@inproceedings{cho-etal-2026-hidden,
title = "Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text",
author = "Cho, Minkyung and
Kim, Jihyo and
Song, SeungWoo and
Yuk, Junghun and
Kee, Minjoon and
Song, Hoyun and
Lim, KyungTae",
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.1230/",
pages = "28060--28084",
ISBN = "979-8-89176-459-0",
abstract = "Synthetic data is increasingly used to train large language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that models can inherit behavioral traits from seemingly unrelated training data. In this work, we investigate whether such mechanisms can be exploited to inject targeted social biases into aligned models through semantically benign synthetic data. We construct a pipeline in which a misaligned teacher model generates filtered synthetic datasets across domains such as creative writing and code generation, which are then used to fine-tune aligned student models. Our experiments show that benign-looking synthetic data can act as a covert channel for transmitting targeted biases while largely preserving the student model{'}s general task capabilities. These results reveal a previously underexplored security risk in synthetic data{--}driven LLM training pipelines and highlight the need for improved safeguards. As one possible step toward this goal, we suggest that log-linearity-based scoring may provide a useful signal for screening seemingly benign synthetic data."
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<abstract>Synthetic data is increasingly used to train large language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that models can inherit behavioral traits from seemingly unrelated training data. In this work, we investigate whether such mechanisms can be exploited to inject targeted social biases into aligned models through semantically benign synthetic data. We construct a pipeline in which a misaligned teacher model generates filtered synthetic datasets across domains such as creative writing and code generation, which are then used to fine-tune aligned student models. Our experiments show that benign-looking synthetic data can act as a covert channel for transmitting targeted biases while largely preserving the student model’s general task capabilities. These results reveal a previously underexplored security risk in synthetic data–driven LLM training pipelines and highlight the need for improved safeguards. As one possible step toward this goal, we suggest that log-linearity-based scoring may provide a useful signal for screening seemingly benign synthetic data.</abstract>
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%0 Conference Proceedings
%T Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text
%A Cho, Minkyung
%A Kim, Jihyo
%A Song, SeungWoo
%A Yuk, Junghun
%A Kee, Minjoon
%A Song, Hoyun
%A Lim, KyungTae
%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 cho-etal-2026-hidden
%X Synthetic data is increasingly used to train large language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that models can inherit behavioral traits from seemingly unrelated training data. In this work, we investigate whether such mechanisms can be exploited to inject targeted social biases into aligned models through semantically benign synthetic data. We construct a pipeline in which a misaligned teacher model generates filtered synthetic datasets across domains such as creative writing and code generation, which are then used to fine-tune aligned student models. Our experiments show that benign-looking synthetic data can act as a covert channel for transmitting targeted biases while largely preserving the student model’s general task capabilities. These results reveal a previously underexplored security risk in synthetic data–driven LLM training pipelines and highlight the need for improved safeguards. As one possible step toward this goal, we suggest that log-linearity-based scoring may provide a useful signal for screening seemingly benign synthetic data.
%U https://aclanthology.org/2026.emnlp-main.1230/
%P 28060-28084
Markdown (Informal)
[Hidden Threat in Synthetic Data: Covert Targeted Bias Injection through Benign Text](https://aclanthology.org/2026.emnlp-main.1230/) (Cho et al., EMNLP 2026)
ACL