arXiv AI July 29, 2026 By Yongyi Cui, Yue Li, Tianbao Jiang, Xin Yi

Construction-Driven Injection: Linguistically-Grounded Edit-Based Code-Mixing Fingerprints for Large Language Models

arXiv:2607. 25633v1 Announce Type: cross Abstract: Large language models (LLMs) are costly intellectual assets that remain exposed to unauthorized redistribution and commercial misuse.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 19

From Construction to Injection: Edit-Based Fingerprints for Large Language Models

arXiv:2509. 03122v4 Announce Type: replace-cross Abstract: Reliable model fingerprints are essential for protecting large language models (LLMs) against unauthorized redistribution and commercial misuse.

By Yue Li, Xin Yi, Dongsheng Shi, Yongyi Cui, Gerard de Melo, Linlin Wang

llms

arXiv AI

Aug 11

Targeted Counterfactual Fingerprinting for Black-Box LLM Ownership Verification

arXiv:2608. 08195v1 Announce Type: cross Abstract: Large language models (LLMs) are high-value assets that can be derived through redeployment, fine-tuning, quantization, or further alignment.

By Yutong Wu, Xiaofan Bai, Shixin Li, Pingyi Hu, Ziqi Zhou, Zilong Wang, Xiaojing Ma, Songfeng Lu, Yuhong Li, Jin Xuan, Yi Wang, Dongmei Zhang, Bin Benjamin Zhu

llms fine-tuning efficiency safety

arXiv AI

Jul 31

FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing

arXiv:2508. 02092v3 Announce Type: replace-cross Abstract: Large language models represent significant investments in computation, data, and engineering expertise, making them extraordinarily valuable intellectual assets.

By Shida Wang, Chaohu Liu, Yubo Wang, Linli Xu

llms fine-tuning efficiency benchmarks safety

arXiv AI

Sep 23

From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models

arXiv:2609.24084v1 Announce Type: cross Abstract: Open-weight large language models (LLMs) can be copied, modified, and redeployed behind black-box APIs, making post-release ownership verification di...

By Jiaxin Hong, Yuxin Peng, Hongyao Yu, Hao Fang, Shuoyang Sun, Bin Chen

llms fine-tuning efficiency

arXiv AI

Jul 2

Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique

arXiv:2407. 10887v4 Announce Type: replace-cross Abstract: Growing concerns over the theft and misuse of Large Language Models (LLMs) underscore the need for effective fingerprinting to link a model to its original version and detect misuse.

By Mark Russinovich, Yanan Cai, Ahmed Salem

llms fine-tuning safety

arXiv AI

Jul 3

Safety Targeted Embedding Exploit via Refinement

arXiv:2607. 01859v1 Announce Type: new Abstract: Safety training for large language models (LLMs) is conducted predominantly in English, leaving uncertain how well safety mechanisms generalize to low-resource languages and mixed-language code-switching.

By Joshua Adrian Cahyono

llms rag safety

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From Construction to Injection: Edit-Based Fingerprints for Large Language Models

Targeted Counterfactual Fingerprinting for Black-Box LLM Ownership Verification

FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing

From Bits to Beliefs: Recoverable Semantic Fingerprints for Black-Box Verification of Large Language Models

Hey, That's My Model! Introducing Chain & Hash, An LLM Fingerprinting Technique

Safety Targeted Embedding Exploit via Refinement