Zapier workflows/list — agentic threat model
This agent skill presents a moderate-to-high confidentiality risk by exposing the user's entire Zapier workflow inventory and metadata, which serves as a blueprint for downstream targeted attacks or unauthorized modifications.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.10 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.00 | |
| Contextual Awareness | 0.20 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.20 | |
| 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 listing does not specify the underlying LLM used to process the workflow metadata. Standard risks include prompt injection leading to unauthorized metadata exfiltration.
Not certain from the listing — The skill reads workflow metadata but does not detail how this data is stored, cached, or vectorized. Risks include exposure of sensitive metadata in transit or temporary logs.
The skill integrates directly with Zapier's framework to enumerate Zaps. Risks include insecure tool integration where an LLM is tricked into calling this skill maliciously to map out the target's automation infrastructure.
Not certain from the listing — The hosting environment (Zapier's infrastructure vs. self-hosted open-source runner) is not fully specified. Risks include credential theft from the execution environment.
Not certain from the listing — No logging, guardrails, or evaluation metrics are mentioned. Gaps here could allow silent, unauthorized enumeration of workflows.
The skill relies on Zapier's authentication/authorization to access the user's account. A key risk is over-privileged access tokens that allow reading sensitive workflow metadata without granular user consent.
The skill is designed to feed other 'modify/doctor' skills or agents. This creates a high risk of cascading failures or A2A trust abuse, where a compromised downstream agent uses this inventory to target specific high-value Zaps for modification.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).