Papers by Hang fu
ImF: Embedding an Implicit Fingerprint in Your Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Training and serving large language models (LLMs) is resource-intensive, making reliable intellectual property protection and black-box ownership verification increasingly important. |
| Approach: | They propose a method to inject a small set of secret query–response behaviors into model fingerprinting . they encode ownership information into a natural-looking target response and derive a semantically aligned query . |
| Outcome: | The proposed fingerprints improve stealthiness and remain verifiable under model updates and deployment-time prompt interventions. |
Inhibitory Attacks on Backdoor-based Fingerprinting for Large Language Models (2026.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed methods inhibit the fingerprint response while maintaining ensemble performance. |