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MonetaStealer Malware Powered with AI Code Attacking macOS Users in the Wild

Posted on January 15, 2026January 15, 2026 By CWS

A brand new information-stealing malware named MonetaStealer has been found actively focusing on macOS customers by means of misleading file disguises and social engineering ways.

Safety researchers at Iru first recognized this menace on January 6, 2026, after they discovered a suspicious Mach-O binary masquerading as a Home windows executable file named Portfolio_Review.exe.

The malware represents a rising concern for Mac customers, particularly these in skilled industries who incessantly obtain portfolio information from potential candidates or collaborators.

MonetaStealer is designed to extract delicate info from contaminated macOS methods, together with browser passwords, cryptocurrency pockets knowledge, Wi-Fi credentials, SSH keys, and monetary paperwork.

The malware accommodates code that particularly checks for macOS methods utilizing the verification if sys.platform != ‘darwin’, guaranteeing it solely executes on Apple units.

What makes this menace notably attention-grabbing is its heavy reliance on code generated by means of machine studying instruments, which researchers consider signifies the malware remains to be in early growth phases.

Regardless of its incomplete nature, MonetaStealer maintains a zero-detection fee on VirusTotal on the time of discovery, making it invisible to most safety options.

The Sequence analysts recognized portfolio_app.pyc as the primary payload hidden throughout the PyInstaller-compiled binary.

This Python-based malware embeds its malicious logic inside a compressed CArchive construction that bypasses primary static file scanners.

Decompilation of the code revealed Russian-language feedback and no obfuscation, suggesting the developer prioritized performance over stealth.

Keychain Password Immediate (Supply – The Sequence)

The malware shows a banner studying “PROFESSIONAL MACOS STEALER v2.0” throughout execution, together with print statements that observe its progress by means of varied knowledge theft modules.

Chrome Browser Knowledge Theft

MonetaStealer particularly targets Google Chrome browser knowledge by creating short-term copies of SQLite databases to bypass file locks.

The malware executes the command safety find-generic-password -w -a “Chrome” to retrieve the Base64 grasp key saved within the macOS Keychain, which is required for decrypting saved passwords.

This operation triggers a system immediate requesting the consumer’s keychain password, which might alert observant victims. As soon as entry is granted, the malware queries login credentials, session cookies, and looking historical past by means of focused SQL instructions.

The cookie theft module applies key phrase filtering to determine high-value targets by trying to find phrases like “financial institution,” “crypto,” “trade,” and “paypal” inside cookie host names.

This focused method permits the malware to prioritize monetary and cryptocurrency platform classes. The next code demonstrates how MonetaStealer processes stolen cookies:

print(‘[+] Stealing Chrome cookies…’)
strive:
host, identify, path, encrypted_value = row
if any((key phrase in host.decrease() for key phrase in [‘bank’, ‘crypto’,
‘exchange’, ‘paypal’])) and self.stolen_data[‘browser’][‘cookies’].append({‘host’:
host, ‘identify’: identify, ‘path’: path}):
cross
conn.shut()
besides Exception as e:
print (f’ X Error: {e}’)

The malware additionally harvests looking historical past by extracting URLs, web page titles, and go to frequencies from Chrome’s Historical past database.

This info can reveal consumer pursuits, incessantly visited providers, and potential extra targets for follow-up assaults.

All collected browser knowledge is structured into the malware’s inside storage dictionary for later exfiltration by means of a Telegram bot infrastructure recognized as “b746_mac_collector_bot” with the bot ID 8384579537.

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Cyber Security News Tags:Attacking, Code, macOS, Malware, MonetaStealer, Powered, Users, Wild

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