2 citations · 2 across the 16 of their papers we have counts for
8 papers · 1 filter
DMN: A Compositional Framework for Jailbreaking Multimodal LLMs with Multi-Image Inputs
Wenzhuo Xu, Zhipeng Wei, Zonghao Ying +4
Multimodal Large Language Models (MLLMs) are vulnerable to jailbreak attacks, which can elicit harmful responses from MLLMs. Many MLLMs support multi-image inputs, inadvertently in…
SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety
Zhe Liu, Zonghao Ying, Wenxin Zhang +5
Recent advances in foundation models have transformed LLMs from passive conversational systems into autonomous agents capable of reasoning and tool execution. While these capabilit…
Reasoning-Oriented Programming: Chaining Semantic Gadgets to Jailbreak Large Vision Language Models
Quanchen Zou, Moyang Chen, Zonghao Ying +6
Large Vision-Language Models (LVLMs) undergo safety alignment to suppress harmful content. However, current defenses predominantly target explicit malicious patterns in the input r…
Two Frames Matter: A Temporal Attack for Text-to-Video Model Jailbreaking
Moyang Chen, Zonghao Ying, Wenzhuo Xu +4
Recent text-to-video (T2V) models can synthesize complex videos from lightweight natural language prompts, raising urgent concerns about safety alignment in the event of misuse in…
Sequential Comics for Jailbreaking Multimodal Large Language Models via Structured Visual Storytelling
Deyue Zhang, Dongdong Yang, Junjie Mu +6
Multimodal large language models (MLLMs) exhibit remarkable capabilities but remain susceptible to jailbreak attacks exploiting cross-modal vulnerabilities. In this work, we introd…
PRISM: Programmatic Reasoning with Image Sequence Manipulation for LVLM Jailbreaking
Quanchen Zou, Zonghao Ying, Moyang Chen +7
The increasing sophistication of large vision-language models (LVLMs) has been accompanied by advances in safety alignment mechanisms designed to prevent harmful content generation…
Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findings
Zonghao Ying, Guangyi Zheng, Yongxin Huang +6
This study presents the first comprehensive safety evaluation of the DeepSeek models, focusing on evaluating the safety risks associated with their generated content. Our evaluatio…
Probabilistic Modeling of Jailbreak on Multimodal LLMs: From Quantification to Application
Wenzhuo Xu, Zhipeng Wei, Xiongtao Sun +5
Recently, Multimodal Large Language Models (MLLMs) have demonstrated their superior ability in understanding multimodal content. However, they remain vulnerable to jailbreak attack…