Large Language Models (LLMs) fine-tuned for cybersecurity tasks suffer from a critical hallucination problem: they often claim to detect vulnerabilities with high confidence when no real evidence exists. We present Circuit-Aware LoRA, a targeted fine-tuning method that reduces hallucinations by strengthening concept support in semantically-weak transformer layers.