When fixes teach: Repair-aware contrastive learning for optimization-resilient binary vulnerability detection

Deep learning (DL)-based vulnerability detection in source code are prevalent, yet detecting vulnerabilities in binary code using this paradigm remains underexplored. The few works typically treat input instructions as individual entities, failing to extract and leverage fine-grained information due to their inability to account for the inherent connections and correlations between code segments and the impact of compilation optimizations. To address these challenges, this paper proposes DELTA, a novel approach that incorporates Dynamic contrastive lEarning with vuLnerabiliTy repair Awareness to fine-tune pre-trained models, significantly enhancing the accuracy and efficiency of vulnerability detection in binary code. DELTA proceeds by standardizing assembly instructions and utilizing function pairs that represent code before and after vulnerability repair along with their versions compiled under different optimization settings as contrastive learning samples. Building on these rich and diverse training signals, DELTA fine-tunes CodeBERT using contrastive learning augmented with masked language modeling, resulting in a feature encoder CMBERT, which is adept at capturing nuanced vulnerability patterns in binary code and remain resilient to the impacts of compilation optimizations. DELTA is evaluated on the Juliet Test Suite dataset, achieving an average performance improvement of 8.04% in detection accuracy and 7.13% in F1 score compared to alternative methods.