Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
Modern malware detection pipelines rely on continuous data ingestion and machine learning to counter the high volume of novel threats. This work investigates a realistic gray-box poisoning threat model targeting these pipelines. Using the secml_malware framework, we generate problem-space adversarial binaries through functionality-preserving manipulations, specifically Import Address Table (IAT) and section injections. We evaluate the impact of these poisoned samples when ingested into a defender's training set for a LightGBM malware detection model. Our empirical results demonstrate that subtle IAT-based perturbations enable compact poisoning samples that significantly degrade detection ...
| Authors | Jan Dolejš, Martin Jureček, Róbert Lórencz |
|---|---|
| Primary category | cs.CR |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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