Cybercriminals Imitate Antivirus Site to Distribute Venom RAT and Steal Crypto Wallets

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May 27, 2025Ravie LakshmananMalware / Cybersecurity

Recent Cybersecurity Threat: Fake Antivirus Campaign Uncovered

Cybersecurity experts have unveiled a troubling new scheme involving a counterfeit website that promotes fake antivirus software attributed to Bitdefender. This deceptive initiative is designed to lure unsuspecting victims into downloading a remote access trojan (RAT) known as Venom RAT.

The Mechanism Behind the Scam

The fraudulent website, “bitdefender-download[.]com,” entices visitors with the promise of downloading a Windows-compatible version of Bitdefender antivirus. When users click the conspicuous “Download for Windows” button, they are redirected to a file hosted on a Bitbucket repository, which ultimately connects to an Amazon S3 bucket. It is important to note that the Bitbucket account associated with this malicious activity has since been deactivated.

Within the downloaded ZIP file, labeled “BitDefender.zip,” lies an executable file titled “StoreInstaller.exe.” This file contains malware configurations linked to Venom RAT, along with components from the open-source frameworks SilentTrinity and StormKitty stealer.

The Capabilities of Venom RAT

Venom RAT is an extension of Quasar RAT that is engineered to gather sensitive information and offer continual remote access to cybercriminals. According to DomainTools, the fraudulent site mimicking Bitdefender shares similarities with other malicious domains that have previously targeted customers from banks and IT service providers, such as the Royal Bank of Canada and Microsoft.

The DTI team noted that these various tools operate synergistically: Venom RAT infiltrates the target’s system, StormKitty extracts passwords and details from digital wallets, and SilentTrinity enables the attacker to maintain stealth and control.

A Growing Trend in Cyber Attacks

This incident highlights a worrisome trend in cybersecurity—attackers leveraging modular malware built from easy-to-access open-source components. This “build-your-own-malware” strategy enhances the efficiency, stealth, and adaptability of such attacks.

This news comes alongside alerts from Sucuri regarding another sophisticated campaign. This involves the creation of fraudulent Google Meet pages aimed at tricking users into installing a different type of RAT, known as noanti-vm.bat. This particular script is heavily obfuscated and gives remote control over the infected computer.

Social Engineering Tactics at Play

As described by security researcher Puja Srivastava, the fake Google Meet page doesn’t request credentials directly. Instead, it uses social engineering tactics, claiming to encounter a “Microphone Permission Denied” error. Users are misled into copying and pasting a specific PowerShell command, believing it to be a necessary fix.

This incident is part of a broader spike in phishing attacks that exploit Google’s AppSheet no-code development platform. These are highly targeted and sophisticated campaigns aimed at impersonating Meta, utilizing advanced techniques including polymorphic identifiers and sophisticated man-in-the-middle proxy methods.

Phishing Campaigns Exploit Trusted Platforms

The current phishing campaigns utilizing AppSheet enable attackers to send emails at scale without being blocked by standard email security measures like SPF, DKIM, and DMARC. Notably, the emails claim to be from Facebook Support, warning of potential account deletions to provoke users into clicking on fake links that appear authentic.

This tactic often culminates in redirecting victims to an adversary-in-the-middle (AitM) phishing page that is structured to harvest login credentials and two-factor authentication (2FA) codes.

To further complicate detection and remediation efforts, the attackers make use of AppSheets’ ability to generate unique identifiers for each phishing email, presented as Case IDs within the email body. This strategy ensures that every message is unique, allowing them to bypass traditional detection systems reliant on static indicators, such as known malicious URLs or hashes.

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