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New backdoor attack method requires only one poisoned data sample

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for mounting backdoor attacks on machine learning models, specifically targeting linear models and ReLU neural networks. This technique, termed a "one-poison backdoor attack," requires only a single malicious data sample and does not necessitate knowledge of individual training data points. The attack can be executed using coarse geometric bounds of the input space and training parameters, achieving zero backdooring error without significantly impacting the model's benign performance. AI

IMPACT This research highlights a significant vulnerability in machine learning models, potentially impacting the security and trustworthiness of AI systems trained on untrusted data.

RANK_REASON Academic paper detailing a new machine learning attack method. [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 →

New backdoor attack method requires only one poisoned data sample

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Academic paper detailing a new machine learning attack method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Thorsten Peinemann, Paula Arnold, Sebastian Berndt, Thomas Eisenbarth, Esfandiar Mohammadi ·

    Provable one-poison backdoor attacks on linear models and ReLU neural networks

    arXiv:2508.05600v3 Announce Type: replace Abstract: Backdoor poisoning attacks are a threat to machine learning models that are trained on data collected from untrusted sources; these attacks enable attackers to inject malicious behavior into the model that can be triggered by sp…

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