AI Assurance DirectoryAI assurance & verification
Marabou
Evidence strength: Strong How this is scored
SMT-based neural-network verifier (Marabou 2.0). ONNX main format; also .nnet, TensorFlow and VNNLIB. pip install maraboupy (Python 3.8–3.11). Default: DeepPoly bound tightening and DeepSoI. Optional Gurobi, parallelism and Split-and-Conquer. ACAS Xu example. Research product.
Certification evidence
Cited literature: 3 DO-178C / related aerospace guidance requirements — see which with Pro
Analyst assessment: 1 NIST AI RMF function or subcategory — see which with Pro
Certification relevance. peer-reviewed or agency literature maps it to DO-178C / related aerospace guidance (3 requirements).
Cited literature: Marabou 2.0: A Versatile Formal Analyzer of Neural Networks (CAV 2024, 2024) — a named DO-178C / related aerospace guidance requirement.
Paper or report · The quoted wording is Pro.
Listing detail and source evidence (Pro)
Included on Pro, Team and Enterprise. Public summaries, tags and official links on this page stay free.
Cited literature and analyst assessments (Pro)
Included on Pro, Team and Enterprise. Public summaries, tags and official links on this page stay free.
Procurement evidence pack (Pro) — print cited mappings and analyst notes. The print view is not on this page.
- Vendor / publisher
- Neural Network Verification project (Stanford and collaborators)
- Type
- Open source
- Open source
- Open source · BSD-3-Clause on the official repository https://github.com/NeuralNetworkVerification/Marabou. Badge is used only when that repo shows an OSI-approved license.
- Official site
- https://github.com/NeuralNetworkVerification/Marabou
- Industries
- Frameworks named on cited source
- None tagged. The cited pages do not name a framework we cover.
- Last verified
Listing is not an endorsement, certification or recommendation. Capabilities, availability and framework support can change. Confirm with the vendor and the relevant authority. See the disclaimer.