OVIG: Optimistic Verification of AI Training Integrity via Gradient Signals
For model owners outsourcing post-training, OVIG provides a practical integrity verification method that detects training deviations despite benign numerical drift from heterogeneous accelerators.
OVIG audits outsourced AI post-training by checking gradient differences against an empirical boundary from honest heterogeneous replays, achieving 0% attack success rate across language, vision, and diffusion workloads while reducing off-chain storage by 1996× and incurring only 1.143× overhead.
The rapid growth of AI has increased the demand for domain-specific post-training, while the cost and specialization of accelerator infrastructure push many model owners to outsource this process. Outsourced training lowers operational barriers, but creates a training-integrity gap: the owner receives a checkpoint, logs, and aggregate metrics without direct evidence that the declared training trajectory was faithfully executed. An untrusted provider may have incentives to deviate from that trajectory, either to save computation or to introduce targeted security risks. Auditing such deviations is difficult because floating-point execution on heterogeneous accelerators introduces benign numerical drift, making it hard to distinguish honest replay differences from integrity violations. Existing verification methods either observe training at too coarse a granularity or impose costs and deployment constraints that are impractical at scale. We present OVIG, an optimistic verification framework that audits outsourced post-training using an empirical boundary on gradient differences calibrated from honest heterogeneous replays. OVIG checks opened intervals against this boundary and combines optimistic sampling with a stride parameter $s$, which partitions training into stride-aligned intervals and retains only interval-endpoint evidence. Across shortcut training attacks and targeted manipulation attacks, OVIG maintains $0\%$ ASR on language, vision, and diffusion workloads. On Qwen3, increasing the stride from $s=1$ to $s=2000$ reduces off-chain storage and evidence transmission by $1996\times$ while preserving $0\%$ ASR; at this setting, OVIG incurs only $1.143\times$ total system overhead relative to training without verification. These results show that OVIG provides a practical integrity layer for outsourced AI post-training under heterogeneous execution.