{"data":{"id":"b6f477b6-44cb-444c-943c-80300a83190d","title":"A Unified Decision Rule for Generalized Out-of-Distribution Detection","summary":"This research paper addresses generalized out-of-distribution detection (OOD detection, where an AI system identifies inputs that are very different from its training data), which is important for AI systems used in safety-critical applications. Rather than focusing on designing better scoring functions, the authors propose a new decision rule called the generalized Benjamini Hochberg procedure that uses hypothesis testing (a statistical method for making decisions about data) to determine whether an input is out-of-distribution, and they prove this method controls false positive rates better than traditional threshold-based approaches.","solution":"N/A -- no mitigation discussed in source.","labels":["research","safety"],"sourceUrl":"http://ieeexplore.ieee.org/document/11288088","publishedAt":"2025-12-09T13:16:09.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}