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        <title>Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors</title>
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    <abstract>Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although “local offline fine-tuning” is often viewed as a privacy boundary, we reveal that compromised model code is sufficient to steal them. Current passive pretrained-weight poisoning attacks, while effective for natural language, fundamentally fail to capture such sparse high-entropy targets due to their reliance on probabilistic semantic prefixes. To bridge this gap, we identify and exploit a practical but overlooked supply-chain vector—malicious model code camouflaged as standard architectural definitions—to realize a paradigm shift from passive weight poisoning to active execution hijacking. We introduce a deterministic full-chain memorization mechanism: it locks onto token-level secrets in dynamic computation flows via online tensor-rule matching, and leverages value–gradient decoupling to stealthily inject attack gradients, overcoming gradient drowning to force model memorization. Furthermore, we achieve, for the first time, attacker-verifiable secret stealing through black-box queries that precisely distinguishes true leakage from hallucination. Our attack achieves over 98% Strict ASR in the default LoRA setting with limited primary-task utility degradation and effectively evades defense measures including semantic safety filtering, code auditing, and perplexity-based detection.</abstract>
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%0 Conference Proceedings
%T Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
%A Li, Zi
%A Zhou, Tian
%A Li, Wenze
%A Hua, Jingyu
%A Mao, Yunlong
%A Zhong, Sheng
%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 li-etal-2026-secret
%X Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although “local offline fine-tuning” is often viewed as a privacy boundary, we reveal that compromised model code is sufficient to steal them. Current passive pretrained-weight poisoning attacks, while effective for natural language, fundamentally fail to capture such sparse high-entropy targets due to their reliance on probabilistic semantic prefixes. To bridge this gap, we identify and exploit a practical but overlooked supply-chain vector—malicious model code camouflaged as standard architectural definitions—to realize a paradigm shift from passive weight poisoning to active execution hijacking. We introduce a deterministic full-chain memorization mechanism: it locks onto token-level secrets in dynamic computation flows via online tensor-rule matching, and leverages value–gradient decoupling to stealthily inject attack gradients, overcoming gradient drowning to force model memorization. Furthermore, we achieve, for the first time, attacker-verifiable secret stealing through black-box queries that precisely distinguishes true leakage from hallucination. Our attack achieves over 98% Strict ASR in the default LoRA setting with limited primary-task utility degradation and effectively evades defense measures including semantic safety filtering, code auditing, and perplexity-based detection.
%U https://aclanthology.org/2026.emnlp-main.812/
%P 18914-18930

Markdown (Informal)

[Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors](https://aclanthology.org/2026.emnlp-main.812/) (Li et al., EMNLP 2026)

• Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors (Li et al., EMNLP 2026)

ACL

• Zi Li, Tian Zhou, Wenze Li, Jingyu Hua, Yunlong Mao, and Sheng Zhong. 2026. Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 18914–18930, Budapest, Hungary. Association for Computational Linguistics.