Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Pretrained, Frozen, Still Leaking: Auditing Cross-Encoder Attribute Transfer in EEG Foundation Models
TL;DR
Existing audits of EEG (electroencephalogram) foundation models typically evaluate one aspect at a time, missing broader vulnerabilities. A cross-encoder transfer audit was conducted using a ridge attribute decoder to assess multiple endpoints simultaneously.
✦ Why It Matters
Engineers should adopt multi-faceted auditing approaches to better identify vulnerabilities in machine learning models.
Key Takeaways
- Implement multi-endpoint audits for comprehensive security assessments.
- Utilize cross-encoder techniques for better attribute evaluation.
- Reassess existing models for potential vulnerabilities.
- Stay updated on auditing methodologies in AI research.
Full Summary
EEG foundation models, which analyze brain activity, are often audited for privacy and security one endpoint at a time, such as raw reconstruction or membership inference. This study introduces a cross-encoder transfer audit method, utilizing a ridge attribute decoder that learns from a frozen encoder to evaluate multiple endpoints together.
The models assessed include BIOT, LaBraM, and EEGPT. Findings indicate that while individual audits may pass, they can still leak sensitive spectral attributes when evaluated collectively.
This highlights a significant gap in current auditing practices, as models deemed secure in isolation may pose risks in real-world applications. The implications suggest a need for more comprehensive auditing frameworks in the development of EEG models to ensure true privacy and security.