{"data":{"id":"db25fe6e-7049-48a0-9e5e-2580e6ba3514","title":"Meet Trick With Trick: Revealing Collusion Intentions in Highly Concealed Poisoning Behavior","summary":"This paper proposes METT, a divide-and-conquer detection method for data poisoning attacks on recommender systems. It combines causality inference over group-level and individual-level unfairness sequences, an early attack-target detection method named ideaT, and a disturbance tolerance mechanism for fake injections. On synthetic and real data, METT reduces the false alarm rate (FAR) by averages of 21% for S-attacks, 18% for profile injection attacks, 26% for reverse attacks, and 17% for optimal-injection attacks, and by 10% and 20% for hybrid promotion and demotion attacks.","solution":"METT, a divide-and-conquer detection method for data poisoning attacks on recommender systems.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11176436","publishedAt":"2025-09-23T13:18:33.000Z","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":null,"fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"framework","llmSpecific":false,"classifierConfidence":0.8,"researchCategory":"peer_reviewed","atlasIds":null}}