3 papers
cs.CR2026
SoK: Systematizing LLM Prompt Security: Taxonomies, Datasets, and Unified Evaluation of Attacks and Defenses
Hanbin Hong, Shuang Wu, Shuya Feng +6
Large Language Models (LLMs) are increasingly used as interfaces to information, code, and real-world services, making prompt-level security failures a practical concern. Although…
cs.CR2026
Revisiting Privacy Leakage in Machine Unlearning: Membership Inference Beyond the Forgotten Set
Jie Fu, Nima Naderloui, Da Zhong +2
Machine unlearning (MU) has emerged as a key mechanism for ensuring data privacy and regulatory compliance by enabling models to forget specific training samples. However, recent s…
cs.CR2025
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
Nima Naderloui, Shenao Yan, Binghui Wang +4
Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning en…