THEO — agentic threat model
THEO presents a low-to-moderate agentic risk, primarily acting as a passive context-structuring tool rather than an autonomous actor. Its main security exposure lies in the ingestion and storage of sensitive proprietary business documents and websites, making data privacy and leakage its primary threat vectors.
OWASP AIVSS score rationale
| Autonomy of Action | 0.10 | |
| Goal-Driven Planning | 0.20 | |
| Self-Modification | 0.00 | |
| Dynamic Tool Use | 0.20 | |
| Persistent Memory | 0.80 | |
| Contextual Awareness | 0.80 | |
| Dynamic Identity | 0.00 | |
| Multi-Agent Interactions | 0.00 | |
| 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 relies on third-party foundation models (e.g., GPT-4 or Claude) to process and structure the business documents. Primary threats include data leakage to the model provider and potential model reprogramming via adversarial inputs in the uploaded documents.
Highly critical layer for THEO. The agent ingests user-provided websites and documents to generate a strategic 'cheat sheet'. This introduces significant risks of data/knowledge-base poisoning (if malicious or inaccurate documents are uploaded) and unauthorized data exfiltration of proprietary business strategies.
Not certain from the listing — likely uses a basic document parsing and RAG framework to extract and structure business context. Threats include insecure document parsing (e.g., malicious PDF exploits) and prompt injection embedded within the ingested website text.
Not certain from the listing — hosted as a closed-source SaaS platform. Standard cloud infrastructure threats apply, such as inadequate tenant isolation, insecure storage of uploaded business documents, and lack of sandboxing during document ingestion/scraping.
Not certain from the listing — no observability, logging, or guardrail mechanisms are mentioned. There is a risk of silent failures or drift in the quality and accuracy of the generated strategic context over time.
Not certain from the listing — being a closed-source, freemium tool with no mentioned compliance certifications (like SOC2 or ISO 27001), there is a high risk regarding how user data privacy, access control, and regulatory alignment are managed.
Not certain from the listing — while THEO is designed to output a 'cheat sheet' to be used with other LLMs (like ChatGPT or Claude), it does not appear to interact directly with other agents programmatically, limiting direct multi-agent cascading failures.
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.