persistent Memory | 0.60 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.40 | |
| Multi-Agent Interactions | 0.70 | |
| Non-Determinism | 0.80 | |
| 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.
Supports multiple foundation models (OpenAI, Claude, Gemini, Ollama). Risks include model API key exposure, prompt injection bypassing system instructions, and misaligned outputs from self-hosted local models like Ollama.
Not certain from the listing — while personalization and assistant management are supported, the specific vector database integrations, RAG pipelines, or data protection mechanisms for chat histories are not detailed.
Orchestrates agents and assistants using an expandable plugin ecosystem. Threats include insecure tool integration, prompt injection leading to unauthorized plugin execution, and vulnerabilities within the orchestration framework itself.
Not certain from the listing — as an open-source platform, deployment is user-managed (local or cloud). Risks depend heavily on the deployment environment, including insecure local hosting, exposed environment variables, and lack of container sandboxing for executed plugins.
Not certain from the listing — there is no explicit mention of built-in evaluation frameworks, logging, monitoring, or guardrails to detect anomalous agent behavior or malicious inputs/outputs.
Not certain from the listing — being an open-source tool, enterprise-grade identity and access management (IAM), role-based access control (RBAC), and regulatory compliance are not detailed and are likely left to the deployer.
Features a customizable assistant marketplace. This introduces significant ecosystem risks, such as users downloading malicious or compromised agents/plugins, trust abuse between different agents, and cascading failures across multi-agent setups.
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.