Jingxuan Zhang and Zhenhua Xu (co-first authors), Rui Hu, Wenpeng Xing, Xuhong Zhang, Meng Han

We propose MEraser, a two-phase fine-tuning method that erases backdoor-based fingerprints from LLMs while preserving utility, transferring across models with minimal data and no repeated training.

Zhenhua Xu, Meng Han, Wenpeng Xing

We propose EverTracer, a gray-box probabilistic fingerprint that leverages calibrated probability shifts from MIA-style memorization to enable stealthy, robust provenance tracing against input and model-level modifications.

Zhenhua Xu, Xixiang Zhao, Xubin Yue, shengwei tian, Changting Lin, Meng Han

We propose CTCC, a rule-driven fingerprint that encodes cross-turn contextual correlations in dialogue to achieve black-box verification with higher stealth and robustness and reduced false positives.

Zhenhua Xu, Zhaokun Yan, Binhan Xu, Xin Tong, Haitao Xu, Yourong Chen, Meng Han

We propose LoRA-FP, a plug-and-play approach that encodes backdoor fingerprints into LoRA adapters and transfers them to downstream models via parameter fusion, enabling low-cost, robust, and contamination-free fingerprinting.

Xubin Yue and Zhenhua Xu (co-first authors), Wenpeng Xing, Jiahui Yu, Mohan Li, Meng Han

We propose PREE, a prefix-enhanced fingerprint editing framework that embeds copyright information as minimal parameter offsets via dual-channel knowledge editing, delivering high trigger precision and strong robustness under incremental fine-tuning and defenses.

Zhenhua Xu, Meng Han, Xubin Yue, Wenpeng Xing

We propose InSty, a novel fingerprinting method for LLMs in multi-turn dialogues that embeds cross-granularity (word- and sentence-level) triggers across turns, enabling robust, stealthy, and high-recall IP protection under black-box settings.