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arXiv · 2602.16944

Exact Certification of Data-Poisoning Attacks Using Mixed-Integer Programming

Philip Sosnin·Jodie Knapp·Fraser Kennedy·Josh Collyer·Calvin Tsay
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Abstract

This work introduces a verification framework that provides both sound and complete guarantees for data poisoning attacks during neural network training. We formulate adversarial data manipulation, model training, and test-time evaluation in a single mixed-integer quadratic programming (MIQCP) problem. Finding the global optimum of the proposed formulation provably yields worst-case poisoning attacks, while simultaneously bounding the effectiveness of all possible attacks on the given training pipeline. Our framework encodes both the gradient-based training dynamics and model evaluation at test time, enabling the first exact certification of training-time robustness. Experimental evaluation on small models confirms that our approach delivers a complete characterization of robustness against data poisoning.

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Philip Sosnin, Jodie Knapp, Fraser Kennedy, Josh Collyer, Calvin Tsay. 2026-02-18. Exact Certification of Data-Poisoning Attacks Using Mixed-Integer Programming. https://arxiv.org/abs/2602.16944

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