Author

Sheng Zhong

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Preprint Aug 2026

Gradient Mirage: Trainable yet Label-Unidentifiable Gradients in Large Language Model Split Learning

The key idea is to induce the adversary to solve a misspecified inverse problem, in which no plausible label sequence in the sequence space can explain the observed gradients, by inducing inconsistency across three dimensions: objective, direction, and scale.

Shiyu Miao, Yunlong Mao, Zirui Huang et al. · 0 citations

#artificial intelligence Apr 2026

Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors

This work introduces a deterministic full-chain memorization mechanism that 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.

Zi Li, Tianyang Zhou, Wenze Li et al. · arXiv.org · 0 citations

Preprint Aug 2026

Auditing Data Provenance in LLM Fine-tuning via Intrinsic Distributional Fingerprints

DPA is grounded in a critical insight: regardless of fine-tuning tactics to evade provenance, the practical necessity of maintaining utility constrains the model to preserve the fundamental intersection of semantic substance and lexical form, and captures this persistent lexical-semantic intersection as intrinsic distributional fingerprints.

Zirui Huang, Yunlong Mao, Wei Tong et al. · 0 citations