SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models
arXiv:2608.16931v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for EEG decoding by learning reusable representations from large-scale heterogeneous neural recordings. However, the open release of EEG foundation encoders, while facilitating downstream developments, also introduces a previously unexplored security risk: publicly available representations may make private downstream models vulnerable. This paper investigates adversarial transfer attacks in EEG foundation model deployment in a public-encoder and private-downstream setting, where attackers have white-box access to a released encoder and a small task-matched labeled reference set, but no access or query to victim parameters, outputs, or gradients. We propose Shrinkage-Whitened Proxy Cross-Entropy (SW-ProxyCE), a query-free task-aware attack framework that recovers task-level decision geometry from a small labeled reference set through shrinkage-whitened class prototypes, enabling transferable adversarial generation without training an additional surrogate classifier. We evaluated SW-ProxyCE across three EEG tasks using three general-purpose foundation encoders and a paradigm-specific pre-trained encoder, covering both linear-probing and full-fine-tuning downstream models in cross-subject and within-subject scenarios. Results demonstrated that adversarial examples generated from the public encoder and limited labeled references can effectively transfer to inaccessible downstream models. SW-ProxyCE consistently outperformed task-agnostic representation-shift attacks, revealing that the strong transferability of EEG foundation models does not necessarily lead to adversarial robustness. Our code will be available on GitHub.
-->
[Submitted on 5 Aug 2026]
Title:SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models
View a PDF of the paper titled SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models, by Linhua Cong and 2 other authors
View PDF HTML (experimental)
Abstract:Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for EEG decoding by learning reusable representations from large-scale heterogeneous neural recordings. However, the open release of EEG foundation encoders, while facilitating downstream developments, also introduces a previously unexplored security risk: publicly available representations may make private downstream models vulnerable. This paper investigates adversarial transfer attacks in EEG foundation model deployment in a public-encoder and private-downstream setting, where attackers have white-box access to a released encoder and a small task-matched labeled reference set, but no access or query to victim parameters, outputs, or gradients. We propose Shrinkage-Whitened Proxy Cross-Entropy (SW-ProxyCE), a query-free task-aware attack framework that recovers task-level decision geometry from a small labeled reference set through shrinkage-whitened class prototypes, enabling transferable adversarial generation without training an additional surrogate classifier. We evaluated SW-ProxyCE across three EEG tasks using three general-purpose foundation encoders and a paradigm-specific pre-trained encoder, covering both linear-probing and full-fine-tuning downstream models in cross-subject and within-subject scenarios. Results demonstrated that adversarial examples generated from the public encoder and limited labeled references can effectively transfer to inaccessible downstream models. SW-ProxyCE consistently outperformed task-agnostic representation-shift attacks, revealing that the strong transferability of EEG foundation models does not necessarily lead to adversarial robustness. Our code will be available on GitHub.
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2608.16931 [cs.LG]
(or arXiv:2608.16931v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.16931
arXiv-issued DOI via DataCite
Submission history
From: Linhua Cong [view email] [v1] Wed, 5 Aug 2026 06:26:45 UTC (1,711 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled SW-ProxyCE: Zero-Query Adversarial Transfer from Public EEG Encoders to Private Downstream Models, by Linhua Cong and 2 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.LG
new | recent | 2026-08
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
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
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)