3 papers
cs.SE2026
OASIF: An Efficient Obfuscation-Aware Self-Improving Framework for LLM-Based Assembly Code Instruction Following and Comprehension
Xinyi Wang, Rongze Chen, Ke Wang +4
Large Language Models (LLMs) have recently shown promise in automated binary analysis, yet they remain brittle under commercial-grade obfuscation. We present OASIF, an Obfuscation-…
cs.CR2025
Adversarially Robust Assembly Language Model for Packed Executables Detection
Shijia Li, Jiang Ming, Lanqing Liu +3
Detecting packed executables is a critical component of large-scale malware analysis and antivirus engine workflows, as it identifies samples that warrant computationally intensive…
cs.SE2025
ASMA-Tune: Unlocking LLMs' Assembly Code Comprehension via Structural-Semantic Instruction Tuning
Xinyi Wang, Jiashui Wang, Jinbo Su +9
Assembly code analysis and comprehension play critical roles in applications like reverse engineering, yet they face substantial challenges due to low information density and a lac…