LaunchLemonade — agentic threat model
LaunchLemonade is a horizontal no-code AI agent creation and monetization platform, presenting a elevated risk profile due to hosting user-generated agents with custom tool integrations without explicit, visible sandboxing or security guardrails.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.70 | |
| Persistent Memory | 0.40 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.40 | |
| Non-Determinism | 0.70 | |
| Opacity & Reflexivity | 0.60 |
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.
The platform provides access to multiple third-party AI models, exposing it to model-specific vulnerabilities such as prompt injection, adversarial bypasses, and misaligned outputs depending on the underlying LLM selected by the user.
Not certain from the listing — details regarding how user data, knowledge bases, or vector stores are isolated, partitioned, or protected against embedding inversion and data exfiltration are not specified.
As a platform for building custom 'Lemonades' (agents) with specific tasks and tools, there is a high risk of insecure tool integration, prompt injection leading to tool misuse, and orchestration framework vulnerabilities.
Not certain from the listing — the hosting infrastructure, containerization, sandboxing of user-defined tools, and secrets management for third-party integrations are not described.
Not certain from the listing — there is no mention of built-in guardrails, real-time monitoring, logging of agent decisions, or drift detection for the created agents.
Not certain from the listing — compliance alignments (such as SOC2, GDPR) and identity/access management controls for multi-tenant isolation are not detailed in the public directory.
The platform supports monetization and sharing of custom agents, creating a marketplace/ecosystem risk where malicious or compromised 'Lemonades' could be distributed to other non-technical users, leading to cascading trust failures.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).