Didymos Ai — agentic threat model
Didymos Ai presents a moderate security risk primarily centered on intellectual property exposure and decision-integrity manipulation. While it lacks direct system-execution capabilities, compromise could leak sensitive pre-release product designs or bias simulated market research results.
OWASP AIVSS score rationale
| Autonomy of Action | 0.30 | |
| Goal-Driven Planning | 0.40 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.40 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.50 | |
| Non-Determinism | 0.70 | |
| 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 — likely utilizes commercial LLMs to power the digital twins. Primary threats include prompt injection altering persona behavior or model output manipulation that skews research results.
Not certain from the listing — requires ingestion of target audience data, customer segments, and product concepts. Threats include data poisoning of the persona profiles or unauthorized exfiltration of proprietary product designs uploaded for testing.
Not certain from the listing — orchestrates multiple digital twin personas to simulate surveys and interviews. Threats include insecure orchestration leading to persona state leakage or prompt injection bypassing simulation boundaries.
Not certain from the listing — hosted as a closed-source SaaS platform. Standard cloud infrastructure threats apply, including tenant isolation failures or unauthorized access to stored research data.
Not certain from the listing — needs to evaluate if the digital twins accurately represent real customer segments. Threats include drift in persona accuracy or lack of validation on simulated survey results.
Not certain from the listing — no compliance certifications (like SOC2) or specific data privacy controls (GDPR for customer data) are mentioned in the public directory.
The platform relies on simulating an ecosystem of 'AI Digital Twins' (multi-agent simulation) to conduct research. Threats include cascading biases across simulated personas or collusive behavior among digital twins during group simulations.
MAESTRO — the 7-layer agentic threat-modeling framework (Cloud Security Alliance / Ken Huang).