5 papers
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…
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),…
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…
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…
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…