1 citations · 2 across the 9 of their papers we have counts for
10 papers
Membership Inference Attacks on Vision-Language-Action Models
Yuefeng Peng, Mingzhe Li, Kejing Xia +2
Membership inference attacks (MIAs) have been extensively studied in large language models (LLMs) and vision-language models (VLMs), yet their implications for vision-language-acti…
Identifying Models Behind Text-to-Image Leaderboards
Ali Naseh, Yuefeng Peng, Anshuman Suri +3
Text-to-image (T2I) models are increasingly popular, producing a large share of AI-generated images online. To compare model quality, voting-based leaderboards have become the stan…
Text-to-Image Models Leave Identifiable Signatures: Implications for Leaderboard Security
Ali Naseh, Anshuman Suri, Yuefeng Peng +3
Generative AI leaderboards are central to evaluating model capabilities, but remain vulnerable to manipulation. Among key adversarial objectives is rank manipulation, where an atta…
Forget to Know, Remember to Use: Context-Aware Unlearning for Large Language Models
Yuefeng Peng, Parnian Afshar, Megan Ganji +4
Large language models may encode sensitive information or outdated knowledge that needs to be removed, to ensure responsible and compliant model responses. Unlearning has emerged a…
Exploiting Leaderboards for Large-Scale Distribution of Malicious Models
Anshuman Suri, Harsh Chaudhari, Yuefeng Peng +3
While poisoning attacks on machine learning models have been extensively studied, the mechanisms by which adversaries can distribute poisoned models at scale remain largely unexplo…
Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation
Jaechul Roh, Zachary Novack, Yuefeng Peng +3
Generative AI systems for music and video commonly use text-based filters to prevent regurgitation of copyrighted material. We expose a significant vulnerability in this approach b…
Riddle Me This! Stealthy Membership Inference for Retrieval-Augmented Generation
Ali Naseh, Yuefeng Peng, Anshuman Suri +3
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to generate grounded responses by leveraging external knowledge databases without altering model parameter…
Data Extraction Attacks in Retrieval-Augmented Generation via Backdoors
Yuefeng Peng, Junda Wang, Hong Yu +1
Despite significant advancements, large language models (LLMs) still struggle with providing accurate answers when lacking domain-specific or up-to-date knowledge. Retrieval-Augmen…
OSLO: One-Shot Label-Only Membership Inference Attacks
Yuefeng Peng, Jaechul Roh, Subhransu Maji +1
We introduce One-Shot Label-Only (OSLO) membership inference attacks (MIAs), which accurately infer a given sample's membership in a target model's training set with high precision…
Diffence: Fencing Membership Privacy With Diffusion Models
Yuefeng Peng, Ali Naseh, Amir Houmansadr
Deep learning models, while achieving remarkable performances, are vulnerable to membership inference attacks (MIAs). Although various defenses have been proposed, there is still s…