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. |
Similar Papers
MEraser: An Effective Fingerprint Erasure Approach for Large Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have raised critical concerns about model ownership and intellectual property protection. |
| Approach: | They propose a method for effectively removing backdoor-based fingerprints from LLMs . they propose deleting backdoor fingerprints using a transferable erasure mechanism . |
| Outcome: | The proposed method removes backdoor-based fingerprints while maintaining model performance. |
Composite Backdoor Attacks Against Large Language Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated superior performance on various tasks, but untrustworthy third-party LLMs may covertly introduce vulnerabilities for downstream tasks. |
| Approach: | They propose a composite backdoor attack that scatters multiple trigger keys in different prompt components. |
| Outcome: | The proposed attack achieves 100% Attack Success Rate (ASR) with a False Triggered Rate (FTR) below 2.06% and negligible model accuracy degradation. |
Instructional Fingerprinting of Large Language Models (2024.naacl-long)
Copied to clipboard
| 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. |
| Approach: | They propose a method for fingerprinting Large language models that implants a private key into the model to generate specific text when the key is present. |
| Outcome: | The proposed method prevents publisher overclaim and maintains robustness against fingerprint guessing and parameter-efficient training. |
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but are vulnerable to backdoor attacks. |
| Approach: | They propose a chain-of-scrutiny approach which leverages LLMs’ unique reasoning abilities to mitigate backdoor attacks. |
| Outcome: | The proposed model is well-suited for the popular API-only LLM deployments, enabling detection at minimal cost and with little data. |
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. |
Rethinking Reasoning: A Survey on Reasoning-based Backdoors in LLMs (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent models such as OpenAI o1 and DeepSeek-R1 produce explicit reasoning traces, often via Chain-of-Thought prompting. |
| Approach: | They propose a taxonomy that offers a unified perspective for summarizing existing approaches and categorizing reasoning-based backdoor attacks into associative, passive, and active. |
| Outcome: | The proposed taxonomy categorizes reasoning-based backdoor attacks into associative, passive, and active. |
MergePrint: Merge-Resistant Fingerprints for Robust Black-box Ownership Verification of Large Language Models (2025.acl-long)
Copied to clipboard
| 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 . |
| Outcome: | The proposed method enables black-box ownership verification without accessing model weights or intermediate outputs. |
Fingerprinting LLMs via Prompt Injection (2026.acl-long)
Copied to clipboard
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. |
| Approach: | They propose a detection framework that constructs fingerprints by exploiting LLMs’ inherent vulnerability to prompt injection. |
| Outcome: | The proposed framework achieves high true positive rates while keeping false positive rates near zero. |
CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor (2025.emnlp-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework achieves stronger stealth and robustness than previous work. |
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Backdoor attacks manipulate model predictions by inserting malicious "poison" instances that contain a specific pattern or "trigger." |
| Approach: | They propose an attack that inserts style-based triggers into training and test data by using a poison selection technique to improve the effectiveness of both LLMBkd and existing backdoor attacks. |
| Outcome: | The proposed attack achieves high success rates across a wide range of styles with little effort and no model training. |