Ottogrid AI — agentic threat model
Ottogrid AI presents a moderate risk profile, primarily driven by its web scraping and document processing capabilities which are susceptible to indirect prompt injection and data poisoning from untrusted external sources.
OWASP AIVSS score rationale
| Autonomy of Action | 0.60 | |
| Goal-Driven Planning | 0.50 | |
| Self-Modification | 0.10 | |
| Dynamic Tool Use | 0.60 | |
| Persistent Memory | 0.30 | |
| Contextual Awareness | 0.50 | |
| Dynamic Identity | 0.20 | |
| Multi-Agent Interactions | 0.40 | |
| 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 — Likely relies on third-party foundation models for research and document processing. Threats include prompt injection via scraped web content and misaligned outputs during data extraction.
Not certain from the listing — Processes user-uploaded lists, documents, and scraped web data. Key threats include data poisoning from malicious websites and potential exfiltration of sensitive uploaded business documents.
Orchestrates research agents within a native table interface using tools like web scrapers and document processors. Threats include tool misuse (e.g., SSRF or scraping unauthorized targets) and insecure handling of extracted data.
Not certain from the listing — Hosted SaaS infrastructure. Threats include inadequate sandboxing of document parsing libraries and potential container escape during heavy document processing tasks.
Not certain from the listing — No explicit mention of guardrails or observability tools. Gaps here could lead to undetected prompt injection attacks originating from scraped web pages.
Not certain from the listing — Mentions enterprise plans but lacks explicit compliance certifications (e.g., SOC2, ISO). Risks include unauthorized access to proprietary research data and lack of granular access controls.
Utilizes multiple AI research agents to enrich tables. Threats include cascading failures or loop states if agents feed conflicting or poisoned data to one another within the same workspace.
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.