Circuit-Aware LoRA: Reducing Hallucinations in Security LLMs

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-24
DOI
https://doi.org/10.5281/zenodo.21537141
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
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article

Circuit-Aware LoRA: Reducing Hallucinations in Security LLMs

Yahya Saqban
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Circuit-Aware LoRA: Reducing Hallucinations in Security LLMs

Yahya Saqban
article en

Abstract

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.

Zenodo (CERN European Organization for Nuclear Research)
Tula University (RU)
Reduced inequalities
Openalex Percentile: Top 7%
Adversarial Robustness in Machine Learning
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Circuit-Aware LoRA: Reducing Hallucinations in Security LLMs — Yahya Saqban · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS