{"data":{"id":"1c6e9c1d-aef0-4fe2-b822-f206a6c22dc0","title":"MU-MIA: Machine Unlearning for Membership Inference Attacks","summary":"The paper proposes MU-MIA, a membership inference attack that uses machine unlearning to gradually reduce a model's memorization of specific samples and records a forgetting trajectory for each one. A BiLSTM-based binary classifier with attention then separates member from non-member samples using these trajectories. The unlearning step runs in a zero-shot setting that needs no real data, and the authors report that the method outperforms existing baseline attacks across different datasets and model architectures.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11553248","publishedAt":"2026-06-08T13:18:04.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["membership_inference"],"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":"2026-06-08T13:18:04.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.95,"researchCategory":"peer_reviewed","atlasIds":["AML.T0024.000"]}}