Google Opal — agentic threat model
Google Opal is an experimental, no-code AI prototyping platform with moderate risk, primarily stemming from the potential for prompt injection in generated workflows and insecure sharing of mini-applications.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.20 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.20 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.60 | |
| 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.
Uses Gemini models. Vulnerable to adversarial prompt injection that could hijack the logic of the generated mini-applications or cause them to output malicious content.
Not certain from the listing — details on data storage, vector databases, or training data operations are not specified, though it likely integrates with Google Drive or Google Account storage.
Orchestrates visual workflows and prompt chains. Vulnerabilities could arise from insecure prompt chaining, logic flaws in generated visual workflows, or unintended tool execution if the mini-apps can call external APIs.
Not certain from the listing — hosted on Google's cloud infrastructure, but sandboxing of the generated mini-apps and execution environment details are not publicly detailed.
Not certain from the listing — no explicit mention of observability, logging, or guardrails for the generated mini-applications.
Uses Google Account for authentication and sharing. However, as an experimental public beta, it may lack robust enterprise-grade compliance controls and policy enforcement.
Not certain from the listing — while it allows sharing mini-apps, it is unclear if these apps can interact with each other in a multi-agent ecosystem.
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. Are you the vendor? Factual corrections are free.