217 citations · 1.8k across the 154 of their papers we have counts for
9 papers · 1 filter
When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?
Yuan Tian, Bing Hu, Fang Wu +3
Think-with-image reasoning is emerging as a new inference paradigm for large vision-language models, but its safety implications remain poorly understood. Existing systems already…
Benchmarking Vision-Language Models under Contradictory Virtual Content Attacks in Augmented Reality
Yanming Xiu, Zhengyuan Jiang, Neil Zhenqiang Gong +1
Augmented reality (AR) has rapidly expanded over the past decade. As AR becomes increasingly integrated into daily life, its security and reliability emerge as critical challenges.…
Leave My Images Alone: Preventing Multi-Modal Large Language Models from Analyzing Images via Visual Prompt Injection
Zedian Shao, Hongbin Liu, Yuepeng Hu +1
Multi-modal large language models (MLLMs) have emerged as powerful tools for analyzing Internet-scale image data, offering significant benefits but also raising critical safety and…
Automatically Generating Visual Hallucination Test Cases for Multimodal Large Language Models
Zhongye Liu, Hongbin Liu, Yuepeng Hu +2
Visual hallucination (VH) occurs when a multimodal large language model (MLLM) generates responses with incorrect visual details for prompts. Existing methods for generating VH tes…
Tracing Back the Malicious Clients in Poisoning Attacks to Federated Learning
Yuqi Jia, Minghong Fang, Hongbin Liu +2
Poisoning attacks compromise the training phase of federated learning (FL) such that the learned global model misclassifies attacker-chosen inputs called target inputs. Existing de…
Visual Hallucinations of Multi-modal Large Language Models
Wen Huang, Hongbin Liu, Minxin Guo +1
Visual hallucination (VH) means that a multi-modal LLM (MLLM) imagines incorrect details about an image in visual question answering. Existing studies find VH instances only in exi…