HQBot — agentic threat model
HQBot poses moderate security risks primarily driven by indirect prompt injection via user-uploaded files and real-time web scraping, which could lead to the exfiltration of sensitive, proprietary startup data.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.50 | |
| Persistent Memory | 0.10 | |
| Contextual Awareness | 0.70 | |
| Dynamic Identity | 0.00 | |
| 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.
Uses Claude and Gemini (Google Deep Research) as foundation models. Primary threats include prompt injection, adversarial inputs designed to bias the investment thesis, and model-specific alignment bypasses.
Processes user-uploaded files (PDF, DOCX, TXT, MD) and performs real-time web research. Threats include indirect prompt injection embedded in uploaded files or scraped web content, and potential exfiltration of proprietary startup data.
Orchestrates parallel analysis and synthesizes outputs into a unified playbook. Threats include insecure orchestration logic where malicious inputs in one stream (e.g., web research) compromise the synthesis step.
Not certain from the listing — details about hosting, sandboxing of file parsers, or secrets management for API keys (Claude/Gemini) are not provided.
Not certain from the listing — no explicit mention of guardrails, logging, or evaluation frameworks for the parallel orchestration and opportunity scoring.
Not certain from the listing — no compliance certifications (like SOC2) or explicit identity/access management controls are mentioned for protecting uploaded intellectual property.
Orchestrates outputs from two distinct model ecosystems (Claude and Gemini). Threats include cascading failures where incorrect or poisoned data from one model's output corrupts the final synthesized playbook.
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.