8 papers
SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models
Xinyi Zeng, Xue Yang, Jingyuan Zhang +5
Multimodal large language models (MLLMs) are gaining increasing attention. Due to the heterogeneity of their input features, they face significant challenges in terms of jailbreak…
Obscure but Effective: Classical Chinese Jailbreak Prompt Optimization via Bio-Inspired Search
Xun Huang, Simeng Qin, Xiaoshuang Jia +6
As Large Language Models (LLMs) are increasingly used, their security risks have drawn increasing attention. Existing research reveals that LLMs are highly susceptible to jailbreak…
Crafting Physical Adversarial Examples by Combining Differentiable and Physically Based Renders
Yuqiu Liu, Huanqian Yan, Xiaopei Zhu +4
Recently we have witnessed progress in hiding road vehicles against object detectors through adversarial camouflage in the digital world. The extension of this technique to the phy…
Distillation-Enhanced Physical Adversarial Attacks
Wei Liu, Yonglin Wu, Chaoqun Li +2
The study of physical adversarial patches is crucial for identifying vulnerabilities in AI-based recognition systems and developing more robust deep learning models. While recent r…
CapGen:An Environment-Adaptive Generator of Adversarial Patches
Chaoqun Li, Zhuodong Liu, Huanqian Yan +1
Adversarial patches, often used to provide physical stealth protection for critical assets and assess perception algorithm robustness, usually neglect the need for visual harmony w…
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…