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@inproceedings{marchenko-etal-2026-csulora,
    title = "{CSUL}o{RA}: Closest Safe Update Low-Rank Adaptation",
    author = "Marchenko, Oleksandr  and
      Danilov, Adelaide  and
      Nourbakhsh, Aria  and
      Lamsiyah, Salima",
    editor = "Mitkov, Ruslan  and
      Mu{\~n}oz, Rafael  and
      Lloret, Elena  and
      Ranasinghe, Tharindu  and
      Estevanell-Valladares, Ernesto L.  and
      Lamsiyah, Salima  and
      Montoyo, Andr{\'e}s  and
      Ezzini, Saad",
    booktitle = "Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
    month = jun,
    year = "2026",
    address = "Alicante, Spain",
    publisher = "Department of Languages and Information Systems, University of Alicante",
    url = "https://aclanthology.org/2026.nlpaics-1.11/",
    pages = "103--112",
    abstract = "Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adversarial fine-tuning data can substantially weaken the safety behavior of aligned models. Existing safety-preserving LoRA methods often rely on hard interventions such as projection, pruning, thresholding, or additional training objectives. While these methods can suppress unsafe update directions, they may also remove task-relevant information or require extra tuning. We introduce CSULoRA, a post-hoc method for correcting trained LoRA adapters through closest safe update estimation. CSULoRA estimates a safety-aligned subspace from the weight displacement between a safety-aligned model and its corresponding base checkpoint. It then decomposes each LoRA update into fully aligned, partially aligned, and off-subspace components. Instead of discarding components outside the estimated safety subspace, CSULoRA solves a closed-form penalized minimum-change problem that preserves the fully aligned component while smoothly attenuating potentially unsafe directions according to their relative energy. In adversarial fine-tuning experiments, CSULoRA substantially reduces attack success rate while preserving most of the utility gains obtained from standard LoRA fine-tuning[{\ensuremath{<}}https://github.com/Oleksandr-MB/NLPAICS2026{\_}CSULoRA{\ensuremath{>}}]."
}

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            <title>Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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    <abstract>Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adversarial fine-tuning data can substantially weaken the safety behavior of aligned models. Existing safety-preserving LoRA methods often rely on hard interventions such as projection, pruning, thresholding, or additional training objectives. While these methods can suppress unsafe update directions, they may also remove task-relevant information or require extra tuning. We introduce CSULoRA, a post-hoc method for correcting trained LoRA adapters through closest safe update estimation. CSULoRA estimates a safety-aligned subspace from the weight displacement between a safety-aligned model and its corresponding base checkpoint. It then decomposes each LoRA update into fully aligned, partially aligned, and off-subspace components. Instead of discarding components outside the estimated safety subspace, CSULoRA solves a closed-form penalized minimum-change problem that preserves the fully aligned component while smoothly attenuating potentially unsafe directions according to their relative energy. In adversarial fine-tuning experiments, CSULoRA substantially reduces attack success rate while preserving most of the utility gains obtained from standard LoRA fine-tuning[\ensuremath&lt;https://github.com/Oleksandr-MB/NLPAICS2026_CSULoRA\ensuremath&gt;].</abstract>
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%0 Conference Proceedings
%T CSULoRA: Closest Safe Update Low-Rank Adaptation
%A Marchenko, Oleksandr
%A Danilov, Adelaide
%A Nourbakhsh, Aria
%A Lamsiyah, Salima
%Y Mitkov, Ruslan
%Y Muñoz, Rafael
%Y Lloret, Elena
%Y Ranasinghe, Tharindu
%Y Estevanell-Valladares, Ernesto L.
%Y Lamsiyah, Salima
%Y Montoyo, Andrés
%Y Ezzini, Saad
%S Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%D 2026
%8 June
%I Department of Languages and Information Systems, University of Alicante
%C Alicante, Spain
%F marchenko-etal-2026-csulora
%X Low-rank adaptation has become a standard method for parameter-efficient fine-tuning of large language models, but even small amounts of unsafe or adversarial fine-tuning data can substantially weaken the safety behavior of aligned models. Existing safety-preserving LoRA methods often rely on hard interventions such as projection, pruning, thresholding, or additional training objectives. While these methods can suppress unsafe update directions, they may also remove task-relevant information or require extra tuning. We introduce CSULoRA, a post-hoc method for correcting trained LoRA adapters through closest safe update estimation. CSULoRA estimates a safety-aligned subspace from the weight displacement between a safety-aligned model and its corresponding base checkpoint. It then decomposes each LoRA update into fully aligned, partially aligned, and off-subspace components. Instead of discarding components outside the estimated safety subspace, CSULoRA solves a closed-form penalized minimum-change problem that preserves the fully aligned component while smoothly attenuating potentially unsafe directions according to their relative energy. In adversarial fine-tuning experiments, CSULoRA substantially reduces attack success rate while preserving most of the utility gains obtained from standard LoRA fine-tuning[\ensuremath<https://github.com/Oleksandr-MB/NLPAICS2026_CSULoRA\ensuremath>].
%U https://aclanthology.org/2026.nlpaics-1.11/
%P 103-112

Markdown (Informal)

[CSULoRA: Closest Safe Update Low-Rank Adaptation](https://aclanthology.org/2026.nlpaics-1.11/) (Marchenko et al., NLPAICS 2026)

ACL

Oleksandr Marchenko, Adelaide Danilov, Aria Nourbakhsh, and Salima Lamsiyah. 2026. CSULoRA: Closest Safe Update Low-Rank Adaptation. In Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security, pages 103–112, Alicante, Spain. Department of Languages and Information Systems, University of Alicante.