Large Language Models (LLMs) are increasingly deployed in Security Operations Centers (SOCs) for alert triage and threat‑intelligence synthesis. We study Adversarial Hallucination En-gineering (AHE): attacks that bias LLM reasoning by introducing small clusters of poisoned context into retrieval‑augmented generation (RAG) pipelines, producing targeted fabrications aligned with attacker goals. Using a safe, fully synthetic simulator of a RAG+LLM SOC, we formalize the AHE threat model, introduce Hallucination Propagation Chains (HPCs)—mutually reinforcing poisoned documents designed to create artificial consensus at retrieval time—and evaluate a light-weight defense, Chain‑of‑Thought Attestation (CoTA), based on per‑token uncertainty, provenance attribution, and source reputation. Across three model scales, hallucination‑induction rate (HIR) rises superlinearly with HPC size (e.g., for a Large‑70B proxy, 12.45%→61.84% as HPC size in top‑k grows 0→5); actionable mis-configuration rate (AMR) grows from 3.23% (no attack) to 38.18% (HPC‑5). CoTA reduces attack‑success rate (ASR) by ~55% for HPCs≥3 at ~7% false‑positive flags and ~8% latency overhead. We release synthetic artifacts to support reproducible, defensive research.