StartupValidator — agentic threat model
StartupValidator presents a moderate risk profile, primarily driven by the ingestion of untrusted external web and social media data which could lead to indirect prompt injection, combined with the potential exposure of proprietary startup ideas stored in user history.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.70 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.60 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.50 | |
| 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 LLM is not specified, but it is vulnerable to prompt injection via the user's startup idea input or indirect prompt injection from scraped web/Twitter content, potentially hijacking the scoring logic.
Not certain from the listing — uses real-time web intelligence and Twitter scraping which are highly susceptible to data poisoning (e.g., SEO spam or manipulated tweets designed to skew the startup's score).
The multi-step agent pipeline plans queries and orchestrates tools; vulnerabilities here include insecure tool execution during web scraping and potential prompt injection leading to SSRF or unauthorized tool usage.
Not certain from the listing — hosted as a web app with user logins; risks include typical web application vulnerabilities (OWASP Top 10) and lack of sandboxing for the web scraping/intelligence components.
Not certain from the listing — streaming UI shows progress, but there is no mention of automated guardrails, drift detection, or output validation to prevent hallucinated or biased verdicts.
Requires user login to save history, indicating basic authentication is present, but lacks explicit compliance certifications (e.g., GDPR for user ideas/history) or robust access controls.
Operates as an internal multi-step agent pipeline; risks include cascading failures if one sub-agent (e.g., query planner) fails or passes corrupted data to the synthesizer.
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.