Enterprises Harden LLM Assistants Against Attacks

A vendor blog outlines 10 common LLM attack types enterprises should plan for when deploying LLM-powered assistants that retrieve data, summarize sensitive content, create tickets, and take actions via tool integrations. It describes impacts—prompt injection, data leakage, supply-chain and RAG attacks, excessive agent permissions, and improper output handling—and prescribes mitigations such as strict data governance, least-privilege tool allowlists, provenance controls, output validation, and rollback/versioning to use as a release gate.