Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs
Transformer-based malware detection systems operating on graph modalities such as control flow graphs (CFGs) achieve strong performance by modeling structural relationships in program behavior. However, their robustness to adversarial evasion attacks remains underexplored. This paper examines the vulnerability of a RoBERTa-based malware detector that linearizes CFGs into sequences of function calls, a design choice that enables transformer modeling but may introduce token-level sensitivities and ordering artifacts exploitable by adversaries. By evaluating evasion strategies within this graph-to-sequence framework, we provide insight into the practical robustness of transformer-based malwa...
| Authors | Andrew Wheeler, Kshitiz Aryal, Maanak Gupta |
|---|---|
| Primary category | cs.CR |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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