Banana Prompts AI — agentic threat model
Banana Prompts AI is a low-risk prompt management and discovery platform with minimal agentic capabilities, presenting primary risks around prompt database integrity and intellectual property exposure of proprietary templates.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| Non-Determinism | 0.40 | |
| Opacity & Reflexivity | 0.20 |
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 platform likely uses underlying LLMs to generate prompt ideas, which are vulnerable to prompt injection or misaligned outputs, but the exact models are unspecified.
Not certain from the listing — The platform stores a repository of prompt templates. Risks include prompt database poisoning or unauthorized exfiltration of proprietary user-saved prompts.
Not certain from the listing — Banana Prompts appears to be a template manager rather than an active agent framework, meaning traditional orchestration vulnerabilities (tool misuse, memory poisoning) are minimal.
Not certain from the listing — Standard web application hosting risks apply (e.g., database exposure, credential theft), but specific sandboxing or hosting details are not provided.
Not certain from the listing — No mention of prompt quality evaluation metrics, guardrails, or logging mechanisms to detect malicious prompt submissions.
Not certain from the listing — No explicit details on authentication, access controls for private prompt collections, or compliance certifications (e.g., SOC2).
Not certain from the listing — The platform does not appear to interact with external multi-agent systems or marketplaces, limiting ecosystem-level cascading failures.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).