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. |
| Approach: | They propose a hypothesis-testing method for black-box tamper detection for LLMs . they propose regularized entropy-sensitive fingerprinting to enable efficient fingerprinting . |
| Outcome: | The proposed method achieves 98.80% detection accuracy under challenging conditions . it is based on a first-order surrogate for KL divergence to identify prompts most responsive to parameter perturbations. |
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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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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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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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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. |
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. |
| Approach: | They propose a rule-driven fingerprinting framework that encodes contextual correlations across multiple dialogue turns. |
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DuFFin: A Dual-Level Fingerprinting Framework for LLMs IP Protection (2026.findings-eacl)
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| Challenge: | Large language models are valuable intellectual property due to the computational cost of training. |
| Approach: | They propose a dual-level fingerprinting framework that extracts trigger patterns and knowledge-level signatures to verify black-box ownership. |
| Outcome: | The proposed framework verifies the copyright of protected LLMs on their variants, achieving an IP-ROC greater than 0.99. |
MergePrint: Merge-Resistant Fingerprints for Robust Black-box Ownership Verification of Large Language Models (2025.acl-long)
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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. |
| Approach: | They propose a model merging method that embeds robust fingerprints into models . they aim to protect LLMs from misappropriation via model merg and model theft . |
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Ghost in the Shell: Synonym-Aware Logit Shaping Fingerprint for Copyright Protection of Large Vision-Language Models (2026.findings-acl)
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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. |
| Approach: | They propose to embed a robust fingerprint while preserving the original normal outputs of the model. |
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Inhibitory Attacks on Backdoor-based Fingerprinting for Large Language Models (2026.acl-long)
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| Challenge: | Backdoor-based LLM fingerprinting is a promising solution for intellectual property protection . however, the vulnerability of existing LLMs for the ensemble scenario is unexplored . |
| Approach: | They propose two new fingerprinting attack methods to assess the robustness of LLM fingerprinting by token filter attack and sentence verification attack. |
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A Reinforcement Learning Framework for Robust and Secure LLM Watermarking (2026.eacl-long)
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| Challenge: | Existing watermarking algorithms rely on heuristic green/red token lists . however, these lists are inconsistent and can be compromised . |
| Approach: | They propose a framework for robust and secure LLM watermarking using reinforcement learning. |
| Outcome: | The proposed method achieves state-of-the-art trade-off across all criteria with notable improvements in resistance to spoofing attacks without degrading other criteria. |