XLoader Malware Analyzed Using ChatGPT’s AI, Breaks RC4 Encryption Layers in Hours

Cybersecurity researchers have demonstrated that artificial intelligence can dramatically accelerate malware analysis, using XLoader as the case study. The work, reported on 4 November 2025, examined XLoader, a loader with information‑theft capabilities dating back to 2020, which employs multiple encryption layers, obfuscated API calls, process injection techniques and sandbox evasion.

The researchers focused on XLoader version 8.0 and used two complementary approaches that involved integrating an AI model with reverse engineering tools via the Model Context Protocol (MCP) and exporting analysis data from IDA Pro into JSON for the AI to process in its cloud sandbox.

Crucially, the RC4 encryption implementation was exposed: within eight minutes and forty-six seconds the AI identified RC4 and correctly labelled the sample as XLoader, revealing two rounds of RC4 decryption—first across the entire buffer, then through 256‑byte chunks with a different key. The overall analysis from initial entry point to key extraction took about 39 minutes and 8 seconds, a dramatic reduction from weeks typical for such complex reverse engineering tasks.