15 citations · 16 across the 6 of their papers we have counts for
6 papers
SafeTy Reasoning Elicitation Alignment for Multi-Turn Dialogues
Martin Kuo, Jianyi Zhang, Aolin Ding +12
Malicious attackers can exploit large language models (LLMs) by engaging them in multi-turn dialogues to achieve harmful objectives, posing significant safety risks to society. To…
Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility
Martin Kuo, Jingyang Zhang, Jianyi Zhang +9
With the rise of large language models (LLMs), increasing research has recognized their risk of leaking personally identifiable information (PII) under malicious attacks. Although…
H-CoT: Hijacking the Chain-of-Thought Safety Reasoning Mechanism to Jailbreak Large Reasoning Models, Including OpenAI o1/o3, DeepSeek-R1, and Gemini 2.0 Flash Thinking
Martin Kuo, Jianyi Zhang, Aolin Ding +6
Large Reasoning Models (LRMs) have recently extended their powerful reasoning capabilities to safety checks-using chain-of-thought reasoning to decide whether a request should be a…
FedProphet: Memory-Efficient Federated Adversarial Training via Robust and Consistent Cascade Learning
Minxue Tang, Yitu Wang, Jingyang Zhang +5
Federated Adversarial Training (FAT) can supplement robustness against adversarial examples to Federated Learning (FL), promoting a meaningful step toward trustworthy AI. However,…
FADE: Enabling Federated Adversarial Training on Heterogeneous Resource-Constrained Edge Devices
Minxue Tang, Jianyi Zhang, Mingyuan Ma +5
Federated adversarial training can effectively complement adversarial robustness into the privacy-preserving federated learning systems. However, the high demand for memory capacit…
FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective
Jingwei Sun, Ang Li, Louis DiValentin +3
Federated learning (FL) is a popular distributed learning framework that trains a global model through iterative communications between a central server and edge devices. Recent wo…