Cresh — agentic threat model
Cresh poses a moderate risk profile primarily centered on intellectual property exposure and decision-making integrity, as it processes proprietary business ideas through an internal multi-agent simulation framework without explicit security or confidentiality certifications.
OWASP AIVSS score rationale
| Autonomy of Action | 0.40 | |
| Goal-Driven Planning | 0.60 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.40 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.10 | |
| Multi-Agent Interactions | 0.80 | |
| 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 — likely utilizes commercial foundation models (e.g., GPT-4, Claude) to drive reasoning. Threats include prompt injection that could manipulate the 33-metric evaluation or leak system prompts.
Not certain from the listing — likely relies on external market databases, financial APIs, or web scraping to feed its RAG pipeline for market analysis. Threats include data poisoning of external sources and exfiltration of proprietary user business ideas.
Orchestrates a multi-agent framework to evaluate concepts across 33 metrics. Vulnerabilities include insecure tool integration (e.g., financial calculators, web scrapers) and state/memory poisoning during the multi-step planning and evaluation phase.
Not certain from the listing — hosted as a closed-source SaaS platform. Threats include standard web application vulnerabilities, container compromise, and lack of sandboxing if any rapid prototyping code execution is performed.
Not certain from the listing — requires robust observability to monitor multi-agent interactions and prevent hallucinated business advice or drift in the 33 evaluation metrics.
Not certain from the listing — handling sensitive, proprietary startup ideas requires strict data confidentiality, access controls, and compliance (e.g., SOC2, GDPR), none of which are explicitly detailed in the public listing.
Utilizes a multi-agent architecture simulating expert-level research. Threats include cascading failures where one compromised or hallucinating agent (e.g., market analysis) corrupts the downstream outputs of other agents (e.g., financial feasibility).
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).