146 citations · 1.1k across the 129 of their papers we have counts for
20 papers · 1 filter
GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models
Kunsheng Tang, Wenbo Zhou, Jie Zhang +7
Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but they have also been observed to magnify societal biases, particularly those…
Walking in Others' Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias
Rongwu Xu, Zi'an Zhou, Tianwei Zhang +5
The common toxicity and societal bias in contents generated by large language models (LLMs) necessitate strategies to reduce harm. Present solutions often demand white-box access t…
A Case for Application-Aware Space Radiation Tolerance in Orbital Computing
Meiqi Wang, Han Qiu, Longnv Xu +5
We are witnessing a surge in the use of commercial off-the-shelf (COTS) hardware for cost-effective in-orbit computing, such as deep neural network (DNN) based on-satellite sensor…
Backdoor Attacks against Image-to-Image Networks
Wenbo Jiang, Hongwei Li, Jiaming He +4
Recently, deep learning-based Image-to-Image (I2I) networks have become the predominant choice for I2I tasks such as image super-resolution and denoising. Despite their remarkable…
Course-Correction: Safety Alignment Using Synthetic Preferences
Rongwu Xu, Yishuo Cai, Zhenhong Zhou +6
The risk of harmful content generated by large language models (LLMs) becomes a critical concern. This paper presents a systematic study on assessing and improving LLMs' capability…
Systematic Categorization, Construction and Evaluation of New Attacks against Multi-modal Mobile GUI Agents
Yulong Yang, Xinshan Yang, Shuaidong Li +4
The integration of Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) into mobile GUI agents has significantly enhanced user efficiency and experience. Howe…
ShadowCode: Towards (Automatic) External Prompt Injection Attack against Code LLMs
Yuchen Yang, Yiming Li, Hongwei Yao +5
Recent advancements have led to the widespread adoption of code-oriented large language models (Code LLMs) for programming tasks. Despite their success in deployment, their securit…
AquaLoRA: Toward White-box Protection for Customized Stable Diffusion Models via Watermark LoRA
Weitao Feng, Wenbo Zhou, Jiyan He +6
Diffusion models have achieved remarkable success in generating high-quality images. Recently, the open-source models represented by Stable Diffusion (SD) are thriving and are acce…
Oedipus: LLM-enchanced Reasoning CAPTCHA Solver
Gelei Deng, Haoran Ou, Yi Liu +3
CAPTCHAs have become a ubiquitous tool in safeguarding applications from automated bots. Over time, the arms race between CAPTCHA development and evasion techniques has led to incr…
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign Users
Guanlin Li, Kangjie Chen, Shudong Zhang +2
Large-scale pre-trained generative models are taking the world by storm, due to their abilities in generating creative content. Meanwhile, safeguards for these generative models ar…
BadEdit: Backdooring large language models by model editing
Yanzhou Li, Tianlin Li, Kangjie Chen +5
Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applie…
Fluent: Round-efficient Secure Aggregation for Private Federated Learning
Xincheng Li, Jianting Ning, Geong Sen Poh +3
Federated learning (FL) facilitates collaborative training of machine learning models among a large number of clients while safeguarding the privacy of their local datasets. Howeve…
Groot: Adversarial Testing for Generative Text-to-Image Models with Tree-based Semantic Transformation
Yi Liu, Guowei Yang, Gelei Deng +5
With the prevalence of text-to-image generative models, their safety becomes a critical concern. adversarial testing techniques have been developed to probe whether such models can…
Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning
Gelei Deng, Yi Liu, Kailong Wang +3
Large Language Models~(LLMs) have gained immense popularity and are being increasingly applied in various domains. Consequently, ensuring the security of these models is of paramou…
PRIME: Protect Your Videos From Malicious Editing
Guanlin Li, Shuai Yang, Jie Zhang +1
With the development of generative models, the quality of generated content keeps increasing. Recently, open-source models have made it surprisingly easy to manipulate and edit pho…
Model X-ray:Detecting Backdoored Models via Decision Boundary
Yanghao Su, Jie Zhang, Ting Xu +3
Backdoor attacks pose a significant security vulnerability for deep neural networks (DNNs), enabling them to operate normally on clean inputs but manipulate predictions when specif…
Robust-Wide: Robust Watermarking against Instruction-driven Image Editing
Runyi Hu, Jie Zhang, Ting Xu +2
Instruction-driven image editing allows users to quickly edit an image according to text instructions in a forward pass. Nevertheless, malicious users can easily exploit this techn…
SAME: Sample Reconstruction against Model Extraction Attacks
Yi Xie, Jie Zhang, Shiqian Zhao +2
While deep learning models have shown significant performance across various domains, their deployment needs extensive resources and advanced computing infrastructure. As a solutio…
Model Supply Chain Poisoning: Backdooring Pre-trained Models via Embedding Indistinguishability
Hao Wang, Shangwei Guo, Jialing He +3
Pre-trained models (PTMs) are widely adopted across various downstream tasks in the machine learning supply chain. Adopting untrustworthy PTMs introduces significant security risks…
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
Haodong Li, Gelei Deng, Yi Liu +7
Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of Large Language Models (LLMs) across numerous applications. However, the detailed…