62 citations · 153 across the 21 of their papers we have counts for
22 papers
Only What You've Signed up For: Secure Workflows with Constrained Diffusion Agents
Michael Cardei, Anjun Hu, Chenrui Ma +4
As agentic AI systems take on increasingly complex and consequential tasks, securing them against prompt injection becomes critical. Existing defenses often rely on detecting or re…
RedCodeAgent: Automatic Red-teaming Agent against Diverse Code Agents
Chengquan Guo, Chulin Xie, Yu Yang +6
Code agents have gained widespread adoption due to their strong code generation capabilities and integration with code interpreters, enabling dynamic execution, debugging, and inte…
BlueCodeAgent: A Blue Teaming Agent Enabled by Automated Red Teaming for CodeGen AI
Chengquan Guo, Yuzhou Nie, Chulin Xie +3
As large language models (LLMs) are increasingly used for code generation, concerns over the security risks have grown substantially. Early research has primarily focused on red te…
MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models
Chejian Xu, Jiawei Zhang, Zhaorun Chen +22
Multimodal foundation models (MMFMs) play a crucial role in various applications, including autonomous driving, healthcare, and virtual assistants. However, several studies have re…
RedCode: Risky Code Execution and Generation Benchmark for Code Agents
Chengquan Guo, Xun Liu, Chulin Xie +5
With the rapidly increasing capabilities and adoption of code agents for AI-assisted coding, safety concerns, such as generating or executing risky code, have become significant ba…
LLM-PBE: Assessing Data Privacy in Large Language Models
Qinbin Li, Junyuan Hong, Chulin Xie +10
Large Language Models (LLMs) have become integral to numerous domains, significantly advancing applications in data management, mining, and analysis. Their profound capabilities in…
GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning
Zhen Xiang, Linzhi Zheng, Yanjie Li +9
The rapid advancement of large language model (LLM) agents has raised new concerns regarding their safety and security. In this paper, we propose GuardAgent, the first guardrail ag…
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
Rishub Tamirisa, Chulin Xie, Wenxuan Bao +3
Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via persona…
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Bowen Jin, Chulin Xie, Jiawei Zhang +8
Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs…
TablePuppet: A Generic Framework for Relational Federated Learning
Lijie Xu, Chulin Xie, Yiran Guo +6
Current federated learning (FL) approaches view decentralized training data as a single table, divided among participants either horizontally (by rows) or vertically (by columns).…
Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression
Junyuan Hong, Jinhao Duan, Chenhui Zhang +12
Compressing high-capability Large Language Models (LLMs) has emerged as a favored strategy for resource-efficient inferences. While state-of-the-art (SoTA) compression methods boas…
Differentially Private Synthetic Data via Foundation Model APIs 2: Text
Chulin Xie, Zinan Lin, Arturs Backurs +9
Text data has become extremely valuable due to the emergence of machine learning algorithms that learn from it. A lot of high-quality text data generated in the real world is priva…
Effective and Efficient Federated Tree Learning on Hybrid Data
Qinbin Li, Chulin Xie, Xiaojun Xu +5
Federated learning has emerged as a promising distributed learning paradigm that facilitates collaborative learning among multiple parties without transferring raw data. However, m…
Ring-A-Bell! How Reliable are Concept Removal Methods for Diffusion Models?
Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie +6
Diffusion models for text-to-image (T2I) synthesis, such as Stable Diffusion (SD), have recently demonstrated exceptional capabilities for generating high-quality content. However,…
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei +16
Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in their capabilities, capturing the interest of practitioners and the public alike. Yet, while the…
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees
Chulin Xie, De-An Huang, Wenda Chu +4
Personalized Federated Learning (pFL) has emerged as a promising solution to tackle data heterogeneity across clients in FL. However, existing pFL methods either (1) introduce high…
Privacy of Autonomous Vehicles: Risks, Protection Methods, and Future Directions
Chulin Xie, Zhong Cao, Yunhui Long +3
Recent advances in machine learning have enabled its wide application in different domains, and one of the most exciting applications is autonomous vehicles (AVs), which have encou…
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
Chulin Xie, Yunhui Long, Pin-Yu Chen +4
Federated learning (FL) provides an efficient paradigm to jointly train a global model leveraging data from distributed users. As local training data comes from different users who…
UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
Xiaoyuan Liu, Tianneng Shi, Chulin Xie +9
Federated Learning (FL) has become a practical and widely adopted distributed learning paradigm. However, the lack of a comprehensive and standardized solution covering diverse use…
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
Wenda Chu, Chulin Xie, Boxin Wang +5
Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to…
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM
Chulin Xie, Pin-Yu Chen, Qinbin Li +3
Federated learning (FL) enables distributed resource-constrained devices to jointly train shared models while keeping the training data local for privacy purposes. Vertical FL (VFL…
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks
Chulin Xie, Minghao Chen, Pin-Yu Chen +1
Federated Learning (FL) as a distributed learning paradigm that aggregates information from diverse clients to train a shared global model, has demonstrated great success. However,…