{"data":{"id":"6dc0d01a-7962-4369-baec-9bf8acd80c15","title":"Provable Adversarial Detection: Prime Quantization meets Gromov--Wasserstein","summary":"The authors ask when adversarial perturbations on vision models can be provably detected, using a geometric approach. They compare a standard CNN embedding space with a novel prime-quantized space, where small perturbations are absorbed by number-theoretic discretization, and use the geometric discrepancies between the two spaces to flag adversarial samples. They report theoretical guarantees, including Gromov-Wasserstein separation, and state that experiments show strong detection across many attack types and model families.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2610.26102v1","publishedAt":"2026-10-19T10:00:07.000Z","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":"2026-10-19T10:00:07.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.95,"researchCategory":"preprint","atlasIds":null}}