1 citations · 2 across the 17 of their papers we have counts for
18 papers
OpenART: Scaling Agent Red Teaming via Open-Ended Environment Evolution
Yunhao Chen, Xin Wang, Yixu Wang +6
AI agents operate in persistent environments where early state changes can influence decisions far into the future. Unlike conventional language-model interactions, agent behavior…
OpenRT: An Open-Source Red Teaming Framework for Multimodal LLMs
Xin Wang, Yunhao Chen, Juncheng Li +7
The rapid integration of Multimodal Large Language Models (MLLMs) into critical applications is increasingly hindered by persistent safety vulnerabilities. However, existing red-te…
BackdoorVLM: A Benchmark for Backdoor Attacks and Defenses on Vision-Language Models
Juncheng Li, Yige Li, Hanxun Huang +5
Backdoor attacks undermine the reliability and trustworthiness of machine learning systems by injecting hidden behaviors that can be maliciously activated at inference time. While…
Evolve the Method, Not the Prompts: Evolutionary Synthesis of Jailbreak Attacks on LLMs
Yunhao Chen, Xin Wang, Juncheng Li +5
Automated red teaming frameworks for Large Language Models (LLMs) have become increasingly sophisticated, yet many still formulate attack optimization primarily in the prompt space…
Imperceptible Jailbreaking against Large Language Models
Kuofeng Gao, Yiming Li, Chao Du +4
Jailbreaking attacks on the vision modality typically rely on imperceptible adversarial perturbations, whereas attacks on the textual modality are generally assumed to require visi…
FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models
Xin Wang, Jie Li, Zejia Weng +9
Vision-Language-Action (VLA) models are driving rapid progress in robotics by enabling agents to interpret multimodal inputs and execute complex, long-horizon tasks. However, their…
AgenticEval: Toward Agentic and Self-Evolving Safety Evaluation of Large Language Models
Yixu Wang, Xin Wang, Yang Yao +5
The rapid integration of Large Language Models (LLMs) into high-stakes domains necessitates reliable safety and compliance evaluation. However, existing static benchmarks are ill-e…
Simulated Ensemble Attack: Transferring Jailbreaks Across Fine-tuned Vision-Language Models
Ruofan Wang, Xin Wang, Yang Yao +3
The widespread practice of fine-tuning open-source Vision-Language Models (VLMs) raises a critical security concern: jailbreak vulnerabilities in base models may persist in downstr…
LeakyCLIP: Extracting Training Data from CLIP
Yunhao Chen, Shujie Wang, Xin Wang +3
Understanding the memorization and privacy leakage risks in Contrastive Language--Image Pretraining (CLIP) is critical for ensuring the security of multimodal models. Recent studie…
DAVID-XR1: Detecting AI-Generated Videos with Explainable Reasoning
Yifeng Gao, Yifan Ding, Hongyu Su +9
As AI-generated video becomes increasingly pervasive across media platforms, the ability to reliably distinguish synthetic content from authentic footage has become both urgent and…
Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?
Yang Yao, Lingyu Li, Jiaxin Song +8
As Multimodal Large Language Models (MLLMs) continue to evolve, their cognitive and reasoning capabilities have seen remarkable progress. However, challenges in visual fine-grained…
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models
Jiaming Zhang, Xin Wang, Xingjun Ma +3
Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capabilities in understanding relationships between visual and textual data through joint embedding spaces.…
SafeVid: Toward Safety Aligned Video Large Multimodal Models
Yixu Wang, Jiaxin Song, Yifeng Gao +6
As Video Large Multimodal Models (VLMMs) rapidly advance, their inherent complexity introduces significant safety challenges, particularly the issue of mismatched generalization wh…
Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety
Xingjun Ma, Yifeng Gao, Yixu Wang +45
The rapid advancement of large models, driven by their exceptional abilities in learning and generalization through large-scale pre-training, has reshaped the landscape of Artifici…
TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models
Xin Wang, Kai Chen, Jiaming Zhang +2
Large pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated excellent zero-shot generalizability across various downstream tasks. However, recent studies have sh…
Adversarial Prompt Distillation for Vision-Language Models
Lin Luo, Xin Wang, Bojia Zi +3
Large pre-trained Vision-Language Models (VLMs) such as Contrastive Language-Image Pre-training (CLIP) have been shown to be susceptible to adversarial attacks, raising concerns ab…
AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning
Xin Wang, Kai Chen, Xingjun Ma +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks even under a black-box setting where the adversary can only query the model. Particularly, query-based…
Adversarial Prompt Tuning for Vision-Language Models
Jiaming Zhang, Xingjun Ma, Xin Wang +4
With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual…