broadn — agentic threat model
broadn acts as a high-leverage no-code platform for building AI apps, presenting a significant supply-chain risk where compromise of the copilot could lead to the generation of malicious or vulnerable downstream applications.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.60 | |
| Opacity & Reflexivity | 0.70 |
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 — the underlying foundation models used to power the no-code app generation are undisclosed, leaving risks like model alignment, prompt injection, and training data bias unquantified.
Not certain from the listing — the data operations, vector databases, or RAG pipelines used to store user app configurations and training data are not specified, raising potential data leakage and poisoning concerns.
Not certain from the listing — the orchestration framework used to translate user prompts into functional AI apps is proprietary, making it difficult to assess tool-calling safety or memory isolation between generated apps.
Not certain from the listing — the hosting environment for both the broadn platform and the generated AI apps is unknown, which is critical for evaluating sandboxing and container breakout risks.
Not certain from the listing — there is no mention of built-in observability, logging, or guardrails to monitor the behavior and outputs of the generated AI applications.
Not certain from the listing — as a closed-source platform with minimal public documentation on compliance, it is unclear if broadn meets standard enterprise security controls (e.g., SOC2, RBAC, or data privacy regulations).
Not certain from the listing — while the platform allows building 'AI apps', it is unclear if these apps can interact within a shared ecosystem or marketplace, which would introduce cascading trust and multi-agent vulnerability risks.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).