Faculty
2022: Busy Beaver Award for "Privacy of Machine Learning"
2019: Best paper award at NDSS
Dr. YAng Zhang is Faculty at CISPA. His research concentrates on trustworthy machine learning (privacy, safety, and security). Moreover, he works on measuring and understanding misinformation and unsafe content like hateful memes on the Internet. Over the years, he has published multiple papers at top venues in computer science, including CCS, NDSS, Oakland, and USENIX Security. His work has received the NDSS 2019 distinguished paper award and the CCS 2022 best paper award runner-up.
European Association for Computational Linguistics (EACL)
Defeating Cerberus: Privacy-Leakage Mitigation in Vision Language Models
IEEE Transactions on Dependable and Secure ComputingBackdoor Complications: A Comprehensive Analysis and Mitigation of the Unforeseen Consequences of Backdoor Attacks
National Conference of the American Association for Artificial Intelligence (AAAI)
SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability
Conference on Neural Information Processing Systems (NeurIPS)
Adjacent Words, Divergent Intents: Jailbreaking Large Language Models via Task Concurrency
Conference on Neural Information Processing Systems (NeurIPS)
Finding and Reactivating Post-Trained LLMs’ Hidden Safety Mechanisms
Conference on Empirical Methods in Natural Language Processing (EMNLP)
Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification
IEEE International Conference on Computer Vision (ICCV)
Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions
ACM Conference on Computer and Communications Security (CCS)
UnsafeBench: Benchmarking Image Safety Classifiers onReal-World and AI-Generated Images
IEEE Transactions on Dependable and Secure ComputingRevealing the Risk of Hyper-parameter Leakage in Deep Reinforcement Learning Models
Usenix Security Symposium (USENIX-Security)
Data Duplication: A Novel Multi-Purpose Attack Paradigm in Machine Unlearning