AI models can provably hide arbitrary capabilities
Researchers have demonstrated that neural network models can conceal arbitrary computations by embedding encrypted sub‑networks within the host model's weights. The hidden circuits are activated only when a secret trigger is presented, and they remain provably difficult to uncover even when the full model weights are available. The study used backdoor attacks and cryptographic techniques to…
Key points
- Researchers embed encrypted neural networks inside host model weights, activated only by a secret trigger.
- Hidden circuits remain provably hard to elicit even with full weight access.
- Proof of concept evaluated against fine‑tuning, weight noising, gradient trigger search, and latent adversarial training.
AI models can provably hide arbitrary capabilities
arxiv.org · 9 October 2026
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