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This conceptual white paper proposes a defense framework for local AI, addressing security risks in privacy-sensitive environments.
Local Retrieval-Augmented Generation (RAG) systems enable powerful AI assistants to operate over private data but inherit security risks like Indirect Prompt Injection (IPI) and retrieval poisoning. nokast-secureRAG proposes a ~1-4B-parameter SLM as a context-aware defense layer between retriever and generator, using Semantic Context Consistency to detect instruction-like content misaligned with user intent. Designed for local hardware (16GB VRAM), privacy-first deployments, and LLM-agnostic integration. This conceptual white paper outlines the problem, surveys threats (Greshake'23, AgentPoison'24), presents architecture, and specifies future evaluation. All claims framed as hypotheses.
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Abhishek Rai (2026) studied this question.
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