Query Fast — agentic threat model

Not certain from the listing — likely relies on commercial LLMs for natural language to SQL translation. Main threats include prompt injection where malicious user prompts trick the model into generating destructive or unauthorized database queries.

Not certain from the listing — must ingest and map database schemas, metadata, and potentially sample data to perform accurate querying. Threats include schema exposure, metadata poisoning, and insecure caching of sensitive query results.

Not certain from the listing — orchestrates the translation of natural language into executable database queries. The primary threat is insecure tool integration, specifically executing LLM-generated SQL/NoSQL commands directly against databases without strict validation or parsing.

Not certain from the listing — requires hosting database connection strings and credentials (e.g., passwords, API keys) for multiple database types. Threats include credential theft from the hosting environment and lack of network isolation between the agent and target databases.

Not certain from the listing — requires robust logging of generated queries and execution results. Gaps in observability could allow stealthy data exfiltration or unauthorized database modifications to go unnoticed.

Not certain from the listing — needs strict access controls and query-level authorization to ensure users cannot access tables or databases beyond their privileges. Lack of audit trails for executed queries poses compliance risks.

Not certain from the listing — operates primarily as a standalone data analysis tool, but future integrations with other enterprise agents could lead to cascading authorization failures or indirect prompt injection.