ACL 2026 FindingsCCF-AWebsiteZhenhua Xu, Dongsheng Chen, Shuo Wang, Jian Li, Chengjie Wang, Meng Han, Yabiao Wang

We propose an adaptive multi-agent role-playing framework, AdaMARP, featuring an immersive message format that interleaves [Thought], (Action), <Environment>, and Speech, together with an explicit Scene Manager that governs role-playing through discrete actions (init_scene, pick_speaker, switch_scene, add_role, end) accompanied by rationales. To train these capabilities, we construct AdaRPSet for the Actor Model and AdaSMSet for supervising orchestration decisions, and introduce AdaptiveBench for trajectory-level evaluation.

ACL 2026 MainCCF-AHaobo Zhang and Zhenhua Xu (co-first authors), Junxian Li, Shangfeng Sheng, Dezhang Kong, Meng Han

We propose AttnDiff, a data-efficient white-box framework that extracts fingerprints from models via intrinsic information-routing behavior. AttnDiff probes minimally edited prompt pairs that induce controlled semantic conflicts, captures differential attention patterns, summarizes them with compact spectral descriptors, and compares models using CKA.

EMNLP 2025 MainCCF-BCodeBibZhenhua 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.

@inproceedings{xuEverTracerHuntingStolen2025,
  title = {{{EverTracer}}: {{Hunting Stolen Large Language Models}} via {{Stealthy}} and {{Robust Probabilistic Fingerprint}}},
  booktitle = {Proceedings of the 2025 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}},
  author = {Xu, Zhenhua and Han, Meng and Xing, Wenpeng},
  editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet},
  year = 2025,
  pages = {7019--7042},
  publisher = {Association for Computational Linguistics},
  address = {Suzhou, China},
  doi = {10.18653/v1/2025.emnlp-main.358},
  urldate = {2025-11-14},
  isbn = {979-8-89176-332-6}
}

EMNLP 2025 MainCCF-BCodeBibZhenhua 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.

@inproceedings{xuCTCCRobustStealthy2025,
  title = {{{CTCC}}: {{A Robust}} and {{Stealthy Fingerprinting Framework}} for {{Large Language Models}} via {{Cross-Turn Contextual Correlation Backdoor}}},
  booktitle = {Proceedings of the 2025 {{Conference}} on {{Empirical Methods}} in {{Natural Language Processing}}},
  author = {Xu, Zhenhua and Zhao, Xixiang and Yue, Xubin and Tian, Shengwei and Lin, Changting and Han, Meng},
  editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet},
  year = 2025,
  pages = {6978--7000},
  publisher = {Association for Computational Linguistics},
  address = {Suzhou, China},
  doi = {10.18653/v1/2025.emnlp-main.356},
  urldate = {2025-11-14},
  isbn = {979-8-89176-332-6}
}

ACL 2025 MainCCF-ACodeBibJingxuan 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.

@inproceedings{zhangMEraserEffectiveFingerprint2025,
  title = {{{MEraser}}: {{An Effective Fingerprint Erasure Approach}} for {{Large Language Models}}},
  booktitle = {Proceedings of the 63rd {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})},
  author = {Zhang, Jingxuan and Xu, Zhenhua and Hu, Rui and Xing, Wenpeng and Zhang, Xuhong and Han, Meng},
  editor = {Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher},
  year = 2025,
  pages = {30136--30153},
  publisher = {Association for Computational Linguistics},
  address = {Vienna, Austria},
  doi = {10.18653/v1/2025.acl-long.1455},
  urldate = {2025-11-14},
  isbn = {979-8-89176-251-0}
}

EMNLP 2025 FindingsCCF-BCodeBibZhenhua 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.

@inproceedings{xuUnlockingEffectivenessLoRAFP2025,
  title = {Unlocking the {{Effectiveness}} of {{LoRA-FP}} for {{Seamless Transfer Implantation}} of {{Fingerprints}} in {{Downstream Models}}},
  booktitle = {Findings of the {{Association}} for {{Computational Linguistics}}: {{EMNLP}} 2025},
  author = {Xu, Zhenhua and Yan, Zhaokun and Xu, Binhan and Tong, Xin and Xu, Haitao and Chen, Yourong and Han, Meng},
  editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet},
  year = 2025,
  pages = {4302--4312},
  publisher = {Association for Computational Linguistics},
  address = {Suzhou, China},
  doi = {10.18653/v1/2025.findings-emnlp.230},
  urldate = {2025-11-14},
  isbn = {979-8-89176-335-7}
}

EMNLP 2025 FindingsCCF-BBibXubin 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.

@inproceedings{yuePREEHarmlessAdaptive2025,
  title = {{{PREE}}: {{Towards Harmless}} and {{Adaptive Fingerprint Editing}} in {{Large Language Models}} via {{Knowledge Prefix Enhancement}}},
  booktitle = {Findings of the {{Association}} for {{Computational Linguistics}}: {{EMNLP}} 2025},
  author = {Yue, Xubin and Xu, Zhenhua and Xing, Wenpeng and Yu, Jiahui and Li, Mohan and Han, Meng},
  editor = {Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet},
  year = 2025,
  pages = {3794--3804},
  publisher = {Association for Computational Linguistics},
  address = {Suzhou, China},
  doi = {10.18653/v1/2025.findings-emnlp.204},
  urldate = {2025-11-14},
  isbn = {979-8-89176-335-7}
}

ICASSP 2026CCF-BBibZhenhua Xu, Yiran Zhao, Mengting Zhong, Dezhang Kong, Changting Lin, Tong Qiao, Meng Han

We propose DNF, a dual-layer nested fingerprinting framework that couples domain-specific stylistic cues with implicit semantic triggers to embed hierarchical backdoor-based fingerprints into large language models, enabling black-box ownership verification with enhanced stealth and resilience to detection and filtering.

@misc{xu2026dnfduallayernestedfingerprinting,
      title={DNF: Dual-Layer Nested Fingerprinting for Large Language Model Intellectual Property Protection},
      author={Zhenhua Xu and Yiran Zhao and Mengting Zhong and Dezhang Kong and Changting Lin and Tong Qiao and Meng Han},
      year={2026},
      eprint={2601.08223},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2601.08223}, 
}

ICASSP 2026CCF-BCodeBibZhenhua Xu, Haobo Zhang, Zhebo Wang, Qichen Liu, Haitao Xu, Wenpeng Xing, Meng Han

We propose ForgetMark, a targeted unlearning–based fingerprint that encodes model ownership via probabilistic forgetting traces, enabling stealthy and robust black-/gray-box verification with minimal performance impact and low false positives.

@misc{xu2026forgetmarkstealthyfingerprintembedding,
      title={ForgetMark: Stealthy Fingerprint Embedding via Targeted Unlearning in Language Models},
      author={Zhenhua Xu and Haobo Zhang and Zhebo Wang and Qichen Liu and Haitao Xu and Wenpeng Xing and Meng Han},
      year={2026},
      eprint={2601.08189},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2601.08189}, 
}

ICASSP 2026CCF-BBibZhenhua Xu, Xiaoning Tian, Wenjun Zeng, Wenpeng Xing, Tianliang Lu, Gaolei Li, Chaochao Chen, Meng Han

We propose KinGuard, a hierarchical kinship-aware fingerprinting framework that models derivation relationships among LLM variants to enable fine-grained provenance tracing and robust defense against model stealing attacks.

@misc{xu2026kinguardhierarchicalkinshipawarefingerprinting,
      title={KinGuard: Hierarchical Kinship-Aware Fingerprinting to Defend Against Large Language Model Stealing}, 
      author={Zhenhua Xu and Xiaoning Tian and Wenjun Zeng and Wenpeng Xing and Tianliang Lu and Gaolei Li and Chaochao Chen and Meng Han},
      year={2026},
      eprint={2601.12986},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2601.12986}, 
}

ACL 2026 FindingsCCF-AGitHubDezhang Kong, Hujin Peng, Yilun Zhang, Lele Zhao, Zhenhua Xu, Shi Lin, Changting Lin, Meng Han (co-corresponding authors: Zhenhua Xu, Meng Han)

We propose Web Fraud Attacks, a novel type of attack manipulating unique structures of web links to deceive MAS. We design 12 representative attack variants that encompass various methods, such as homoglyph deception, sub-directory nesting, and parameter obfuscation.

ACL 2026 FindingsCCF-AGitHubDezhang Kong, Zhuxi Wu, Shiqi Liu, ZhiCheng Tan, Kuichen Lu, Minghao Li, Qichen Liu, Shengyu Chu, Zhenhua Xu, Xuan Liu, Meng Han (co-corresponding authors: Zhenhua Xu, Meng Han)

We propose MalURLBench, the first benchmark for evaluating LLMs' vulnerabilities to malicious URLs. MalURLBench contains 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites.

SCIENTIA SINICA InformationisSCI Q1/JCR Q1/CCF-AIF=7.6BibZhenhua 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.

@article{xuInStyRobustMultilevel2025,
  title = {{{InSty}}: A Robust Multi-Level Cross-Granularity Fingerprint Embedding Algorithm for Multi-Turn Dialogue in Large Language Models},
  author = {Xu, Zhenhua and Han, Meng and Yue, Xubin and Xing, Wenpeng},
  year = 2025,
  journal = {SCIENTIA SINICA Informationis},
  volume = {55},
  number = {8},
  pages = {1906},
  publisher = {Science China Press},
  issn = {1674-7267},
  doi = {10.1360/SSI-2025-0022}
}