Large Language Models (LLMs) have significantly impacted numerous domains, including
Software Engineering (SE). Many recent publications have explored LLMs applied to …

Large language models (LLMs) have demonstrated significant potential in various tasks,
including vulnerability detection. However, current efforts in this area are preliminary, lacking …

The rapid advancements in large language models (LLMs) have opened new avenues
across various fields, including cybersecurity, which faces an ever-evolving threat landscape …

Binary code similarity detection (BCSD) is a fundamental technique for various applications.
Many BCSD solutions have been proposed recently, which mostly are embedding-based …

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 …

When mobile meets LLMs, mobile app users deserve to have more intelligent usage
experiences. For this to happen, we argue that there is a strong need to apply LLMs for the …

Decompilation aims to restore compiled code to human-readable source code, but struggles
with details like names and structure. Large language models (LLMs) show promise for …

Language model approaches have recently been integrated into binary analysis tasks, such
as function similarity detection and function signature recovery. These models typically …

Neural decompilers are machine learning models that reconstruct the source code from an
executable program. Critical to the lifecycle of any machine learning model is an evaluation …

Human-Oriented Binary Reverse Engineering (HOBRE) lies at the intersection of binary and
source code, aiming to lift binary code to human-readable content relevant to source code …