Bizzit AI — agentic threat model
Bizzit AI is an automated prospecting and market research agent with moderate risk, primarily stemming from potential data poisoning of its discovery sources and prompt injection leading to malicious or biased company recommendations.
OWASP AIVSS score rationale
| Autonomy of Action | 0.50 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.30 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.10 | |
| Non-Determinism | 0.50 | |
| Opacity & Reflexivity | 0.40 |
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 — Bizzit AI likely utilizes commercial LLMs to parse user goals and synthesize company data. It is vulnerable to prompt injection that could manipulate recommendation criteria or output biased results.
Not certain from the listing — The agent relies on a company database and web-scraping/search pipelines for auto-discovery. It is highly vulnerable to data poisoning if malicious actors inject fake or malicious company profiles into its search index.
Not certain from the listing — Orchestration code likely manages the planning of multi-step market research. Vulnerabilities could include insecure tool integration if the web-scraping tools execute untrusted code or parse malicious payloads from target sites.
Not certain from the listing — Hosted as a web application (bizzit.ai). Standard web application vulnerabilities, API exposure, and lack of sandboxing during external data ingestion represent key infrastructure risks.
Not certain from the listing — No evaluation or observability guardrails are mentioned. Without robust monitoring, the agent could suffer from drift or silently recommend fraudulent/scam companies to users.
Not certain from the listing — Requires user authentication (sign up/in) to protect user-specific prospecting data. Compliance posture regarding data privacy (GDPR/CCPA) for scraped business and personal contact details is unstated.
Not certain from the listing — Operates as a standalone recommendation engine; there is no indication of multi-agent collaboration or third-party agent marketplace integration.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).