12 citations · 46 across the 52 of their papers we have counts for
19 papers · 1 filter
Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media
Zhen Sun, Zongmin Zhang, Xinyue Shen +5
Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs). However, the misuse of AIGTs could have profound implications for public opinion, such as…
: Measuring Stereotypical Bias in Large Vision-Language Models from Vision and Language Modalities
Yukun Jiang, Zheng Li, Xinyue Shen +3
Large vision-language models (LVLMs) have been rapidly developed and widely used in various fields, but the (potential) stereotypical bias in the model is largely unexplored. In th…
Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?
Rui Wen, Michael Backes, Yang Zhang
Machine learning has revolutionized numerous domains, playing a crucial role in driving advancements and enabling data-centric processes. The significance of data in training model…
Membership Inference Attacks Against In-Context Learning
Rui Wen, Zheng Li, Michael Backes +1
Adapting Large Language Models (LLMs) to specific tasks introduces concerns about computational efficiency, prompting an exploration of efficient methods such as In-Context Learnin…
Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
Atilla Akkus, Masoud Poorghaffar Aghdam, Mingjie Li +4
Large language models (LLMs) have demonstrated significant success in various domain-specific tasks, with their performance often improving substantially after fine-tuning. However…
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…
Inside the Black Box: Detecting Data Leakage in Pre-trained Language Encoders
Yuan Xin, Zheng Li, Ning Yu +4
Despite being prevalent in the general field of Natural Language Processing (NLP), pre-trained language models inherently carry privacy and copyright concerns due to their nature o…
Vera Verto: Multimodal Hijacking Attack
Minxing Zhang, Ahmed Salem, Michael Backes +1
The increasing cost of training machine learning (ML) models has led to the inclusion of new parties to the training pipeline, such as users who contribute training data and compan…
Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification
Boyang Zhang, Yicong Tan, Yun Shen +4
Recently, autonomous agents built on large language models (LLMs) have experienced significant development and are being deployed in real-world applications. These agents can exten…
ICLGuard: Controlling In-Context Learning Behavior for Applicability Authorization
Wai Man Si, Michael Backes, Yang Zhang
In-context learning (ICL) is a recent advancement in the capabilities of large language models (LLMs). This feature allows users to perform a new task without updating the model. C…
SOS! Soft Prompt Attack Against Open-Source Large Language Models
Ziqing Yang, Michael Backes, Yang Zhang +1
Open-source large language models (LLMs) have become increasingly popular among both the general public and industry, as they can be customized, fine-tuned, and freely used. Howeve…
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…
Do You Even Lift? Strengthening Compiler Security Guarantees Against Spectre Attacks
Xaver Fabian, Marco Patrignani, Marco Guarnieri +1
Mainstream compilers implement different countermeasures to prevent specific classes of speculative execution attacks. Unfortunately, these countermeasures either lack formal guara…
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.…
Efficient Data-Free Model Stealing with Label Diversity
Yiyong Liu, Rui Wen, Michael Backes +1
Machine learning as a Service (MLaaS) allows users to query the machine learning model in an API manner, which provides an opportunity for users to enjoy the benefits brought by th…
Instruction Backdoor Attacks Against Customized LLMs
Rui Zhang, Hongwei Li, Rui Wen +5
The increasing demand for customized Large Language Models (LLMs) has led to the development of solutions like GPTs. These solutions facilitate tailored LLM creation via natural la…
JailbreakRadar: Comprehensive Assessment of Jailbreak Attacks Against LLMs
Junjie Chu, Yugeng Liu, Ziqing Yang +3
Jailbreak attacks aim to bypass the LLMs' safeguards. While researchers have proposed different jailbreak attacks in depth, they have done so in isolation -- either with unaligned…
Reconstruct Your Previous Conversations! Comprehensively Investigating Privacy Leakage Risks in Conversations with GPT Models
Junjie Chu, Zeyang Sha, Michael Backes +1
Significant advancements have recently been made in large language models represented by GPT models. Users frequently have multi-round private conversations with cloud-hosted GPT m…