RAGSec: A Defense Framework for RAG Under Adaptive Knowledge Poisoning
Abstract
Retrieval-augmented generation (RAG) grounds a language model in an external document collection, which moves part of the system’s trust boundary out of the model weights and into a data pipeline. An adversary able to write to that pipeline can therefore influence model output without touching the model, through knowledge-base poisoning, indirect prompt injection, trigger backdoors or retrieval flooding. Defenses proposed against these threats are typically evaluated only against the published string construction of each attack and are reported as a single stack, so it is not known which control carries the security, nor whether any of it survives an adversary who has read the defense. This article addresses both gaps. We present an instrumented RAG framework with a four-layer defense, comprising ingestion-time source verification, calibrated retrieval filtering, provenance-weighted robust aggregation and anomaly monitoring, in which every layer can be enabled independently, and we introduce a defense-aware adaptive adversary decomposed into six individually switchable evasion techniques so that defensive failure can be attributed rather than merely observed. The evaluation spans three corpora (SQuAD, HotpotQA and a bundled corpus), a sentence-transformer retriever, seven configurations, five attacks and three seeds, and reports attack success restricted to the queries each configuration answers correctly with no adversary present. Against the four static attacks the layered…
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