{"data":{"id":"df46f608-4e54-4c05-8ac6-dd6f0f83a968","title":"QuEST: Quantization-Conditioned Efficient Stealthy Trojan","summary":"QuEST is a framework that makes quantization-conditioned backdoor attacks on deep learning models stealthier and cheaper to train. It uses a stealth-optimized training scheme with parametric backdoor injection and trigger scaling augmentation, plus information-guided parameter sharing that relies on parameter redundancy analysis and Fisher divergence metrics. The authors report 18.75% better stealth performance and 26.66% lower computational cost on average versus state-of-the-art methods, while keeping competitive attack success rates.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11422282","publishedAt":"2026-03-05T13:17:20.000Z","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":"2026-03-05T13:17:20.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.95,"researchCategory":"peer_reviewed","atlasIds":null}}