{"data":{"id":"3fa9f007-c299-434f-a912-3a8c6793e8ef","title":"A comprehensive study of cross-domain adversarial robustness and attack transferability in image-based malware detection and classification","summary":"This paper presents a framework for evaluating adversarial robustness and attack transferability in image-based deep learning models for malware detection and classification. The authors apply image-domain attacks from FGSM to AutoAttack and test whether binary-domain manipulations remain effective after conversion to an image representation. They report average attack success rates of 64.6% for FGSM and 98.8% for AutoAttack, and accuracy drops of up to 42% from transferred binary-domain manipulations.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"https://doi.org/10.1016/j.imavis.2026.106232","publishedAt":"2026-10-05T00:00:00.000Z","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":"2026-10-05T00:00:00.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","availability"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.93,"researchCategory":"peer_reviewed","atlasIds":null}}