Backdoor Channels Hidden in Latent Space: Extending Cryptographic Undetectability to Modern Neural Networks
Recent cryptographic results establish that neural networks can be backdoored such that no efficient algorithm can distinguish them from a clean model. These guarantees, however, have been confined to stylised architectures of limited practical relevance, leaving open whether comparable undetectability extends to modern, end-to-end trained networks. We construct such an attack mechanism for state-of-the-art architectures, closely aligned to the cryptographic notion of undetectability, by identifying backdoor channels as learned latent directions, and show that the question of undetectability reduces to a hypothesis test between two unknown distributions over model parameters, which we con...
| Authors | Marte Eggen, Eirik Reiestad, Kristian Gjøsteen et al. |
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| Primary category | cs.CR |
| Published | 13 May 2026 |
| Last updated at source | 6 September 2026 |
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