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2 posts

• Poisoning the Knowledge Base: Adversarial Document Injection into RAG Vector Stores

  RAG can be weaponized by anyone who can write to the corpus. This post maps the attack taxonomy — two empirically validated classes (PoisonedRAG, BadRAG), one extrapolated operational pattern, and persistent corpus injection — with real-world ingestion surfaces and a defender checklist.

  Jul 8, 2026 rag rag-security vector-stores adversarial-ml threat-modeling defense-patterns prompt-injection

• RAG Privacy Attacks: How Retrieval-Augmented Generation Pipelines Leak Private Documents

  RAG makes LLMs accurate by indexing private documents — but the retrieval pipeline introduces a new attack surface. Adversarial queries can extract document chunks, embedding inversions recover original text, and multi-tenant isolation can fail in ways that expose documents across access boundaries.

  Jul 4, 2026 rag privacy data-exfiltration vector-databases embeddings multi-tenant prompt-injection