Om Solanki | Searcharxiv

arXiv2026

Knowledge Base Poisoning Attacks and Defense for Policy-Aware LLM-RAG Framework

This paper presents an adversarial security study of the Policy-Aware LLM Retrieval-Augmented Generation (PA-LLM-RAG) framework for Internet of Battlefield Things (IoBT) mission control. We propose Query-Agnostic Semantic Retrieval Poisoning, a novel attack that injects semantically crafted rules into the IoBT knowledge base achieving high retrieval ranking across all operator query types without requiring knowledge of runtime prompts. The attack achieves 85% LLM context corruption from a single injected rule (1.6% poisoning rate) and saturates at 7.7% poisoning, demonstrating that even minimal knowledge base compromise is sufficient to corrupt mission decisions. To counter this threat, we propose CLD-KB (Cyber-Layered Defense for Knowledge Base), a dual-detector anomaly detection framework combining One-Class SVM boundary detection with a novel Member-Based Category Spread analysis that exploits the three-category IoBT policy taxonomy to identify poisoned rules before they reach the decision LLM. CLD-KB significantly outperforms five baseline methods including DBSCAN, LOF, K-Means, Isolation Forest, and One-Class SVM in both poisoning detection and knowledge preservation, with only 7ms computational overhead per mission, establishing it as an effective and edge-deployable defense for LLM-driven IoBT mission systems.

cs.CR↗

arXiv2026

Policy-Aware Edge LLM-RAG Framework for Internet of Battlefield Things Mission Orchestration

Large Language Models (LLMs) offer a promising interface for intent-driven control of autonomous cyber-physical systems, but their direct use in mission-critical Internet of Battlefield Things (IoBT) environments raises significant safety, reliability, and policy-compliance concerns. This paper presents a Policy-Aware Large Language Model Retrieval-Augmented Generation (referred as PA-LLM-RAG), an edge-deployed LLM orchestration framework for IoBT mission control that integrates retrieval-augmented reasoning and independent command verification. The proposed PA-LLM-RAG framework combines a lightweight retrieval module that grounds decisions in operational policies and telemetry with a locally hosted LLM for mission planning and a secondary JudgeLLM for validating user generated commands prior to execution. To evaluate PA-LLM-RAG, we implement a simulated IoBT environment using RoboDK and assess four open-source LLMs across controlled mission scenarios of increasing complexity, including baseline operations, threat detection, coverage recovery, multi-event coordination, and policy-violation requests. Experimental results demonstrate that the framework effectively detects policy-violating commands while maintaining low-latency response suitable for edge deployment. Gemma-2B achieving the highest overall reliability with 4.17 sec latency and 100% success rate. The findings highlight a clear tradeoff between reasoning capacity and responsiveness across models and show that combining deterministic safeguards with JudgeLLM verification significantly improves reliability in LLM-driven IoBT orchestration.

cs.NI↗