50 citations · 81 across the 13 of their papers we have counts for
18 papers
GeoCloak: An Agentic Framework for Semantics-Preserving Geolocation Privacy via Peripheral Clue Manipulation
Yixin Wu, Mingjie Li, Yun Shen +2
The rapid advancement of large vision language models (LVLMs) has made geolocation inference from images highly accurate and accessible. This poses serious privacy risks: a passive…
Capability-Routed Guard: Defending Large Reasoning Models Against Reasoning-Centric Jailbreaks
Yiyong Liu, Yixin Wu, Jun Sakuma
Large reasoning models (LRMs) expose a new safety failure mode: adversarial prompts can manipulate reasoning context, task decomposition, or capability interpretation so that harmf…
From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
Chi Cui, Yixin Wu, Yang Zhang
AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals. Prior work has examined dedicated nudification pla…
GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents
Xinyu Zhang, Yixin Wu, Boyang Zhang +4
Images shared on social media often expose geographic cues. While early geolocation methods required expert effort and lacked generalization, the rise of Large Vision Language Mode…
AttackPilot: Autonomous Inference Attacks Against ML Services With LLM-Based Agents
Yixin Wu, Rui Wen, Chi Cui +2
Inference attacks have been widely studied and offer a systematic risk assessment of ML services; however, their implementation and the attack parameters for optimal estimation are…
The Challenge of Identifying the Origin of Black-Box Large Language Models
Ziqing Yang, Yixin Wu, Yun Shen +3
The tremendous commercial potential of large language models (LLMs) has heightened concerns over their unauthorized use. To address this, we focus on the task of identifying the or…
Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
Yixin Wu, Ziqing Yang, Yun Shen +2
Large language models (LLMs) have facilitated the generation of high-quality, cost-effective synthetic data for developing downstream models and conducting statistical analyses in…
Peering Behind the Shield: Guardrail Identification in Large Language Models
Ziqing Yang, Yixin Wu, Rui Wen +2
With the rapid adoption of large language models (LLMs), conversational AI agents have become widely deployed across real-world applications. To enhance safety, these agents are of…
HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns
Xinyue Shen, Yixin Wu, Yiting Qu +3
Large Language Models (LLMs) have raised increasing concerns about their misuse in generating hate speech. Among all the efforts to address this issue, hate speech detectors play a…
Image-Perfect Imperfections: Safety, Bias, and Authenticity in the Shadow of Text-To-Image Model Evolution
Yixin Wu, Yun Shen, Michael Backes +1
Text-to-image models, such as Stable Diffusion (SD), undergo iterative updates to improve image quality and address concerns such as safety. Improvements in image quality are strai…
Voice Jailbreak Attacks Against GPT-4o
Xinyue Shen, Yixin Wu, Michael Backes +1
Recently, the concept of artificial assistants has evolved from science fiction into real-world applications. GPT-4o, the newest multimodal large language model (MLLM) across audio…
Link Stealing Attacks Against Inductive Graph Neural Networks
Yixin Wu, Xinlei He, Pascal Berrang +4
A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in two settings, includi…
UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images
Yiting Qu, Xinyue Shen, Yixin Wu +3
With the advent of text-to-image models and concerns about their misuse, developers are increasingly relying on image safety classifiers to moderate their generated unsafe images.…
Quantifying Privacy Risks of Prompts in Visual Prompt Learning
Yixin Wu, Rui Wen, Michael Backes +4
Large-scale pre-trained models are increasingly adapted to downstream tasks through a new paradigm called prompt learning. In contrast to fine-tuning, prompt learning does not upda…
On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts
Yixin Wu, Ning Yu, Michael Backes +2
Malicious or manipulated prompts are known to exploit text-to-image models to generate unsafe images. Existing studies, however, focus on the passive exploitation of such harmful c…
UMDFood: Vision-language models boost food composition compilation
Peihua Ma, Yixin Wu, Ning Yu +4
Nutrition information is crucial in precision nutrition and the food industry. The current food composition compilation paradigm relies on laborious and experience-dependent method…
Membership Inference Attacks Against Text-to-image Generation Models
Yixin Wu, Ning Yu, Zheng Li +2
Text-to-image generation models have recently attracted unprecedented attention as they unlatch imaginative applications in all areas of life. However, developing such models requi…
Node-Level Membership Inference Attacks Against Graph Neural Networks
Xinlei He, Rui Wen, Yixin Wu +3
Many real-world data comes in the form of graphs, such as social networks and protein structure. To fully utilize the information contained in graph data, a new family of machine l…