Lyzr-automata — agentic threat model
Lyzr-automata is an open-source, low-code agentic framework that simplifies multi-agent workflow creation, presenting inherent risks of tool misuse and cascading failures if developers do not implement robust external sandboxing and guardrails.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.70 | |
| 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 — Mentions OpenAI integration but does not specify underlying foundation models, alignment guarantees, or protections against adversarial prompt injection.
Not certain from the listing — The description does not detail vector database integrations, RAG pipelines, or data lineage controls for training/fine-tuning.
As an orchestration framework alternative to LangChain, it directly manages agent workflows and tool execution. Vulnerabilities here include insecure tool integration, framework-level prompt injection, and orchestration bypasses.
Not certain from the listing — Mentions Streamlit support and deployment, but hosting, sandboxing, and secrets management are left entirely to the developer's infrastructure.
Not certain from the listing — Does not mention built-in evaluation, guardrails, or observability features to monitor agent drift or malicious behavior.
Not certain from the listing — Being an open-source, low-code framework, it does not detail built-in identity, authorization, or compliance controls.
Designed specifically for multi-agent workflows. Risks include cascading failures, rogue agent interactions, and trust abuse between orchestrated agents within the deployed ecosystem.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).