BLens: Contrastive Captioning of Binary Functions Using Ensemble Embedding
MCML Authors
Johannes Kinder
Prof. Dr.
Associate
Abstract
Johannes Kinder
Prof. Dr.
Associate
Abstract
Function names can greatly aid human reverse engineers, which has spurred the development of machine learning-based approaches to predicting function names in stripped binaries. Much current work in this area now uses transformers, applying a metaphor of machine translation from code to function names. Still, function naming models face challenges in generalizing to projects unrelated to the training set. In this paper, we take a completely new approach by transferring advances in automated image captioning to the domain of binary reverse engineering, such that different parts of a binary function can be associated with parts of its name. We propose BLens, which combines multiple binary function embeddings into a new ensemble representation, aligns it with the name representation latent space via a contrastive learning approach, and generates function names with a transformer architecture tailored for function names. Our experiments demonstrate that BLens significantly outperforms the state of the art. In the usual setting of splitting per binary, we achieve an F1 score of 0.79 compared to 0.70. In the cross-project setting, which emphasizes generalizability, we achieve an F1 score of 0.46 compared to 0.29. Finally, in an experimental setting reducing shared components across projects, we achieve an F1 score of 0.32 compared to 0.19.
inproceedings
USENIX-Security 2025
34th USENIX Security Symposium. Seattle, WA, USA, Aug 13-15, 2025.
Authors
T. Benoit • Y. Wang • M. Dannehl • J. KinderLinks
PDFResearch Area
BibTeXKey: BWD+25