most citedT2UE: Generating Unlearnable Examples from Text Descriptions

1 citations · 1 across the 7 of their papers we have counts for

collaborators

10 papers

cs.AI2026

AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents

Yunhao Feng, Yifan Ding, Yingshui Tan +6

Computer-use agents extend language models from text generation to persistent action over tools, files, and execution environments. Unlike chat systems, they maintain state across…

cs.CL2026

Internal Safety Collapse in Frontier Large Language Models

Yutao Wu, Xiao Liu, Yifeng Gao +7

This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): under certain task conditions, models enter a s…

cs.AI2026

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

Xingjun Ma, Yixu Wang, Hengyuan Xu +18

The rapid evolution of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has driven major gains in reasoning, perception, and generation across language and…

cs.CV2025

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…

cs.AI20251 cited

T2UE: Generating Unlearnable Examples from Text Descriptions

Xingjun Ma, Hanxun Huang, Tianwei Song +3

Large-scale pre-training frameworks like CLIP have revolutionized multimodal learning, but their reliance on web-scraped datasets, frequently containing private user data, raises s…

cs.CV2025

BrokenVideos: A Benchmark Dataset for Fine-Grained Artifact Localization in AI-Generated Videos

Jiahao Lin, Weixuan Peng, Bojia Zi +4

Recent advances in deep generative models have led to significant progress in video generation, yet the fidelity of AI-generated videos remains limited. Synthesized content often e…