15 papers
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models
Dongdong Yang, Deyue Zhang, Zhao Liu +5
Text-to-Image (T2I) generative models have achieved remarkable progress in synthesizing high-quality visual content, yet they remain vulnerable to adversarial misuse, particularly…
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…
Attention Hijacking: Response Manipulation Across Queries in Vision-Language Models
Zhiqiang Wang, Dongrui Liu, Yan Li +4
Existing adversarial attacks on vision-language models (VLMs) can steer model outputs toward attacker-specified target responses, but their effectiveness often degrades when the sa…
TrajShield: Trajectory-Level Safety Mediation for Defending Text-to-Video Models Against Jailbreak Attacks
Quanchen Zou, Nizhang Li, Wenxin Zhang +4
Text-to-Video (T2V) models have demonstrated remarkable capability in generating temporally coherent videos from natural language prompts, yet they also risk producing unsafe conte…
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…
Reading Between the Pixels: An Inscriptive Jailbreak Attack on Text-to-Image Models
Zonghao Ying, Haowen Dai, Lianyu Hu +5
Modern text-to-image (T2I) models can now render legible, paragraph-length text, enabling a fundamentally new class of misuse. We identify and formalize the inscriptive jailbreak,…
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…
DIVER: Dynamic Iterative Visual Evidence Reasoning for Multimodal Fake News Detection
Weilin Zhou, Zonghao Ying, Chunlei Meng +6
Multimodal fake news detection is crucial for mitigating adversarial misinformation. Existing methods, relying on static fusion or LLMs, face computational redundancy and hallucina…
RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic
Le Wang, Zonghao Ying, Xiao Yang +7
Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks, yet they remain vulnerable to hazardous instructions that m…
Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection
Weilin Zhou, Zonghao Ying, Rongchen Zhao +7
Prevalent multimodal fake news detection relies on consistency-based fusion, yet this paradigm fundamentally misinterprets critical cross-modal discrepancies as noise, leading to o…
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…
Mask-GCG: Are All Tokens in Adversarial Suffixes Necessary for Jailbreak Attacks?
Junjie Mu, Zonghao Ying, Zhekui Fan +6
Jailbreak attacks on Large Language Models (LLMs) have demonstrated various successful methods whereby attackers manipulate models into generating harmful responses that they are d…
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…
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…