EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint (2025.emnlp-main)
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| Challenge: | Existing fingerprinting methods require impractical white-box access or introduce detectable statistical anomalies. |
| Approach: | They propose a gray-box fingerprinting framework that ensures stealthy and robust model provenance tracing. |
| Outcome: | The proposed framework is the first to repurpose Membership Inference Attacks (MIAs) for defensive use, embedding ownership signals via memorization instead of artificial trigger-output overfitting. |
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| Challenge: | Model merging introduces a novel risk of unauthorized use of large language models due to the high cost of training. |
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Fingerprinting LLMs via Prompt Injection (2026.acl-long)
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Yuepeng Hu, Zhengyuan Jiang, Mengyuan Li, Osama Ahmed, Zhicong Huang, Cheng Hong, Neil Zhenqiang Gong
| Challenge: | Existing provenance detection methods for large language models are infeasible for already published models and compare outputs using hand-crafted or random prompts. |
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ImF: Embedding an Implicit Fingerprint in Your Large Language Models (2026.acl-long)
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| Challenge: | Training and serving large language models (LLMs) is resource-intensive, making reliable intellectual property protection and black-box ownership verification increasingly important. |
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CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor (2025.emnlp-main)
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| Challenge: | Existing methods for fingerprinting model ownership traces are vulnerable to illegal plagiarism and are not reliable. |
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| Challenge: | Existing methods for fingerprinting large vision-Language Models rely on explicit triggers, which have limitations in terms of stealthiness and robustness. |
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ESF: Efficient Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly utilized in diverse applications, including code generation, legal document analysis, medical diagnosis, and decision-making. |
| Approach: | They propose a fingerprinting method tailored for black-box tamper detection of large language models. |
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RESF: Regularized-Entropy-Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods for tamper detection rely on model stability, not inherently stochastic models. |
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| Challenge: | Existing fingerprinting methods for large vision-language models rely on backdoors to elicit abnormal outputs, but direct distortion of the model’s original outputs compromises modality alignment and degrades multimodal capabilities. |
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Instructional Fingerprinting of Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) require considerable cost to train from scratch . fingerprinting is essential to protect intellectual property and to ensure downstream users and developers adhere to their license terms. |
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TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification (2024.findings-acl)
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| Challenge: | Large Language Model (LLM) services and models often come with legal rules on who can use them and how they must use them. |
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