{"data":{"id":"b4e17d7f-0f01-452f-9c5d-ddaf6b97c863","title":"AI-Shielder: Exploiting Backdoors to Defend Against Adversarial Attacks","summary":"Deep neural networks (DNNs, machine learning models with many layers that learn patterns from data) are vulnerable to adversarial attacks, where small, carefully crafted changes to input data trick the AI into making wrong predictions, especially in critical areas like self-driving cars. This paper presents AI-Shielder, a method that intentionally embeds backdoors (hidden pathways that alter how the model behaves) into neural networks to detect and block adversarial attacks while keeping the AI's normal performance intact. Testing shows AI-Shielder reduces successful attacks from 91.8% to 3.8% with only minor slowdowns.","solution":"AI-Shielder is the proposed solution presented in the paper. According to the results, it 'reduces the attack success rate from 91.8% to 3.8%, which outperforms the state-of-the-art works by 37.2%, with only a 0.6% decline in the clean data accuracy' and 'introduces only 1.43% overhead to the model prediction time, almost negligible in most cases.' The approach works by leveraging intentionally embedded backdoors to fail adversarial perturbations while maintaining original task performance.","labels":["security","research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11184428","publishedAt":"2025-09-29T13:25:32.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"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":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["integrity","safety"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}