18 citations · 20 across the 18 of their papers we have counts for
6 papers · 1 filter
CollageAttack: Exploiting Cross-Modal Alignment Flaws in T2I Models through Spatial Text Composition
Zhiyi Mou, Yao Lu, Wangze Ni +6
Text-to-image (T2I) models have substantially improved in language understanding, in-image text rendering, and visual composition, while their safety mechanisms do not always keep…
ActBench: Self-Evolving Benchmark of Behavioral Safety in Cowork Agents
Hongwei Yao, Yiming Liu, Meihui Chen +6
Cowork agents may complete benign tasks while disclosing protected data, manipulating unauthorized state, invocate unauthorized API. We define behavioral safety and introduce ActBe…
From Role Prompt to Infinite Thinking: Exploiting Persona Conditioning for Inference Cost Attacks in LLMs
Zhiyi Mou, Wangze Ni, Tianfang Xiao +6
LLMs are increasingly deployed in real-world applications, making inference efficiency and service reliability critical concerns due to their substantial computational costs. Howev…
SpatialJB: How Text Distribution Art Becomes the "Jailbreak Key" for LLM Guardrails
Zhiyi Mou, Jingyuan Yang, Zeheng Qian +6
While Large Language Models (LLMs) have powerful capabilities, they remain vulnerable to jailbreak attacks, which is a critical barrier to their safe web real-time application. Cur…
DualBreach: Efficient Dual-Jailbreaking via Target-Driven Initialization and Multi-Target Optimization
Xinzhe Huang, Kedong Xiu, Tianhang Zheng +5
Recent research has focused on exploring the vulnerabilities of Large Language Models (LLMs), aiming to elicit harmful and/or sensitive content from LLMs. However, due to the insuf…
ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
Yuke Hu, Jian Lou, Jiaqi Liu +4
Over the past years, Machine Learning-as-a-Service (MLaaS) has received a surging demand for supporting Machine Learning-driven services to offer revolutionized user experience acr…