AI RESEARCH
Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
arXiv CS.LG
•
ArXi:2605.04698v1 Announce Type: cross 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.