Generative AI Accelerates Malware Reverse Engineering: ChatGPT Breaks XLoader’s Multiple RC4 Encryption Layers in Hours

Check Point Research (CPR) has unveiled research showcasing how Generative AI tools—specifically ChatGPT—can accelerate malware reverse engineering, reducing analysis time from days to mere hours. The study centers around the notorious XLoader malware, a rebrand of the FormBook information stealer, known for its complex encryption layers, obfuscation, and evasive techniques that have long challenged analysts.

“XLoader remains one of the most challenging malware families to analyze,” the researchers explained. “Its code decrypts only at runtime and is protected by multiple layers of encryption, each locked with a different key hidden somewhere else in the binary.”

First surfacing in 2020, XLoader has evolved into a formidable multi-platform threat targeting both Windows and macOS systems. The malware is capable of information theft, process injection, sandbox evasion, and encrypted network communications.

The Check Point team highlights that new versions are released faster than researchers can analyze them, making it “a race against time.” Each iteration introduces new anti-analysis methods, changing the internal mechanisms to break previous decryption tools.

“The challenge is that XLoader’s constantly shifting tactics break automated extraction tools and scripts almost as soon as they’re developed,” CPR noted. “An automated config extractor that worked yesterday might fail today.”

To address this escalating challenge, Check Point experimented with two complementary AI-assisted workflows:

• Live integration with MCP (Model Context Protocol) – connecting ChatGPT directly with IDA Pro, x64dbg, and VMware for real-time debugging and memory inspection.
• Offline static analysis pipeline – exporting IDA data into ChatGPT’s environment to perform deep static analysis entirely in the cloud.

The second approach, which relies solely on ChatGPT’s built-in project features, proved transformative. By analyzing decompiled code, binary data, and memory structures, the AI was able to identify cryptographic algorithms, decryption keys, and C2 (command and control) infrastructure.

“Instead of spending days on painstaking manual analysis and writing decryption routines by hand, researchers can now use AI to examine complex functions, identify algorithms, and generate working tools in just hours,” CPR explained.

Using the latest XLoader 8.0 sample, CPR’s AI assistant successfully identified multiple RC4 encryption layers, obfuscated API calls, and “secure-call trampolines” that temporarily encrypted parts of memory to resist live debugging.

“The main payload block goes through two rounds of RC4: first, an RC4 decryption of the entire buffer, and then a second pass in 256-byte chunks using a different key,” the report noted.

AI-driven analysis produced working decryption scripts, revealing hidden configuration data and domain names used for command and control. One decrypted sample uncovered domains like taskcomputer[.]xyz, streamingsite[.]xyz, and goldenspoon[.]click — a mix of fake and operational servers used by threat actors.

Even when faced with complex multi-layered encryption, the AI was able to trace relationships between markers, XOR modifiers, and dynamically generated keys, ultimately decrypting over 100 functions and 175 strings in the sample.

Traditional sandboxes proved ineffective against XLoader due to its aggressive evasion techniques. As CPR wrote, “If XLoader detects signs of virtual machines or analysis tools, the malicious branch may never run at all. Even memory dumps end up with encrypted and decrypted data jumbled together.”

In contrast, AI-powered static analysis allowed researchers to bypass runtime traps entirely, working from exported disassembly snapshots. This approach proved faster, reproducible, and collaboration-friendly, enabling researchers to share results without relying on heavy local toolchains.

“Because the entire state of the analysis was captured in our export, anyone with the archive and the prompt could reproduce the analysis,” the team said.

Tasks that once required manual scripting, debugging, and reanalysis—often spanning multiple days—were completed in under one hour with the AI assistant.

“Generative AI changes this balance,” CPR concluded. “Combining cloud-based analysis and occasional MCP-assisted runtime checks, we delegated a large part of the mechanical reverse engineering to LLM. What once took days can now be compressed into hours.”

While AI cannot yet replace human intuition—especially for novel cryptographic logic or key derivation steps—it has become a critical accelerator in modern malware research.

“The heavy lifting of triage, deobfuscation, and scripting can now be accelerated dramatically,” the researchers wrote. “Faster turnaround means fresher IoCs, quicker detection updates, and a shorter window of opportunity for attackers.”

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