BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning

Computer Science > Cryptography and Security [Submitted on 29 May 2026] BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning Ziqing Yang, Rui Wen, Xinlei He, Yun Shen, Michael Backes, Yang Zhang Prompt learning is a new machine learning paradigm that has attracted ample attention due to its simplicity and proven efficacy. Despite its growing adoption, the security vulnerabilities associated with this paradigm remain underexplored. In this work, we take the first step to propose BadBone, a stealthy and adaptive backdoor attack against prompt learning using bi-level optimization. Instead of backdooring the prompt learning process, we aim to compromise a backbone model such that only target downstream tasks employing prompt learning inherit the backdoor vulnerability. Extensive experiments on three different models and three datasets from various domains show that our targeted/untargeted backdoored models achieve high attack performance while maintaining utility on both pre-training and downstream tasks. Moreover, we evaluate our approach against six state-of-the-art model-level defenses, including Neural Cleanse, ABS, MNTD, NAD, CLP, and D-BR. The results demonstrate that these defenses are largely ineffective against our backdoored models and thus leave the effective defense as an important direction for future work. Comments: Accepted by IEEE Transactions on Information Forensics & Security Subjects: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2605.31246 [cs.CR]   (or arXiv:2605.31246v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.31246 Focus to learn more Related DOI: https://doi.org/10.1109/TIFS.2026.3698596 Focus to learn more Submission history From: Ziqing Yang [view email] [v1] Fri, 29 May 2026 12:46:15 UTC (14,009 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CV References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)