all AI news
Mudjacking: Patching Backdoor Vulnerabilities in Foundation Models
Feb. 26, 2024, 5:43 a.m. | Hongbin Liu, Michael K. Reiter, Neil Zhenqiang Gong
cs.LG updates on arXiv.org arxiv.org
Abstract: Foundation model has become the backbone of the AI ecosystem. In particular, a foundation model can be used as a general-purpose feature extractor to build various downstream classifiers. However, foundation models are vulnerable to backdoor attacks and a backdoored foundation model is a single-point-of-failure of the AI ecosystem, e.g., multiple downstream classifiers inherit the backdoor vulnerabilities simultaneously. In this work, we propose Mudjacking, the first method to patch foundation models to remove backdoors. Specifically, given …
abstract ai ecosystem arxiv attacks backdoor become build classifiers cs.cr cs.cv cs.lg ecosystem failure feature foundation foundation model general type vulnerabilities vulnerable
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Founding AI Engineer, Agents
@ Occam AI | New York
AI Engineer Intern, Agents
@ Occam AI | US
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Data Scientist (Database Development)
@ Nasdaq | Bengaluru-Affluence