{"data":{"id":"bfd1ada1-40a1-4746-8032-c7c76695451e","title":"Parameter-Agnostic Privacy-Preserving Machine Unlearning for Large Language Models","summary":"This research paper proposes a privacy-preserving machine unlearning mechanism for large language models. The method uses information retrieval to remove high-risk semantic meanings from model output and adds differentially-private randomization so unlearned information is statistically indiscernible. It requires neither parametric fine-tuning nor in-context prompt calibration, and the authors report theoretical privacy and unlearning guarantees plus experiments on real-world datasets.","solution":"N/A -- no mitigation discussed in source.","labels":["research","privacy"],"sourceUrl":"http://ieeexplore.ieee.org/document/11541209","publishedAt":"2026-06-01T13:17:32.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["large language models"],"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":"2026-06-01T13:17:32.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["confidentiality"],"aiComponentTargeted":"model","llmSpecific":true,"classifierConfidence":0.93,"researchCategory":"peer_reviewed","atlasIds":null}}