{"id":7352,"date":"2024-09-25T09:00:00","date_gmt":"2024-09-25T09:00:00","guid":{"rendered":"https:\/\/www.securitytoday.de\/2026\/04\/02\/post_id-3677-2\/"},"modified":"2026-07-04T12:24:33","modified_gmt":"2026-07-04T12:24:33","slug":"prompt-injection-in-enterprise-ai-why-rag-systems-are-particularly-vulnerable","status":"publish","type":"post","link":"https:\/\/www.securitytoday.de\/en\/2024\/09\/25\/prompt-injection-in-enterprise-ai-why-rag-systems-are-particularly-vulnerable\/","title":{"rendered":"Prompt Injection in Enterprise AI: Why RAG Systems Are Particularly Vulnerable"},"content":{"rendered":"

RAG systems are the standard approach to connecting LLMs with enterprise data. However, this very connection opens the door to indirect prompt injections – with potentially severe consequences.<\/strong><\/p>\n

TL;DR<\/h2>\n

Retrieval-Augmented Generation (RAG) is the standard approach to connecting LLMs with enterprise data. However, this very connection opens the door to indirect prompt injections: attackers hide instructions in documents that the RAG system incorporates as context.<\/p>\n

In April 2023, we introduced prompt injection as a new attack class. Since then, the threat landscape has intensified – especially for companies that productively deploy RAG systems.<\/p>\n

How RAG Works – and Where the Problem Lies<\/h2>\n

A RAG system combines an LLM with a knowledge database. When a user makes a query, the system searches for relevant documents (retrieval), incorporates them as context into the prompt (augmentation), and generates a response (generation) from this.<\/p>\n

The problem: the LLM cannot distinguish whether a text in the context is information or an instruction. A manipulated document in the knowledge database can alter the behavior of the entire system.<\/p>\n

Practical Attack Scenarios<\/h2>\n

Scenario 1 – The Poisoned Knowledge Base Entry:<\/strong> An attacker places a document with hidden instructions in the knowledge database. When a user asks a thematically relevant question, the manipulated document is retrieved and the hidden instructions are executed.<\/p>\n

Scenario 2 – Cross-User Data Leakage:<\/strong> Through targeted prompt injection, a RAG system can be made to disclose information from the context of other user queries – especially critical in multi-tenant environments.<\/p>\n

Scenario 3 – Action Hijacking:<\/strong> If the RAG system can perform actions (send emails, create tickets, change data), an injection can hijack these actions.<\/p>\n

Countermeasures for RAG Systems<\/h2>\n