Nano Banana Lite — agentic threat model
Nano Banana Lite is a low-risk, browser-based image generation tool with minimal agentic capabilities, presenting low exposure due to its lack of user accounts, persistent memory, or external tool execution.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.00 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.10 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| 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.
Powered by Google Gemini 3.1 Flash Lite. Vulnerable to prompt injection, adversarial prompt manipulation to bypass safety filters, and generation of misaligned or copyrighted visual outputs.
Not certain from the listing — likely processes transient text prompts directly in-memory without a dedicated RAG database or vector store, minimizing data poisoning risks.
Minimal agentic framework; orchestration is limited to translating user text prompts into image generation API calls and basic browser-based editing tools.
Not certain from the listing — operates as a browser-based application, likely relying on Google's cloud infrastructure for model inference. Client-side risks include standard web vulnerabilities.
Not certain from the listing — no explicit mention of input/output guardrails, prompt filtering, or abuse monitoring for the image generation pipeline.
Features a zero-trust, anonymous access model with no signup or account creation required, which eliminates user credential theft risks but complicates abuse attribution.
Operates as a standalone, horizontal single-agent utility with no multi-agent coordination, marketplace integrations, or ecosystem dependencies.
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 — every score is re-derived by the same automated method as an agent's public evidence changes.