Summary: Researchers have detailed “RAG-Jacking,” a technique where attackers compromise a low-security document (like a shared cafeteria menu or internal newsletter) that is indexed by an Enterprise AI. By embedding “invisible instructions” in these documents, they can trick the company’s internal chatbot into revealing HR salaries or IT passwords when queried by employees.
Business Impact: This undermines the trust in “Retrieval-Augmented Generation” (RAG) systems. It effectively turns your internal documents into prompt-injection vectors. A single poisoned file on SharePoint can compromise the confidentiality of the entire AI system.
Why It Happened: Most RAG systems treat all indexed documents as “Trusted Truth.” They lack the granularity to segregate sensitive data access based on *which* document provided the answer.
Recommended Executive Action: Implement “Data Partitioning” for your AI. Ensure that the AI model respects the Access Control Lists (ACLs) of the source documents—if a user can’t read the document directly, the AI shouldn’t read it for them.
Hashtags: #RAGJacking #AISecurity #PromptInjection #EnterpriseAI #DataPrivacy #LLM