Explainer-guided Targeted Adversarial Attacks against Binary Code Similarity Detection Models

TL;DR

This paper introduces a novel, explainability-guided adversarial attack method for binary code similarity detection models, significantly improving attack success rates and efficiency, and emphasizing the need for more robust BCSD models.

Contribution

The work presents a new optimization-based attack leveraging explainers for targeted adversarial attacks on BCSD models, outperforming prior heuristic and greedy approaches.

Findings

Higher attack success rate across scenarios

Improved efficiency and transferability of attacks

Real-world vulnerability detection case studies

Abstract

Binary code similarity detection (BCSD) serves as a fundamental technique for various software engineering tasks, e.g., vulnerability detection and classification. Attacks against such 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, failing to operate on a solid theoretical basis. Moreover, when processing programs with high complexities, such attacks tend to be time-consuming. In this work, we propose a novel optimization for adversarial attacks against BCSD models. In particular, we aim to improve the attacks in a challenging scenario, where the attack goal is to limit the model predictions to a specific range, i.e., the targeted attacks. Our attack leverages the superior capability of black-box, model-agnostic explainers in interpreting the model decision boundaries, thereby pinpointing the critical code snippet to apply semantic-preserving perturbations. The evaluation results demonstrate that compared with the state-of-the-art attacks, the proposed attacks achieve higher attack success rate in almost all scenarios, while also improving the efficiency and transferability. Our real-world case studies on vulnerability detection and classification further demonstrate the security implications of our attacks, highlighting the urgent need to further enhance the robustness of existing BCSD models.