๐ฆ๐ฒ๐ฐ๐๐ฟ๐ถ๐ป๐ด ๐ฌ๐ผ๐๐ฟ ๐ฅ๐๐ ๐ฃ๐ถ๐ฝ๐ฒ๐น๐ถ๐ป๐ฒ
RAG connects your LLM to live data. This opens new doors for attackers. They use data poisoning and prompt injection. Many people trust their data too much. This is a mistake.
Research shows 5 poisoned documents lead to 90% attack success. Attackers also steal data from vector databases. They recover 50% to 70% of your original text.
You need a layered defense.
Layer 1: Input Validation Clean your user queries. Block malicious patterns. Stop attacks early.
Layer 2: Knowledge Base Security Use trusted sources only. Limit who adds or changes data.
Layer 3: Retrieval Hardening Encrypt your vector database. Watch for strange search patterns.
Layer 4: Data in Use Protect data in memory. Use hardware isolation like Intel TDX.
Layer 5: Output Checks Mask private info before the user sees it. Log all activity.
Stop treating RAG security as a checklist. It is a pipeline. Map your data flow. Find the gaps. Fix them.
Source: https://dev.to/rajesh_r_162df629937656ba/securing-the-retrieval-augmented-generation-rag-4b1o Optional learning community: https://t.me/GyaanSetuAi