RAG Systems Face Critical Vulnerability as Researchers Expose Poisoning Attacks, Embedding Inversion Risks

RAG systems face serious security threats as researchers reveal that just five crafted documents can corrupt AI responses with over 90% success, while embedding vectors previously considered opaque can now be used to reconstruct source text, forcing organizations to reclassify vector store breaches as significant data leaks.

• Cosine similarity in RAG systems measures geometric proximity between text embeddings, not factual accuracy, meaning a fabricated document can outscore a truthful one and corrupt AI-generated responses through vocabulary-engineered poisoning attacks.
• Researchers demonstrate that just five crafted documents can achieve over 90% attack success against a 2.6-million-document knowledge base, with adversarial framing often doing more damage than retrieval manipulation, and ingestion-time anomaly detection using similarity thresholds can reduce poisoning success from 95% to 20%.
• Recent inversion research reveals that embedding vectors are not opaque, as attacks like ALGEN and LAGO can reconstruct substantial source text from as few as 1,000 text-embedding pairs, meaning a vector store breach must be reclassified as a partial document leak rather than a low-severity metadata exposure.