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title
Data Poisoning Attacks against Vision Language Models
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Project Website for the paper Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models
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2026-01-31 00:04:06
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Shadowcast Shadowcast : Stealthy Data Poisoning Attacks Against Vision-Language Models Yuancheng Xu 1 , Jiarui Yao 2 , Manli Shu 3 , Yanchao Sun 4 , Zichu Wu 5 Ning Yu 6 , Tom Goldstein 1 , Furong Huang 1 1 University of Maryland, College Park 2 University of Illinois Urbana-Champaign 3 Salesforce Research 4 Apple 5 University of Waterloo 6 Netflix Eyeline Studios Neurips, 2024 Paper Code Responses of the clean and poisoned LLaVA-1.5 models. The poisoned samples are crafted using a different VLM, MiniGPT-v2. Abstract Vision-Language Models (VLMs) excel in generating textual responses from visual inputs, yet their versatility raises significant security concerns. This study takes the first step in exposing VLMs' susceptibility to data poisoning attacks that can manipulate responses to innocuous, everyday prompts. We...
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