activity
20242026
most citedJPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering

3 citations · 3 across the 7 of their papers we have counts for

collaborators

7 papers

cs.MM2026

Transfer Safety Awareness for Cross-Modal Safety Drift in Multimodal Large Language Models

Tianqi Xiao, Shiyao Cui, Minghao Zhang +2

Visual modality enhances the capabilities of multimodal large language models (MLLMs) but also introduces a safety concern: a benign textual query may convey harmful intent when gr…

cs.CV2026

The Side Effects of Being Smart: Safety Risks in MLLMs' Multi-Image Reasoning

Renmiao Chen, Yida Lu, Shiyao Cui +6

As Multimodal Large Language Models (MLLMs) acquire stronger reasoning capabilities to handle complex, multi-image instructions, this advancement may pose new safety risks. We stud…

cs.MM2025★ 3 cited

JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering

Renmiao Chen, Shiyao Cui, Xuancheng Huang +7

Jailbreak attacks against multimodal large language Models (MLLMs) are a significant research focus. Current research predominantly focuses on maximizing attack success rate (ASR),…

cs.MM2025

ShieldVLM: Safeguarding the Multimodal Implicit Toxicity via Deliberative Reasoning with LVLMs

Shiyao Cui, Qinglin Zhang, Xuan Ouyang +6

Toxicity detection in multimodal text-image content faces growing challenges, especially with multimodal implicit toxicity, where each modality appears benign on its own but convey…

cs.CL2025

AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement

Zhexin Zhang, Leqi Lei, Junxiao Yang +13

As AI models are increasingly deployed across diverse real-world scenarios, ensuring their safety remains a critical yet underexplored challenge. While substantial efforts have bee…

cs.CR2024

BlackDAN: A Black-Box Multi-Objective Approach for Effective and Contextual Jailbreaking of Large Language Models

Xinyuan Wang, Victor Shea-Jay Huang, Renmiao Chen +4

While large language models (LLMs) exhibit remarkable capabilities across various tasks, they encounter potential security risks such as jailbreak attacks, which exploit vulnerabil…

cs.CR2024

Global Challenge for Safe and Secure LLMs Track 1

Xiaojun Jia, Yihao Huang, Yang Liu +27

This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme…