{"data":{"id":"d934c895-7c01-43fa-b49f-aad79dda8761","title":"UAAD: A unified anomaly-aware defense against diverse backdoor attacks on graph neural networks","summary":"UAAD (Unified Anomaly-Aware Defense) is a framework that mitigates diverse backdoor attacks on graph neural networks. It builds a pseudo-clean reference distribution with spectral clustering, scores suspicious nodes with Mahalanobis distance, and prunes suspicious edges with an adaptive Gaussian Mixture Model, without fixed thresholds. In GCN-based evaluation on five real-world datasets, its mean ASR stays at or below 0.0775 against GTA, UGBA, and DPGBA, with a dataset-dependent clean-accuracy trade-off.","solution":"UAAD is the proposed defense: an anomaly-aware framework that removes suspicious edges through adaptive GMM-based pruning and uses edge-level anomaly priors for joint training.","labels":["security","research"],"sourceUrl":"https://doi.org/10.1016/j.eswa.2026.134754","publishedAt":"2026-10-17T00:00:00.000Z","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":"2026-10-17T00:00:00.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.95,"researchCategory":"peer_reviewed","atlasIds":null}}