activity
20182026
most citedLook Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models

48 citations · 100 across the 33 of their papers we have counts for

collaborators

40 papers

cs.CL2026

ICO: Enhancing Semantic-Shift Jailbreaks via Iterative Context Optimization

Hujian Zhu, Yihao Huang, Felix Juefei-Xu +5

Foundation models have achieved remarkable success across diverse tasks, but they remain vulnerable. To investigate such vulnerabilities, semantic-shift jailbreaks have recently em…

cs.CV2025

Beyond Pixels: Semantic-aware Typographic Attack for Geo-Privacy Protection

Jiayi Zhu, Yihao Huang, Yue Cao +5

Large Visual Language Models (LVLMs) now pose a serious yet overlooked privacy threat, as they can infer a social media user's geolocation directly from shared images, leading to u…

cs.CR2025

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…

cs.CR2025

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Kun Wang, Guibin Zhang, Zhenhong Zhou +100

The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…

cs.CV2025

Scale-Invariant Adversarial Attack against Arbitrary-scale Super-resolution

Yihao Huang, Xin Luo, Qing Guo +5

The advent of local continuous image function (LIIF) has garnered significant attention for arbitrary-scale super-resolution (SR) techniques. However, while the vulnerabilities of…

cs.CV2024

Concept Guided Co-salient Object Detection

Jiayi Zhu, Qing Guo, Felix Juefei-Xu +3

Co-salient object detection (Co-SOD) aims to identify common salient objects across a group of related images. While recent methods have made notable progress, they typically rely…