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20242026
most citedUnveiling the Safety of GPT-4o: An Empirical Study using Jailbreak Attacks

3 citations · 5 across the 27 of their papers we have counts for

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11 papers · 1 filter

cs.CY2025

Uncovering Strategic Egoism Behaviors in Large Language Models

Yaoyuan Zhang, Aishan Liu, Zonghao Ying +4

Large language models (LLMs) face growing trustworthiness concerns (\eg, deception), which hinder their safe deployment in high-stakes decision-making scenarios. In this paper, we…

cs.CV2025

SPARK: Jailbreaking T2V Models by Synergistically Prompting Auditory and Recontextualized Knowledge

Zonghao Ying, Moyang Chen, Nizhang Li +6

Jailbreak attacks can circumvent model safety guardrails and reveal critical blind spots. Prior attacks on text-to-video (T2V) models typically add adversarial perturbations to obv…

cs.CR2025

SecureWebArena: A Holistic Security Evaluation Benchmark for LVLM-based Web Agents

Zonghao Ying, Yangguang Shao, Jianle Gan +8

Large vision-language model (LVLM)-based web agents are emerging as powerful tools for automating complex online tasks. However, when deployed in real-world environments, they face…

cs.CL2025

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…

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.CV2025

PRJ: Perception-Retrieval-Judgement for Generated Images

Qiang Fu, Zonglei Jing, Zonghao Ying +1

The rapid progress of generative AI has enabled remarkable creative capabilities, yet it also raises urgent concerns regarding the safety of AI-generated visual content in real-wor…

cs.CR2025

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,…

cs.CV2025

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…

cs.CR2025

Towards Understanding the Safety Boundaries of DeepSeek Models: Evaluation and Findings

Zonghao Ying, Guangyi Zheng, Yongxin Huang +6

This study presents the first comprehensive safety evaluation of the DeepSeek models, focusing on evaluating the safety risks associated with their generated content. Our evaluatio…

cs.CL2025

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…

cs.CV2025

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…