Unveiling the Fragility of Binary Code Similarity Detection via Targeted Attacks with Model Explanations

Binary code similarity detection (BCSD) serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against such BCSD models have therefore drawn extensive attention, aiming at misleading the models to generate erroneous predictions. Prior works have explored various approaches to generating semantic-preserving variants, i.e., adversarial samples, to evaluate the robustness of the models against adversarial attacks. However, they have mainly relied on heuristic criteria or iterative greedy algorithms to locate salient code influencing the model output, which often leads to inefficient search and high computational cost. Moreover, when processing programs with high complexities, such attacks tend to be time-consuming.

In this work, we unveil the fragility of BCSD models through a novel attack framework guided by model explanations. In particular, we focus on targeted attacks where the attack goal is to mislead the model's predictions to a specific target. Our attack leverages explainers to pinpoint critical code snippet for perturbations, reducing the exploration overhead. The evaluation results demonstrate that the proposed attacks effectively improve the attack efficiency, while maintaining comparable or higher success rates. Importantly, the speedup for perturbation target selection achieves up to 63.66×, demonstrating the practical value of explanation-guided localization. Our real-world case studies on vulnerability detection and classification further demonstrate the security implications of our attacks, highlighting fundamental robustness limitations in current BCSD models, and the urgent need for more robust designs.