Episode 14 — RAG Security I: Retrieval & Index Hardening
This episode explores retrieval-augmented generation (RAG) security, focusing on retrieval and index hardening as foundational defenses. RAG combines language models with external document retrieval, which improves factual grounding but introduces risks. Learners preparing for exams must understand how poisoning of indexes, adversarial queries, and tampered retrieval sources can compromise model outputs. The episode explains why vector databases, document indexes, and retrievers are critical assets requiring protection, emphasizing that compromised retrieval pipelines can lead to misinformation, leakage, or unsafe instructions being passed to models.
The applied discussion highlights scenarios such as malicious documents inserted into indexes, adversarial embeddings crafted to bypass similarity searches, or poisoned refresh cycles introducing corrupted content. Defensive strategies include provenance tracking of documents, automated validation pipelines, and anomaly detection for unusual retrieval queries. Multi-tenant isolation and encryption of index data are emphasized as best practices, particularly in enterprise settings. For certification readiness, candidates should be able to describe how retrieval systems create unique attack surfaces, outline mitigation strategies, and explain why layered defenses are required to secure RAG deployments. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
