Flash: Query-Efficient Black-Box Static Malware Evasion through Transferable GAN-Guided Modification Sequences

Machine learning (ML)–based static malware detectors are widely deployed for Portable Executable (PE) files due to their scalability and efficiency, yet they remain vulnerable to carefully crafted adversarial perturbations. Existing black-box evasion methods either rely on transfer attacks, which break down when surrogate and target decision boundaries diverge, or on query-driven searches, which require impractically many queries. We present Flash, a two-phase adversarial framework tailored for static PE malware detection that integrates the strengths of both approaches. In the first phase, a generative adversarial network is trained against heterogeneous surrogate detectors to generate function-preserving PE modifications with inherent evasiveness. In the second phase, an evolutionary optimizer refines these sequences directly against the target model with a dual-objective fitness that balances evasion success and minimal perturbation cost. Experiments on 12,039 VirusShare PE files and six state-of-the-art static detectors demonstrate that Flash reduces query counts by 86% while maintaining bypass rates above 95.8%. Furthermore, adversarial training with Flash-generated samples reduces attack success rates by 82.4%, highlighting Flash’s utility for both exposing vulnerabilities and strengthening the robustness of static PE malware detectors.