Bit-Exact AI Inference Verification Without Performance Tradeoffs
Verifying claims about AI workloads is a prerequisite for credible AI governance of covert adversaries (who comply with monitoring only when detection likelihood is high), yet the apparent non-determinism of GPU floating-point arithmetic forces auditors to accept approximate output matches. Covert adversaries can exploit unverifiable degrees of freedom in monitored computation. Attack vectors include steganography, unreported modification of inference software, and covert computation via unreported batch elements. Empirically, we analyze how modern inference engines (vLLM, HF transformers) produce deterministic but non-invariant outputs, without needing to set performance-compromising det...
| Authors | Naci Cankaya |
|---|---|
| Primary category | cs.CR |
| Published | 29 May 2026 |
| Last updated at source | 5 June 2026 |
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