In the ever-evolving landscape of Ethereum smart contracts, the specter of vulnerabilities intensified by code reuse presents a significant challenge to the security of the blockchain. Recent studies employ deep learning for similarity analysis to identify these vulnerabilities, yet their effectiveness wanes as the volume of analyzed code increases. This article introducesSmartTrans, an advanced similarity analysis model designed to efficiently and accurately retrieve similar vulnerabilities within Ethereum bytecodes. Leveraging a novel jump-aware Transformer-based model, our approach captures the semantics and control flow of bytecodes. It not only refines the representation of functions by integrating program analysis with natural language processing techniques but also innovates a contract-level similarity detection scheme tailored for the expansive scale of contracts. Our experiments show thatSmartTransoutperforms state-of-the-art techniques at both function and contract levels, proving its capability to detect n-day vulnerabilities across Ethereum bytecodes accurately. Vulnerabilities recalling experiments show thatSmartTransachieves 95.43% and 99.37% accuracy at two levels. Furthermore, we stand out as the first work to retrieve N-day vulnerabilities across the Ethereum bytecode corpus, unveiling 4,988 vulnerable contracts. Our methodology secures an accuracy of 88.60%, which is 1.30 times higher than the best baseline.

Binary code representation learning has shown significant performance in binary analysis tasks. But existing solutions often have poor transferability, particularly in few-shot and zero-shot scenarios where few or no training samples are available for the tasks. To address this problem, we present CLAP (Contrastive Language-Assembly Pre-training), which employs natural language supervision to learn better representations of binary code (i.e., assembly code) and get better transferability. At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations (in natural language), resulting a model able to generate better embeddings for binary code. To enable this alignment training, we then propose an efficient dataset engine that could automatically generate a large and diverse dataset comprising of binary code and corresponding natural language explanations. We have generated 195 million pairs of binary code and explanations and trained a prototype of CLAP. The evaluations of CLAP across various downstream tasks in binary analysis all demonstrate exceptional performance. Notably, without any task-specific training, CLAP is often competitive with a fully supervised baseline, showing excellent transferability.

Binary code similarity detection (BCSD) is a fundamental technique for various applications. Many BCSD solutions have been proposed recently, which mostly are embedding-based, but have shown limited accuracy and efficiency especially when the volume of target binaries to search is large. To address this issue, we propose a cost-effective BCSD framework, CEBin, which fuses embedding-based and comparison-based approaches to significantly improve accuracy while minimizing overheads. Specifically, CEBin utilizes a refined embedding-based approach to extract features of target code, which efficiently narrows down the scope of candidate similar code and boosts performance. Then, it utilizes a comparison-based approach that performs a pairwise comparison on the candidates to capture more nuanced and complex relationships, which greatly improves the accuracy of similarity detection. By bridging the gap between embedding-based and comparison-based approaches, CEBin is able to provide an effective and efficient solution for detecting similar code (including vulnerable ones) in large-scale software ecosystems. Experimental results on three well-known datasets demonstrate the superiority of CEBin over existing state-of-the-art (SOTA) baselines. To further evaluate the usefulness of BCSD in real world, we construct a large-scale benchmark of vulnerability, offering the first precise evaluation scheme to assess BCSD methods for the 1-day vulnerability detection task. CEBin could identify the similar function from millions of candidate functions in just a few seconds and achieves an impressive recall rate of 85.46% on this more practical but challenging task, which are several order of magnitudes faster and 4.07× better than the best SOTA baseline.