{"data":{"id":"51381bad-cadc-406d-ba09-c010cd5917bc","title":"Rethinking Frequency Modeling: Tail-Aware Dynamic Adversarial Training for Long-Tailed Robustness","summary":"This paper proposes Tail-Aware Dynamic Adversarial Training (TAD-AT) to improve adversarial robustness when training data follows a long-tailed class distribution. The authors report that class frequency alone does not predict adversarial vulnerability, and that adversarial training under long-tailed data is unstable for tail classes. TAD-AT combines a frequency- and accuracy-aware training loss, a class-wise vulnerability-adjusted attack, and class-adaptive weight averaging, with experiments on long-tailed benchmarks showing significant robustness gains.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11458004","publishedAt":"2026-03-30T13:17:27.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"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":"2026-03-30T13:17:27.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","availability"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.9,"researchCategory":"peer_reviewed","atlasIds":null}}