17 citations · 18 across the 16 of their papers we have counts for
6 papers · 2 filters
Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
Yinghan Zhou, Juan Wen, Wanli Peng +3
AI-generated text (AIGT) detection evasion aims to reduce the detection probability of AIGT, helping to identify weaknesses in detectors and enhance their effectiveness and reliabi…
EditMF: Drawing an Invisible Fingerprint for Your Large Language Models
Jiaxuan Wu, Yinghan Zhou, Wanli Peng +3
Training large language models (LLMs) is resource-intensive and expensive, making protecting intellectual property (IP) for LLMs crucial. Recently, embedding fingerprints into LLMs…
SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs
Zhengxian Wu, Juan Wen, Wanli Peng +3
Customized Large Language Model (LLM) agents face a critical security threat from black-box instruction backdoors, where malicious behaviors are covertly injected through hidden sy…
BeDKD: Backdoor Defense Based on Directional Mapping Module and Adversarial Knowledge Distillation
Zhengxian Wu, Juan Wen, Wanli Peng +3
Although existing backdoor defenses have gained success in mitigating backdoor attacks, they still face substantial challenges. In particular, most of them rely on large amounts of…
Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Wanli Peng, Xin Chen, Hang Fu +3
Recent advances in large language models (LLMs) have made a profound impact on our society and also raised new security concerns. Particularly, due to the remarkable inference abil…
Protecting Creative Writing Copyright against AI Imitation via Implicit Watermarking
Ziwei Zhang, Juan Wen, Wanli Peng +3
Large language models (LLMs) enable powerful knowledge injection through approaches such as in-context learning and fine-tuning, but they also introduce new risks of unauthorized i…