GrayShield: Bit-Level Sanitization for Transformer Model Supply-Chain Security
Transformer models such as BERT and Vision Transformer~(ViT) achieve strong performance via densely parameterized attention backbones. However, the least significant bits~(LSBs) of their 32-bit floating-point weights can be abused as covert channels to conceal malicious payloads, posing a serious threat to the AI model supply chain. We propose \GS (\GSabbr), a lightweight, post-training, zero-data sanitization method that completely replaces the declared mantissa-LSB channel with a Gray-code-guided low-transition sequence. Complete payload-independent overwrite, whether keyed or public, makes the sanitized target bits independent of the embedded payload and gives that declared channel zer...
| Authors | Armstrong Foundjem, Tsung-Hsien Chuang, Foutse Khomh et al. |
|---|---|
| Primary category | cs.CR |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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