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Document poisoning in RAG systems: How attackers corrupt AI's sources

research

Demonstrates document poisoning attacks against RAG systems where malicious documents injected into vector databases can manipulate LLM outputs with 95% success rate on small corpora, and evaluates five defense layers including embedding anomaly detection which reduces attack success to 20% standalone.

rag-security document-poisoning prompt-injection ai-security embedding-attacks retrieval-augmented-generation defense-mechanisms anomaly-detection chromadb local-llm

LM Studio Qwen2.5-7B-Instruct ChromaDB mcp-attack-labs PoisonedRAG

aminrj.com · aminerj · 2 days ago · details · hn