XLoader remains one of the most challenging malware families confronting cybersecurity researchers.
This sophisticated information-stealing loader emerged in 2020 as a rebrand of FormBook and has evolved into an increasingly complex threat.
The malware’s code decrypts only at runtime and sits protected behind multiple encryption layers, each locked with different keys hidden throughout the binary.
Even automated sandbox analysis tools struggle against XLoader’s aggressive evasion techniques that block malicious execution when virtual environments are detected.
Check Point researchers identified a breakthrough approach to analyzing XLoader by leveraging generative artificial intelligence.
The latest XLoader version 8.0 sample presented significant obstacles with customized encryption schemes, obfuscated API calls, and extensive sandbox evasion techniques.
The malware authors release new versions regularly, changing internal mechanisms and adding anti-analysis methods that render previous research quickly outdated.
The research demonstrated how ChatGPT accelerated static reverse engineering from days to hours.
By exporting IDA Pro database contents and analyzing them through cloud-based artificial intelligence, researchers showed deep analysis could proceed without maintaining live disassembler sessions.
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This approach removed dependency on heavy local tooling while making results reproducible and easier to share.
Decrypting XLoader’s Built-in Protection
XLoader version 8.0 implements sophisticated protection mechanisms through a built-in crypter that wraps the main payload in two rounds of RC4 encryption.
The first layer applies RC4 decryption to the entire buffer, followed by a second pass processing 256-byte chunks using a different key.
Each encryption round requires specific keys derived through complex algorithms scattered across multiple functions.
Check Point analysts noted the main payload undergoes this dual-layer encryption scheme, with Stage-1 and Stage-2 keys calculated through separate derivation processes.
The Stage-1 key (20EBC3439E2A201E6FC943EE95DACC6250A8A647) and Stage-2 key (86908CFE6813CB2E532949B6F4D7C6E6B00362EE) were successfully extracted through artificial intelligence-assisted analysis combined with runtime debugging validation.
The complete unpacking process traditionally consuming days of manual reverse engineering, was compressed into approximately 40 minutes, offering defenders fresher indicators of compromise.
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