{"data":{"id":"477037f6-7f91-4997-b6ed-b3024f17f2db","title":"Really Unlearned? Verifying Machine Unlearning via Influential Sample Pairs","summary":"IndirectVerify is a formal scheme for verifying whether machine unlearning requests have actually been executed, addressing a gap where existing verification relies on backdoor or membership inference attacks that a model provider can bypass through rapid fine-tuning. The authors generate influential sample pairs, trigger samples and reaction samples, using a perturbation-based method that alters only a small fraction of training samples so that reaction samples are misclassified. The paper claims this indirect influence provides greater robustness against bypassing than schemes that reuse the same samples throughout.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"http://ieeexplore.ieee.org/document/11202435","publishedAt":"2025-10-13T13:16:55.000Z","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":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}