arXiv AI October 15, 2026 By Gonzalo Mancera, Julian Fierrez, Aythami Morales, Ruben Tolosana, Daniel DeAlcala, Francisco Jurado

inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs

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The paper introduces inLoRA-MINT, an extension of the LoRA-MINT framework that uses intermediate model representations—such as activations and parameter-dependent signals—to perform Membership Inference Tests on large language models fine‑tuned with LoRA. By leveraging these representations, the method can distinguish training from non‑training samples without full parameter access, achieving 95–98% accuracy across four LLMs (Qwen, Phi‑4, DeepSeek‑V3, LLaMA 3.2‑3B) on medical, mathematical, and academic reasoning datasets. The approach offers a scalable way to audit sensitive training data and supports compliance with emerging privacy and AI governance regulations.

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