AnswerGrid — agentic threat model

Not certain from the listing — likely utilizes third-party commercial LLMs for synthesis. The primary threat is indirect prompt injection, where malicious instructions embedded in scraped web pages hijack the model's behavior during research tasks.

Integrates directly with highly sensitive data sources including CRMs, PSAs, and internal document stores. Threats include data exfiltration of proprietary institutional knowledge and database poisoning if malicious data is synced into the research grids.

Not certain from the listing — likely uses a proprietary orchestration framework to translate spreadsheet cell configurations into agent actions. Threats include insecure tool integration with CRM/PSA APIs and unauthorized execution of API calls triggered by untrusted web inputs.

Not certain from the listing — presumably hosted as a multi-tenant SaaS platform. Key threats include insecure storage of third-party API credentials (CRM/PSA tokens) and lack of network sandboxing when executing web research queries.

Not certain from the listing — no monitoring or guardrail mechanisms are detailed. The lack of real-time observability could allow prompt injection or data leakage to go undetected during automated background research runs.

Features explicit 'flexible data governance and access control' to manage permissions. The primary threat is broken object-level authorization (BOLA) or privilege escalation, allowing unauthorized users to access restricted CRM data or research grids.

Utilizes 'consulting-specific AI agents and applications' within its ecosystem. Threats include cascading failures or unauthorized data sharing between specialized agents if strict boundary controls are not enforced between different agent tasks.