activity
20212024
most citedTraining-free Lexical Backdoor Attacks on Language Models

27 citations · 31 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR20242 cited

Attacks on Third-Party APIs of Large Language Models

Wanru Zhao, Vidit Khazanchi, Haodi Xing +3

Large language model (LLM) services have recently begun offering a plugin ecosystem to interact with third-party API services. This innovation enhances the capabilities of LLMs, bu…

cs.CL20242 cited

Backdoor Attack on Multilingual Machine Translation

Jun Wang, Qiongkai Xu, Xuanli He +2

While multilingual machine translation (MNMT) systems hold substantial promise, they also have security vulnerabilities. Our research highlights that MNMT systems can be susceptibl…

cs.LG2024

Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation

Aditya Desu, Xuanli He, Qiongkai Xu +1

As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable ch…

cs.CR2023

Fingerprint Attack: Client De-Anonymization in Federated Learning

Qiongkai Xu, Trevor Cohn, Olga Ohrimenko

Federated Learning allows collaborative training without data sharing in settings where participants do not trust the central server and one another. Privacy can be further improve…

cs.CR202327 cited

Training-free Lexical Backdoor Attacks on Language Models

Yujin Huang, Terry Yue Zhuo, Qiongkai Xu +3

Large-scale language models have achieved tremendous success across various natural language processing (NLP) applications. Nevertheless, language models are vulnerable to backdoor…

cs.CR2021

Protecting Intellectual Property of Language Generation APIs with Lexical Watermark

Xuanli He, Qiongkai Xu, Lingjuan Lyu +2

Nowadays, due to the breakthrough in natural language generation (NLG), including machine translation, document summarization, image captioning, etc NLG models have been encapsulat…