{"data":{"id":"d3a55f84-d06f-4658-b5ad-2ae9972abb12","title":"Robust Decentralized Fairness Auditing","summary":"Auditopus is a decentralized method for auditing a large language model's fairness, where multiple auditors each query the LLM and share only cumulative statistics vectors instead of raw queries. The authors show that a single adversarial auditor can fabricate these vectors to make an unfair LLM appear fair, and that the scheme counters this by down-weighting auditors whose vectors are statistically inconsistent with earlier ones. Against an optimizing attacker, it reduces audit error by up to 78% on average relative to no defense and at least 62% relative to robust aggregation baselines.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research","policy"],"sourceUrl":"https://arxiv.org/abs/2610.10199v1","publishedAt":"2026-10-07T15:00:14.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["other"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["LLM (unnamed pre-trained LLMs)"],"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":"2026-10-07T15:00:14.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"api","llmSpecific":true,"classifierConfidence":0.93,"researchCategory":"preprint","atlasIds":null}}