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Explainable AI creates new attack surfaces for machine learning models

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores how explainable AI (XAI) techniques can inadvertently create vulnerabilities for machine learning models. The research systematizes 25 studies that leverage explanations for attacks such as model extraction, membership inference, and model inversion. It categorizes five distinct paths through which adversaries can acquire explanation signals, highlighting that the risk depends on the specific signal exposed, its acquisition method, the targeted asset, and the attacker's existing knowledge. The paper argues for an end-to-end evaluation of explanation privacy, with defenses tailored to the acquisition path and the protected asset. AI

IMPACT Highlights potential privacy risks in machine learning models due to explainability features, suggesting a need for more robust defenses.

RANK_REASON The cluster contains a research paper detailing privacy attacks on machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Explainable AI creates new attack surfaces for machine learning models

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The cluster contains a research paper detailing privacy attacks on machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Abdullah Caglar Oksuz, Anisa Halimi, Erman Ayday ·

    SoK: Privacy Attacks on Machine Learning via Explainable AI

    arXiv:2609.10627v1 Announce Type: cross Abstract: Machine learning explanations reveal model behavior beyond predictions, creating attack surfaces for model confidentiality and data privacy. We systematize 25 studies that exploit explanations for model extraction, membership infe…

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