MalPDT: Backdoor Attack Against Static Malware Detection With Plug-and-Play Dynamic Triggers

The Deep Neural Network (DNN) based detection model’s dependency on third-party crowdsourced sources poses a new security threat from backdoor attacks against malware detectors. Attackers attempt to inject hidden backdoors into the target model, allowing it to perform well on clean samples. Once the attacker-defined trigger activates the hidden backdoor, the model predictions for poisoned samples are maliciously altered. Different from existing backdoor attacks either utilize fixed triggers or generate sample-specific triggers, we explore a novel backdoor attack paradigm in malware domain and propose MalPDT, in which backdoor triggers achieve dynamic variability in trigger patterns and retain compatibility across malware samples. We train a generator capable of hiding information to produce dynamically variable encoded byte segments, which are then injected as triggers into the unused regions of PE malware in a functionality-preserving manner. In MalPDT, any combination of a malware sample and a trigger can form a poisoned sample capable of activating the backdoor, enabling plug-and-play capability. We conduct extensive experiments to validate the effectiveness of MalPDT in attacking models with or without defenses.