Imagine a locked box containing valuable secrets. Now imagine that the lock is incredibly complex, intentionally designed to mislead anyone trying to open it. That’s essentially what 'obfuscated' software is like—code intentionally scrambled to hide its true purpose. This is a common tactic used to protect intellectual property or conceal malicious intent. Disassembling this kind of code, to understand its function, is incredibly challenging for humans. But what if artificial intelligence could crack the code? New research introduces DISASLLM, an AI-powered disassembler that uses Large Language Models (LLMs) to take on this challenge. Traditional methods struggle with obfuscation because they rely on easily fooled heuristics or simpler machine learning techniques. DISASLLM takes a different approach. It works by first creating an initial 'guess' at the disassembled code using traditional methods. Then, the real magic happens: an LLM-powered 'classifier' steps in to analyze whether the resulting instructions make sense within the code’s context. Think of it like an experienced code detective looking for clues that don’t add up. The LLM’s strength lies in understanding code semantics—the meaning and intention behind the instructions—something previous methods lacked. DISASLLM uses a clever combination of batch processing and targeted analysis to check only the most suspicious parts of the code, making the process more efficient. When it finds errors, it tries to “fix” them by intelligently exploring alternative disassemblies, filling in missing pieces like a digital jigsaw puzzle solver. The result? DISASLLM can significantly outperform existing disassemblers when it comes to understanding obfuscated code. Specifically, it’s shown to be roughly 40% better at correctly identifying crucial instructions hidden within the deliberately confusing parts of the code. While there are still challenges in terms of speed and handling extremely complex obfuscation, DISASLLM offers a powerful new way to analyze and understand even the most cleverly disguised software, opening new doors in cybersecurity, software analysis, and intellectual property protection.