51 citations · 139 across the 35 of their papers we have counts for
7 papers · 1 filter
Exploring Semantic-constrained Adversarial Example with Instruction Uncertainty Reduction
Jin Hu, Jiakai Wang, Linna Jing +6
Recently, semantically constrained adversarial examples (SemanticAE), which are directly generated from natural language instructions, have become a promising avenue for future res…
Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles
Jiangfan Liu, Yongkang Guo, Fangzhi Zhong +7
The generation of safety-critical scenarios in simulation has become increasingly crucial for safety evaluation in autonomous vehicles prior to road deployment in society. However,…
MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving
Aishan Liu, Jiakai Wang, Tianyuan Zhang +6
Evaluating and ensuring the adversarial robustness of autonomous driving (AD) systems is a critical and unresolved challenge. This paper introduces MetAdv, a novel adversarial test…
Pushing the Limits of Safety: A Technical Report on the ATLAS Challenge 2025
Zonghao Ying, Siyang Wu, Run Hao +44
Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks…
First-Order Error Matters: Accurate Compensation for Quantized Large Language Models
Xingyu Zheng, Haotong Qin, Yuye Li +5
Post-training quantization (PTQ) offers an efficient approach to compressing large language models (LLMs), significantly reducing memory access and computational costs. Existing co…
AGENTSAFE: Benchmarking the Safety of Embodied Agents on Hazardous Instructions
Zonghao Ying, Le Wang, Yisong Xiao +7
The integration of vision-language models (VLMs) is driving a new generation of embodied agents capable of operating in human-centered environments. However, as deployment expands,…
Towards Benchmarking and Assessing the Safety and Robustness of Autonomous Driving on Safety-critical Scenarios
Jingzheng Li, Xianglong Liu, Shikui Wei +6
Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception task and the development of end-to-end autonomous d…