3 citations · 7 across the 41 of their papers we have counts for
23 papers · 1 filter
Review of Hallucination Understanding in Large Language and Vision Models
Zhengyi Ho, Siyuan Liang, Dacheng Tao
The widespread adoption of large language and vision models in real-world applications has made urgent the need to address hallucinations -- instances where models produce incorrec…
Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing
Yisong Xiao, Aishan Liu, Siyuan Liang +3
Large Language Models (LLMs) have demonstrated impressive performance across various tasks, yet they remain vulnerable to generating toxic content, necessitating detoxification str…
Universal Camouflage Attack on Vision-Language Models for Autonomous Driving
Dehong Kong, Sifan Yu, Siyuan Liang +4
Visual language modeling for automated driving is emerging as a promising research direction with substantial improvements in multimodal reasoning capabilities. Despite its advance…
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,…
Bench2ADVLM: A Closed-Loop Benchmark for Vision-language Models in Autonomous Driving
Tianyuan Zhang, Ting Jin, Lu Wang +5
Vision-Language Models (VLMs) have recently emerged as a promising paradigm in autonomous driving (AD). However, current performance evaluation protocols for VLM-based AD systems (…
PromptSafe: Gated Prompt Tuning for Safe Text-to-Image Generation
Zonglei Jing, Xiao Yang, Xiaoqian Li +4
Text-to-image (T2I) models have demonstrated remarkable generative capabilities but remain vulnerable to producing not-safe-for-work (NSFW) content, such as violent or explicit ima…
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…
ICLShield: Exploring and Mitigating In-Context Learning Backdoor Attacks
Zhiyao Ren, Siyuan Liang, Aishan Liu +1
In-context learning (ICL) has demonstrated remarkable success in large language models (LLMs) due to its adaptability and parameter-free nature. However, it also introduces a criti…
T2V-OptJail: Discrete Prompt Optimization for Text-to-Video Jailbreak Attacks
Jiayang Liu, Siyuan Liang, Shiqian Zhao +5
In recent years, fueled by the rapid advancement of diffusion models, text-to-video (T2V) generation models have achieved remarkable progress, with notable examples including Pika,…
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,…
ME: Trigger Element Combination Backdoor Attack on Copyright Infringement
Feiyu Yang, Siyuan Liang, Aishan Liu +1
The capability of generative diffusion models (DMs) like Stable Diffusion (SD) in replicating training data could be taken advantage of by attackers to launch the Copyright Infring…
SRD: Reinforcement-Learned Semantic Perturbation for Backdoor Defense in VLMs
Shuhan Xu, Siyuan Liang, Hongling Zheng +6
Visual language models (VLMs) have made significant progress in image captioning tasks, yet recent studies have found they are vulnerable to backdoor attacks. Attackers can inject…
Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving
Ming Liu, Siyuan Liang, Koushik Howlader +3
Vision-Language Models (VLMs) have been integrated into autonomous driving systems to enhance reasoning capabilities through tasks such as Visual Question Answering (VQA). However,…
T2VShield: Model-Agnostic Jailbreak Defense for Text-to-Video Models
Siyuan Liang, Jiayang Liu, Jiecheng Zhai +5
The rapid development of generative artificial intelligence has made text to video models essential for building future multimodal world simulators. However, these models remain vu…
Fairness Mediator: Neutralize Stereotype Associations to Mitigate Bias in Large Language Models
Yisong Xiao, Aishan Liu, Siyuan Liang +2
LLMs have demonstrated remarkable performance across diverse applications, yet they inadvertently absorb spurious correlations from training data, leading to stereotype association…
Manipulating Multimodal Agents via Cross-Modal Prompt Injection
Le Wang, Zonghao Ying, Tianyuan Zhang +5
The emergence of multimodal large language models has redefined the agent paradigm by integrating language and vision modalities with external data sources, enabling agents to bett…
Lie Detector: Unified Backdoor Detection via Cross-Examination Framework
Xuan Wang, Siyuan Liang, Dongping Liao +6
Institutions with limited data and computing resources often outsource model training to third-party providers in a semi-honest setting, assuming adherence to prescribed training p…
Reasoning-Augmented Conversation for Multi-Turn Jailbreak Attacks on Large Language Models
Zonghao Ying, Deyue Zhang, Zonglei Jing +7
Multi-turn jailbreak attacks simulate real-world human interactions by engaging large language models (LLMs) in iterative dialogues, exposing critical safety vulnerabilities. Howev…
EAT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models
Liming Lu, Xiang Gu, Shuchao Pang +5
Research endeavors have been made in learning robust Multimodal Large Language Models (MLLMs) against jailbreak attacks. However, existing methods for improving MLLMs' robustness s…
ELBA-Bench: An Efficient Learning Backdoor Attacks Benchmark for Large Language Models
Xuxu Liu, Siyuan Liang, Mengya Han +5
Generative large language models are crucial in natural language processing, but they are vulnerable to backdoor attacks, where subtle triggers compromise their behavior. Although…
Black-Box Adversarial Attack on Vision Language Models for Autonomous Driving
Lu Wang, Tianyuan Zhang, Yang Qu +5
Vision-language models (VLMs) have significantly advanced autonomous driving (AD) by enhancing reasoning capabilities; however, these models remain highly susceptible to adversaria…
CogMorph: Cognitive Morphing Attacks for Text-to-Image Models
Zonglei Jing, Zonghao Ying, Le Wang +4
The development of text-to-image (T2I) generative models, that enable the creation of high-quality synthetic images from textual prompts, has opened new frontiers in creative desig…