activity
20222025
most citedImproving transferability of 3D adversarial attacks with scale and shear transformations

1 citations · 1 across the 5 of their papers we have counts for

collaborators

10 papers

cs.CL2025

Unveiling Trust in Multimodal Large Language Models: Evaluation, Analysis, and Mitigation

Yichi Zhang, Yao Huang, Yifan Wang +10

The trustworthiness of Multimodal Large Language Models (MLLMs) remains an intense concern despite the significant progress in their capabilities. Existing evaluation and mitigatio…

cs.AI2025

A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents

Hang Su, Jun Luo, Chang Liu +4

Recent advances in large language models (LLMs) have catalyzed the rise of autonomous AI agents capable of perceiving, reasoning, and acting in dynamic, open-ended environments. Th…

cs.AI2025

MLA-Trust: Benchmarking Trustworthiness of Multimodal LLM Agents in GUI Environments

Xiao Yang, Jiawei Chen, Jun Luo +4

The emergence of multimodal LLM-based agents (MLAs) has transformed interaction paradigms by seamlessly integrating vision, language, action and dynamic environments, enabling unpr…

cs.CV2025

Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding

Youze Wang, Zijun Chen, Ruoyu Chen +8

Recent advancements in multimodal large language models for video understanding (videoLLMs) have enhanced their capacity to process complex spatiotemporal data. However, challenges…

cs.CV2025

Beyond Quantity: Distribution-Aware Labeling for Visual Grounding

Yichi Zhang, Gongwei Chen, Jun Zhu +2

Visual grounding requires large and diverse region-text pairs. However, manual annotation is costly and fixed vocabularies restrict scalability and generalization. Existing pseudo-…

cs.CV2025

Effective Black-Box Multi-Faceted Attacks Breach Vision Large Language Model Guardrails

Yijun Yang, Lichao Wang, Xiao Yang +2

Vision Large Language Models (VLLMs) integrate visual data processing, expanding their real-world applications, but also increasing the risk of generating unsafe responses. In resp…