{"data":{"id":"7aca02c0-5cf9-4b79-9f8b-55e3a86f9989","title":"Logit Laundering: Evading Data-Use Auditing in Large Language Models","summary":"Researchers propose Logit Laundering, an evasion framework in which a non-compliant LLM provider manipulates model outputs at inference time to suppress watermark-induced statistical signals used by data-use auditors. The method reshapes logits with a plug-and-play statistical bias estimator and a confidence-aware scaling module, leaving model parameters unchanged. Extensive evaluations reportedly evade various state-of-the-art auditing methods while preserving utility on downstream tasks.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2611.09446v1","publishedAt":"2026-11-06T13:20:30.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["other"],"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":"2026-11-06T13:20:30.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","confidentiality"],"aiComponentTargeted":"model","llmSpecific":true,"classifierConfidence":0.93,"researchCategory":"preprint","atlasIds":null}}