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We present PyRAT, a tool based on abstract interpretation to verify the safety and robustness of neural networks. PyRAT uses multiple abstractions to find the reachable states of a neural network starting from its input. Its analysis is fast and accurate. PyRAT has already been used in several industrial and academic collaborations, to ensure safety guarantees, with its second place at the VNN-Comp 2024 showcasing its performance.

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Verifying Neural Networks with PyRAT

  • 会议论文

  • 发表日期：

    2026年

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  • Augustin Lemesle,
  • Julien Lehmann,
  • Tristan Le Gall,
  • Zakaria Chihani

摘要

We present PyRAT, a tool based on abstract interpretation to verify the safety and robustness of neural networks. PyRAT uses multiple abstractions to find the reachable states of a neural network starting from its input. Its analysis is fast and accurate. PyRAT has already been used in several industrial and academic collaborations, to ensure safety guarantees, with its second place at the VNN-Comp 2024 showcasing its performance.