10 papers
Grounding-Driven Attack: Improving Encoder-based Adversarial Transferability against Large Vision-Language Models
Xinwei Zhang, Li Bai, Tianwei Zhang +5
Large vision-language models (LVLMs) have achieved impressive performance across multimodal tasks, but their reliance on visual inputs exposes them to adversarial threats. Encoder-…
On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression
Xinwei Zhang, Hangcheng Liu, Li Bai +4
Visual token compression is widely used to accelerate large vision-language models (LVLMs) by pruning or merging visual tokens, yet its adversarial robustness remains unexplored. W…
Cross-Modal Backdoors in Multimodal Large Language Models
Runhe Wang, Li Bai, Haibo Hu +1
Developers increasingly construct multimodal large language models (MLLMs) by assembling pretrained components,introducing supply-chain attack surfaces.Existing security research p…
United We Defend: Collaborative Membership Inference Defenses in Federated Learning
Li Bai, Junxu Liu, Sen Zhang +3
Membership inference attacks (MIAs), which determine whether a specific data point was included in the training set of a target model, have posed severe threats in federated learni…
Toward Efficient Inference Attacks: Shadow Model Sharing via Mixture-of-Experts
Li Bai, Qingqing Ye, Xinwei Zhang +4
Machine learning models are often vulnerable to inference attacks that expose sensitive information from their training data. Shadow model technique is commonly employed in such at…
MER-Inspector: Assessing model extraction risks from an attack-agnostic perspective
Xinwei Zhang, Haibo Hu, Qingqing Ye +2
Information leakage issues in machine learning-based Web applications have attracted increasing attention. While the risk of data privacy leakage has been rigorously analyzed, the…