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New research reveals label spoofing attacks targeting ML malware detection

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces "label spoofing attacks," a novel threat where attackers subtly embed malicious patterns into benign software. These patterns trick antivirus engines into misclassifying legitimate files as harmful, thereby poisoning datasets used to train machine learning malware detectors. The study demonstrates this with AndroVenom, a method that can cause denial-of-service attacks on state-of-the-art ML models with as little as 1% poisoned samples and flip specific benign sample decisions with minimal data modification. The findings raise significant concerns about the trustworthiness of ML training processes that rely on antivirus annotations. AI

IMPACT Highlights vulnerabilities in ML training data integrity, potentially impacting the reliability of AI-driven security systems.

RANK_REASON Research paper detailing a novel attack vector against ML systems. [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 research reveals label spoofing attacks targeting ML malware detection

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Research paper detailing a novel attack vector against ML systems. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Tianwei Lan, Luca Demetrio, Farid Nait-Abdesselam, Yufei Han, Simone Aonzo ·

    Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection

    arXiv:2503.11841v2 Announce Type: replace-cross Abstract: Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as trusted sources of malware annotations. But what if attackers could manipulate these la…

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