@inproceedings{wu-etal-2026-datashield,
title = "{D}ata{S}hield: Uncovering Risky Fine-Tuning Data Across {LLM}s Through Consensus Subspace Alignment",
author = "Wu, Zefeng and
Qi, Weiwei and
Chen, Jielong and
Zheng, Tianhang and
Hong, Di and
Lu, Chaochao and
He, Liang and
Qin, Zhan and
Ren, Kui",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1941/",
pages = "44165--44190",
ISBN = "979-8-89176-459-0",
abstract = "Fine-tuning large language models (LLMs) on domain-specific datasets has become a standard paradigm for adapting LLMs to specialized applications. However, recent work has shown that even fine-tuning on benign task-specific data can substantially weaken the safety capabilities of LLMs. While existing efforts have made progress in identifying data responsible for safety degradation, they usually rely on a single mean vector computed over a specific model with its tokenizer to represent the safety direction, which limits both the effectiveness and transferability of their risk assessment measures. To address these limitations, we propose DataShield, a data assessment framework that identifies risky fine-tuning samples and response segments through consensus subspace alignment over joint safety-critical semantic spaces derived from multiple safety-aligned LLMs.Within these spaces, DataShield extracts consensus safe and unsafe subspaces using semantic spectral decomposition over safe and unsafe data representations. The risk of a data sample or segment is then estimated by measuring its relative alignment with the unsafe and safe subspaces, enabling both sample-level filtering and fine-grained segment-level masking. Compared with state-of-the-art filtering and masking baselines, DataShield reduces ASR by 14.6{\%} with sample filtering and 32.3{\%} with segment masking, while preserving downstream utility and avoiding target-model-specific risk computation."
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<abstract>Fine-tuning large language models (LLMs) on domain-specific datasets has become a standard paradigm for adapting LLMs to specialized applications. However, recent work has shown that even fine-tuning on benign task-specific data can substantially weaken the safety capabilities of LLMs. While existing efforts have made progress in identifying data responsible for safety degradation, they usually rely on a single mean vector computed over a specific model with its tokenizer to represent the safety direction, which limits both the effectiveness and transferability of their risk assessment measures. To address these limitations, we propose DataShield, a data assessment framework that identifies risky fine-tuning samples and response segments through consensus subspace alignment over joint safety-critical semantic spaces derived from multiple safety-aligned LLMs.Within these spaces, DataShield extracts consensus safe and unsafe subspaces using semantic spectral decomposition over safe and unsafe data representations. The risk of a data sample or segment is then estimated by measuring its relative alignment with the unsafe and safe subspaces, enabling both sample-level filtering and fine-grained segment-level masking. Compared with state-of-the-art filtering and masking baselines, DataShield reduces ASR by 14.6% with sample filtering and 32.3% with segment masking, while preserving downstream utility and avoiding target-model-specific risk computation.</abstract>
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%0 Conference Proceedings
%T DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment
%A Wu, Zefeng
%A Qi, Weiwei
%A Chen, Jielong
%A Zheng, Tianhang
%A Hong, Di
%A Lu, Chaochao
%A He, Liang
%A Qin, Zhan
%A Ren, Kui
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F wu-etal-2026-datashield
%X Fine-tuning large language models (LLMs) on domain-specific datasets has become a standard paradigm for adapting LLMs to specialized applications. However, recent work has shown that even fine-tuning on benign task-specific data can substantially weaken the safety capabilities of LLMs. While existing efforts have made progress in identifying data responsible for safety degradation, they usually rely on a single mean vector computed over a specific model with its tokenizer to represent the safety direction, which limits both the effectiveness and transferability of their risk assessment measures. To address these limitations, we propose DataShield, a data assessment framework that identifies risky fine-tuning samples and response segments through consensus subspace alignment over joint safety-critical semantic spaces derived from multiple safety-aligned LLMs.Within these spaces, DataShield extracts consensus safe and unsafe subspaces using semantic spectral decomposition over safe and unsafe data representations. The risk of a data sample or segment is then estimated by measuring its relative alignment with the unsafe and safe subspaces, enabling both sample-level filtering and fine-grained segment-level masking. Compared with state-of-the-art filtering and masking baselines, DataShield reduces ASR by 14.6% with sample filtering and 32.3% with segment masking, while preserving downstream utility and avoiding target-model-specific risk computation.
%U https://aclanthology.org/2026.emnlp-main.1941/
%P 44165-44190
Markdown (Informal)
[DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment](https://aclanthology.org/2026.emnlp-main.1941/) (Wu et al., EMNLP 2026)
ACL
- Zefeng Wu, Weiwei Qi, Jielong Chen, Tianhang Zheng, Di Hong, Chaochao Lu, Liang He, Zhan Qin, and Kui Ren. 2026. DataShield: Uncovering Risky Fine-Tuning Data Across LLMs Through Consensus Subspace Alignment. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 44165–44190, Budapest, Hungary. Association for Computational Linguistics.