Stress-Testing Neural Network Verifiers with Provably Robust Instances
Neural network verifiers aim to provide formal guarantees on model behavior, but existing verification benchmarks are fundamentally limited by their lack of ground-truth labels. As a result, verifier evaluation relies on indirect heuristics, which prevents exact scoring and systematic study of verifier failure modes. We address this gap by introducing a reusable framework for generating verification instances whose ground-truth robustness labels are known a priori through analytic construction. Our framework led to the discovery of multiple numeric tolerance concerns and an implementation bug in popular verifiers, highlighting the need for ground-truth labels. Additionally, to systematica...
| Authors | David Troxell, Yulia Alexandr, Sofia Hunt et al. |
|---|---|
| Primary category | cs.LG |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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