{"data":{"id":"e2921521-b4da-490d-b9af-43c33154bc9f","title":"A Mathematical Certification for Positivity Conditions in Neural Networks With Applications to Partial Monotonicity and Trustworthy AI","summary":"This paper presents LipVor, an algorithm that certifies whether a closed-box model such as an artificial neural network is positive, based on a finite number of evaluations. Because partial monotonicity can be expressed as a positivity condition on partial derivatives, LipVor can certify partial monotonicity of a trained network without constrained architectures or piecewise linear activations. The authors suggest this could allow unconstrained networks in critical fields such as credit scoring.","solution":"N/A -- no mitigation discussed in source.","labels":["research","safety"],"sourceUrl":"http://ieeexplore.ieee.org/document/11203279","publishedAt":"2025-10-14T13:16:19.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":null,"fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":null,"llmSpecific":false,"classifierConfidence":0.8,"researchCategory":"peer_reviewed","atlasIds":null}}