{"data":{"id":"95d9331c-a33f-477c-906e-8ff82996646c","title":"TRACE: Data-Free Text Reconstruction Attacks against Approximate Unlearning in LLMs","summary":"This paper shows that approximate unlearning in LLMs can leave residual traces in pre- and post-unlearning model snapshots, which can be used to recover the supposedly removed training data. It proposes TRACE, which reconstructs unlearned text from the two snapshots without prior knowledge of the data, using embedding-level parameter differences and a perplexity-gap contrastive score. Experiments on TOFU, MUSE, and WMDP across multiple LLMs and six unlearning methods report high-fidelity reconstruction.","solution":"N/A -- no mitigation discussed in source.","labels":["security","privacy","research"],"sourceUrl":"https://arxiv.org/abs/2610.31995v1","publishedAt":"2026-10-22T23:28:56.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"low","attackType":["data_extraction"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["LLMs","TOFU","MUSE","WMDP"],"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":"2026-10-22T23:28:56.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["confidentiality"],"aiComponentTargeted":"model","llmSpecific":true,"classifierConfidence":0.95,"researchCategory":"preprint","atlasIds":null}}