Federated learning (FL) enables decentralized clients to collaboratively train a global model without sharing raw data, inherently improving data confidentiality during training. With regulatory frameworks such as the General Data Protection Regulation (GDPR) mandating the right to be forgotten, Federated Unlearning (F...
Mang Su, Yang Bai, Cai-Yun Liu et al.· Neural Networks· 0 citations
This survey provides a unified analysis of how LLMs amplify web vulnerabilities across client-side, server-side, and pipeline layers while evaluating defenses and their limitations, and outlines future directions for secure AI-enabled web systems.
Nivedita Singh, Alsharif Abuadbba, Yan-Song Gao et al.· 1 citation
JITterFlip is presented, the first BFA targeting the host-side JIT serving control plane of GPU-based LLM inference, and develops a decision-guided fault-vulnerable code analysis that enables both gibberish output generation and a correct-output sponge attack.
Tai-Rui Wang, Zhi Zhang, Yan-Song Gao et al.· 0 citations
This work establishes a theoretical framework that proves that privacy leakage accumulates as more ODMM models are exposed, and proposes PRIME (Privacy Amplification RIsk from One-Dataset-Multiple-Model Exposure) to systematically assess this risk and quantify the resulting leakage using membership inference attacks (M...
Qirui Huang, Na Li, Hong-Sheng Hu et al.· arXiv.org· 0 citations
This work forms sector-targeted CTI dissemination as a multilabel classification problem, leveraging deep field knowledge of CTI structures and sector-specific threat patterns, and applies BERT, a transformer-based model, to automate the mapping of CTI events to sectors.
Fajar Wijitrisnanto, Alsharif Abuadbba, Yan-Song Gao et al.· 0 citations
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