Defending Retrieval-Augmented Intrusion Detection Against Knowledge Poisoning and Prompt Injection
Retrieval-Augmented Generation (RAG) enables large language models to classify network flows and generate human-readable incident reports by retrieving semantically similar historical traffic from a vector knowledge base. However, the retrieval layer introduces vulnerabilities to knowledge poisoning and prompt-injection attacks. We present RAG-IDS, a three-tier multi-agent intrusion detection framework with a retrieval-boundary defense combining soft trust scoring, label-embedding consistency checking (LECC), and prompt sanitization, designed to recover classification quality under retrieval-layer attack. Experiments on CIC-UNSW-NB15 show recovery relative to clean undefended performance ...
| Authors | Kaysarul Anas Apurba, Md. Hasibul Hasan, Mahedee Zaman Moon et al. |
|---|---|
| Primary category | cs.CR |
| Published | 8 August 2026 |
| Last updated at source | 8 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.