# CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness Source: [CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:19:37.765Z Authors: Julien Girard-Satabin, Michele Alberti, Fran\\c{c}ois Bobot, Zakaria Chihani, Augustin Lemesle Published: 2022-06-07T06:36:27.000Z ## Abstract We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety. CAISAR provides a unified entry point for defining verification problems by using WhyML, the mature and expressive language of the Why3 verification platform. Moreover, CAISAR orchestrates and composes state-of-the-art machine learning verification tools which, individually, are not able to efficiently handle all problems but, collectively, can cover a growing number of properties. Our aim is to assist, on the one hand, the V\\&V process by reducing the burden of choosing the methodology tailored to a given verification problem, and on the other hand the tools developers by factorizing useful features-visualization, report generation, property description-in one platform. CAISAR will soon be available at https://git.frama-c.com/pub/caisar. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Neural Network Verification on cifar100 VNN-COMP 2024](): Neural Network Verification; cifar100 VNN-COMP 2024; Verified Properties Count: 68. - [Neural Network Verification on tinyimagenet VNN-COMP 2024](): Neural Network Verification; tinyimagenet VNN-COMP 2024; Verified Properties Count: 49. ## Original sources and implementation [Original paper]() [PDF]()