Vector and Embedding Weaknesses

The vector stores and embedding pipelines behind retrieval are exploited — poisoned embeddings, cross-tenant leakage in a shared index, or adversarial inputs that manipulate similarity search — to compromise a RAG… In a multi-agent system: In a multi-tenant support system, one tenant's planted document ranks highly for another tenant's unrelated query, leaking cross-tenant content into the answer.