8 papers
Beware What You Autocomplete: Forensic Attribution of Backdoored Code Completions
Anjun Gao, Yueyang Quan, Zhuqing Liu +1
Large language models have enabled powerful code completion systems that assist developers by predicting subsequent lines of code. However, these models remain vulnerable to backdo…
Who Broke the System? Failure Localization in LLM-Based Multi-Agent Systems
Yufei Xia, Anjun Gao, Yueyang Quan +2
Large language model (LLM) based multi-agent systems enable complex problem solving through coordinated reasoning and action, but their distributed structure also introduces new ch…
Patcher: Post-Hoc Patching of Backdoored Large Language Models
Anjun Gao, Yueyang Quan, Yufei Xia +2
Large language models remain vulnerable to jailbreak backdoor attacks, where adversaries poison safety alignment data to embed hidden triggers that bypass safety mechanisms. Existi…
SecureAFL: Secure Asynchronous Federated Learning
Anjun Gao, Feng Wang, Zhenglin Wan +3
Federated learning (FL) enables multiple clients to collaboratively train a global machine learning model via a server without sharing their private training data. In traditional F…
When the Server Steps In: Calibrated Updates for Fair Federated Learning
Tianrun Yu, Kaixiang Zhao, Cheng Zhang +4
Federated learning (FL) has emerged as a transformative distributed learning paradigm, enabling multiple clients to collaboratively train a global model under the coordination of a…
SecureSplit: Mitigating Backdoor Attacks in Split Learning
Zhihao Dou, Dongfei Cui, Weida Wang +7
Split Learning (SL) offers a framework for collaborative model training that respects data privacy by allowing participants to share the same dataset while maintaining distinct fea…