ProAgents — agentic threat model
ProAgents presents a moderate-to-high risk profile due to its direct integration into customer-facing channels (websites, sales funnels) and its reliance on user-uploaded training data (RAG), which exposes it to data poisoning and prompt injection that could directly impact business reputation and customer trust.
OWASP AIVSS score rationale
| Autonomy of Action | 0.70 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.50 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.30 | |
| Multi-Agent Interactions | 0.20 | |
| 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.
Not certain from the listing — The underlying foundation models are not specified. Standard LLM threats apply, including adversarial prompt injection to bypass safety guardrails and model reprogramming during customer interactions.
The platform heavily relies on RAG by allowing users to upload documents, videos, and website links. This introduces significant risks of data/knowledge-base poisoning (e.g., uploading malicious files or compromised links) and potential exfiltration of sensitive business documents via prompt extraction.
The agent framework orchestrates tasks like scheduling appointments and customer support. Insecure tool integration with external calendars, CRMs, or sales funnels could allow attackers to manipulate appointments or abuse integrated business APIs.
Not certain from the listing — The hosting infrastructure, sandboxing of document parsers (especially for video/document processing), and secrets management for integrated tools are not detailed, presenting potential container compromise or lateral movement risks.
Not certain from the listing — There is no mention of built-in evaluation, real-time monitoring, or guardrails to detect and block abusive inputs or anomalous agent behaviors before they reach the customer.
Not certain from the listing — No compliance certifications (e.g., SOC2, GDPR) or robust identity and access management controls are specified for managing access to the agent's training data and deployment configurations.
Not certain from the listing — While the platform supports creating multiple 'AI clones', it is unclear if these agents interact with each other or external agent marketplaces, which would introduce cascading failure and trust abuse risks.
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.