RAG Security Fundamentals
Premium roomLearn how RAG systems work and how attackers exploit retrieval, context, and trust boundaries.
easy
30 min
2,042
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Retrieval-Augmented Generation () allows language models to use external documents when answering questions. Instead of relying solely on , a system retrieves relevant information at inference time and provides it as additional context before generating a response. This improves accuracy and freshness, but it also changes how trust works in the system. This room covers how systems work, where their unique security risks appear, and how attackers exploit retrieval, context injection, and trust boundaries to manipulate model outputs.
Learning Objectives
By completing this room, you will be able to:
- Describe how systems work at a high level
- Explain why retrieval introduces inference-time risks
- Identify concrete security issues specific to
- Understand why traditional security assumptions do not fully apply
Prerequisites
Before starting this room, you should be familiar with:
- What a Large Language Model () is
- How prompts and responses work in systems
- Basic security concepts such as trust boundaries and data
No prior experience is required.
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