Weaponizing Ground Truth: Data Poisoning Attacks by Exploiting Boundary Misalignment Between Antivirus Software and Learning-Based Detectors

arXiv:2609.31003 · cs.CR · Submitted 2026-09-25 · Read on arXiv

cs.CR

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/erocarrera/pefile

Project page: https://virustotal.github.io/yara

Terminology

• Aggregation — 16× in this paper · explained in 1 episode
• Poisoning Ratio — 16× in this paper
• F1 Score — 7× in this paper · explained in 3 episodes
• Attack Success Rate — 4× in this paper
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Spectral Signature — 4× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Label Reversal Rate — 2× in this paper
• Provenance — 2× in this paper · explained in 4 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attack Success Rate (ASR) — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models
• SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
• MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models
• Microsoft Malware Classification Challenge
• Malware Detection by Eating a Whole EXE
• The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices
• Deep Partition Aggregation: Provable Defense against General Poisoning Attacks
• Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers

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