{"data":{"id":"a588f31b-f583-4b7a-a7d3-c1ea1c2e03d2","title":"PREFed: An Effective and Stealthy Static-Anchor Backdoor Attack via Trigger Pre-Optimization in Federated Learning","summary":"PREFed is a backdoor attack (a method to secretly inject malicious behavior into AI models) designed for federated learning (a distributed machine learning approach where multiple parties train a model together without sharing raw data). Unlike previous attacks that continuously adapt their malicious updates during training, PREFed pre-optimizes its trigger patterns (the inputs that activate the backdoor) before training starts, making the attack harder to detect while reducing computational overhead.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11622588","publishedAt":"2026-07-23T13:16:52.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_poisoning"],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-07-23T13:16:52.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.92,"researchCategory":"peer_reviewed","atlasIds":null}}