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Introduction:
Attackers are exploiting the hype around generative AI to infect unsuspecting users with malware. A fake website impersonating Anthropic’s Claude AI chatbot is distributing a new Windows backdoor, dubbed Beagle, through a trojanized installer. This sophisticated attack leverages a technique called DLL sideloading, abusing a signed binary from a legitimate security vendor to execute malicious code and take control of the victim’s machine.
Learning Objectives:
- Understand the anatomy of a DLL sideloading attack chain used to deploy advanced malware.
- Learn how to identify and analyze malicious domains used in malvertising and SEO poisoning campaigns.
- Acquire practical skills for detecting DLL sideloading and analyzing memory-injected payloads like DonutLoader and the Beagle backdoor.
You Should Know:
1. Deconstructing the Beagle Malware Attack Chain
The attack begins with a lure: a fake website, claude-pro[.]com, designed to look almost identical to the legitimate Claude AI site. Victims are directed here via malvertising (malicious ads) or SEO poisoning, which pushes the fake site to the top of search engine results for terms like “Claude Pro download”.
The site offers a download named Claude-Pro-windows-x64.zip, a 505 MB archive. Inside, an MSI installer drops three key files into the user’s Startup folder: a legitimate, signed G DATA antivirus updater renamed NOVupdate.exe, a malicious DLL named avk.dll, and an encrypted data file NOVupdate.exe.dat.
Step‑by‑Step Guide to the Sideloading Process
- Step 1: Execution. When the system restarts or a user logs in, the `NOVupdate.exe` (the signed binary) is executed. This file is designed to look for a specific DLL,
avk.dll, in its local directory to load. - Step 2: The Hijack. However, the attacker has replaced the legitimate DLL with their own malicious version. The binary, trusting its own signature, loads the rogue `avk.dll` into memory.
- Step 3: Decryption and Shellcode. The malicious DLL reads the encrypted `NOVupdate.exe.dat` file, uses a reversed XOR key (found by researchers) to decrypt its contents, and runs the resulting shellcode directly in memory.
- Step 4: Deploying the Loader. This shellcode is the DonutLoader, an open-source tool that can execute .NET assemblies and native code entirely in memory, leaving minimal forensic artifacts on the hard drive.
- Step 5: The Final Payload. DonutLoader then fetches the final payload: the Beagle backdoor, and deploys it into the system’s memory, evading traditional file-based antivirus detection.
Linux/Windows Commands & Tutorials: Analyzing the Artifacts
Detecting Potential Sideloading (Windows): Use `Process Monitor` (ProcMon) from Sysinternals. Filter for `Process Name` contains `NOVupdate.exe` and `Operation` is CreateFile. Look for failed or succeeded `Load Image` operations for unexpected DLL paths.
Using PowerShell to list processes and their loaded DLLs (look for suspicious paths) Get-Process -Name NOVupdate | Select-Object -ExpandProperty Modules | Format-Table -AutoSize
Extracting XOR Keys from Decryption Routines (Linux): If you have a sample of the encrypted `.dat` file, you can attempt to extract the XOR key using tools like `xortool` or CyberChef.
Example: Using xortool to find potential keys based on entropy xortool -x -l 1-32 encrypted_file.dat Look for a key that produces meaningful output (e.g., shellcode headers like MZ or PE)
Network Traffic Analysis (Both): Beagle communicates with its C2 server (license[.]claude-pro[.]com) over TCP port 443 or UDP port 8080 with AES encryption. Use `Wireshark` with a display filter to isolate this traffic:
(tcp.port == 443 or udp.port == 8080) and ip.dst == 8.217.190.58
2. The Beagle Backdoor: Capabilities and C2 Communication
Beagle is a relatively simple but effective backdoor, supporting eight commands that give an attacker full remote control over the victim’s machine. Its power lies in its in-memory execution, making it harder to detect with standard endpoint security solutions that rely on file signatures.
Step‑by‑Step Guide to Beagle’s Commands
uninstall: Removes the Beagle agent from the system.
cmd: Executes any system command (e.g., whoami, ipconfig, powershell).
upload: Exfiltrates files from the victim to the C2 server.
download: Pulls additional malicious tools or modules onto the victim’s machine.
mkdir: Creates a new directory for staging files or malware components.
rename: Renames files to evade detection or for organization.
ls: Lists directory contents to survey the file system.
rm: Deletes directories, used for cleanup or destruction.
Tutorial: Simulating & Blocking C2 Communications
- Static Analysis: To understand the full scope of capabilities, security analysts can perform static analysis on a Beagle sample. Using a disassembler like IDA Pro or Ghidra, analysts can search for the string `license[.]claude-pro[.]com` to locate the C2 communication routine.
Using command-line `strings` on a Linux analysis machine strings beagle_sample.exe | grep -i "claude-pro"
- Dynamic Analysis & Blocking (Indicators of Compromise – IoCs):
Block at the Perimeter: Network administrators can create firewall rules to block traffic to the known malicious IP `8.217.190.58` and domainlicense[.]claude-pro[.]com. On a pfSense or OPNsense firewall, this can be done by adding an alias for these IoCs and creating a block rule on the LAN interface.
DNS Sinkholing: For proactive defense, configure DNS sinkholes (e.g., using Pi-hole or a security DNS provider) to redirect any request to `license[.]claude-pro[.]com` to a benign IP address, effectively breaking the C2 channel. -
Why This Matters: The Evolution of AI-Themed Threat Campaigns
The attackers behind this campaign show a high degree of operational maturity. The infrastructure (C2 on Alibaba Cloud) is separate from the malware distribution (via Cloudflare), making takedowns more complex. They also reused code patterns and techniques (XOR keys) across other VirusTotal samples dating back to February 2026, indicating this isn’t a one-off but a sustained operation. Some samples were even linked to domains impersonating major security vendors like Trellix, CrowdStrike, and SentinelOne.
Tutorials & Hardening
Cloud & AI Security Hardening (For Organizations):
Educate Developers: Many AI tools now offer desktop clients. For DevOps and Software Development teams, implement a strict policy that all software installations, especially AI-related tools, must be approved through a secure internal portal. Prohibit downloading executables or archives directly from search engines.
Implement Application Allowlisting: Use tools like Microsoft AppLocker or Windows Defender Application Control (WDAC) to prevent unauthorized executables from running. This can stop the initial MSI installer or the `NOVupdate.exe` from executing in the first place, as it would not be on the allowlist.
Endpoint Detection and Response (EDR) Configuration: Configure EDR rules to monitor for suspicious process creation events, specifically looking for a signed binary from a third party (like G DATA) executing from a non-standard directory (e.g., `%APPDATA%` or C:\Users\Public). Standard software does not run from these locations.
Forensic Analysis of the Vectored Attack (Linux & Windows):
Memory Forensics (using Volatility): If a system is suspected of infection, memory forensics is crucial. Capture the RAM of the compromised Windows machine.
Dump memory (example using LiME on Linux or winpmem on Windows) Then analyze with Volatility volatility -f memory.dump --profile=Win10x64_19041 pslist volatility -f memory.dump --profile=Win10x64_19041 malfind -D output_folder/
The `malfind` command can often detect hidden or injected code in processes, which is a hallmark of the DonutLoader payload injection.
4. Prediction & Future Impact
This campaign is a harbinger of a larger trend: the weaponization of the public’s insatiable appetite for new technology for initial access. As organizations race to integrate generative AI tools, “Shadow AI” (the use of unsanctioned AI applications and browser extensions) will become a major attack vector.
Prediction:
We will see a rapid evolution in AI-themed malvertising within the next 6–12 months. Attackers will not only target Windows but will develop cross-platform lures, including trojanized packages for popular Python and Node.js libraries on package managers like PyPI and npm. Furthermore, the use of off-the-shelf, open-source tools like DonutLoader, combined with adaptable loaders, indicates a shift towards a “malware-as-a-service” model for AI-themed attacks, lowering the barrier to entry for less-skilled cybercriminals. Expect campaigns to pivot from Claude to other popular AI models like Google’s Gemini or Meta’s Llama.
What Undercode Say:
- The Bait is the New Exploit: Social engineering, not software vulnerabilities, remains the most effective way to breach a network. The trust in well-known AI brands is the new phishing lure.
- Memory Detection is Critical: This attack highlights the absolute necessity of EDR, memory forensics, and behavioral analysis. Traditional, signature-based antivirus is no longer sufficient to stop modern, in-memory payloads like DonutLoader.
This campaign serves as a critical reminder that security for AI-powered tools must begin with the human element. Developers, power-users, and everyday employees are on the front line. The sophistication of the technical chain (DLL sideloading for persistence, multi-stage decryption, in-memory execution) is rendered irrelevant if users are trained to check the URL before clicking “download.” Any organization relying on AI tools must immediately implement technical controls (allowlisting, EDR rules, DNS filtering) and, more importantly, conduct targeted security awareness training on the dangers of Shadow IT and malvertising for emerging technologies.
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Reported By: Varshu25 Fake – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
