{"data":{"id":"8263dc09-d236-4fe7-911a-6631f6634e70","title":"Do More With Less: Architecture-Agnostic and Data-Free Extraction Attack Against Tabular Model","summary":"Researchers developed TabExtractor, a tool that can steal tabular models (AI systems trained on spreadsheet-like data) without needing access to the original training data or knowing how the model was built. The attack works by creating synthetic data samples and using a special neural network architecture called a contrastive tabular transformer (CTT, a type of AI that learns by comparing similar and different examples) to reverse-engineer a clone of the victim model that performs almost as well as the original. This research shows that tabular models face serious security risks from extraction attacks.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11202598","publishedAt":"2025-10-13T13:16:55.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_theft"],"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":"advanced","impactType":["confidentiality","integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}