According to McAfee’s 2026 State of the Scamiverse report, Americans now spend 114 hours a year trying to figure out what’s real and what’s fake online. That’s nearly three full workweeks lost to second-guessing messages, alerts, and links.
And when scams do succeed, they move quickly. The typical scam unfolds in about 38 minutes, leaving little room for hesitation.
That creates a gap: People want to check before they act, but the tools haven’t always met them in that moment.
ChatGPT + McAfee is designed to close that gap, bringing scam detection directly to a platform people are already using to ask questions and make decisions.
And it’s available to anyone. You don’t have to be a McAfee subscriber.
This isn’t just detection. It’s guidance in the exact moment you’re deciding what to do.
Instead of guessing, you can paste a message or drop in a screenshot and get a clear explanation of what’s risky, and what to do next, powered by McAfee’s threat intelligence.
What You Can Do with ChatGPT + McAfee
With this integration, checking something suspicious becomes as simple as asking a question.
Paste a message. Drop in a link. Upload a screenshot.
McAfee analyzes it and explains what’s going on clearly and in context.
Here’s how it works:
Feature
What it does
How it protects you
Link safety check
Paste a suspicious URL and get a reputational analysis based on McAfee threat intelligence
Scam links are often designed to look legitimate. A quick check helps avoid phishing and malware
Message analysis
Submit texts, emails, or social messages for evaluation
Many scams now rely on urgency and tone. Analysis helps surface subtle red flags
Screenshot uploads
Upload screenshots of messages, emails, or posts for review
Scams don’t always come as clean text. This makes it easier to check what you’re actually seeing
Clear explanations
Get a breakdown of why something is flagged as risky or safe
Not just a warning—an explanation that helps you recognize patterns next time
Guided next steps
Receive recommendations on what to do next
Helps prevent escalation, especially in moments of uncertainty
It’s a quick, accessible way to get answers in the moment. But it’s just one part of a broader system designed to protect you more comprehensively.
Behind the scenes, ChatGPT + McAfee is powered by the same intelligence that fuels McAfee’s broader scam protection ecosystem.
When you submit something for review:
Links are checked against known threat signals
Messages are analyzed for scam patterns and language cues
Results are translated into clear, human-readable explanations
The goal isn’t just to flag risk. It’s to help you understand it.
A New Way to Stay Ahead of Scams
Scams aren’t slowing down. If anything, they’re becoming more convincing, more personalized, and harder to detect.
That’s where ChatGPT + McAfee comes in. But this is only one part of a much bigger system designed to protect you before, during, and after a scam attempt.
With McAfee+ Advanced, multiple layers work together so you’re not left figuring it out after the damage is done:
Identity Monitoring alerts you if your personal info shows up where it should not, so you can act fast
The term ‘Vibe coding,’ first coined back in February of 2025 by OpenAI researchers, has exploded across digital platforms. With hundreds of articles and YouTube Videos discussing the dangers of Vibe coding and warning the internet about the rise of “Vibe Coders”, while others labelled it as the fundamental shift in software development and the future of coding.
Vibe Coding is an approach where the AI does heavy lifting, rather than the user. Instead of manually writing code or implementing algorithms, users describe their intent through text-based prompt, and the LLMs respond with fully functional code and explanation. Unsurprisingly, the internet is now flooded with guides on the best LLMs and prompts to generate “perfect” code.
Given the ease of generating fully functional code, McAfee Labs has also seen a rise in vibe-coded malware. In these campaigns, certain components of the kill chain contain AI-generated code, significantly reducing the effort and knowledge required to execute new malware campaigns. This shift not only makes malware campaigns more scalable but also lowers the barrier to entry for new malware authors.
Executive summary
In January 2026, McAfee Labs observed 443 malicious zip files impersonating a wide range of software, including AI image generators and voice-changing tools, stock-market trading utilities, game mods and modding tools, game hacks, graphics card and USB drivers, ransomware decryptors, VPNs, emulators, and even infostealer, cookie-stealer, and backdoor malware, to infect users.
Across the 440+ zip files, we observed 48 unique malicious WinUpdateHelper.dll variants, responsible for the infections. McAfee has been detecting variants of this threat since December 2024, although the vibe coding observed in certain components appears to be a recent addition. These files are distributed through various legitimate content delivery network (CDN) services and file-hosting websites, such as Discord, SourceForge, FOSSHub, and MediaFire, to name a few. Another website that was actively delivering this malware was mydofiles[.]com.
Here, the attackers implement volume-driven malware distribution techniques to infect as many users as possible.
Figure 1: Attack Vector
This attack begins when users surf the internet looking for tools and software that promise to simplify their tasks. Instead, they encounter trojanized zip files.
We discovered over 100 URLs actively spreading this malware, of which approximately 61 were hosted on Discord, 17 on SourceForge, and 15 on mydofiles[.]com.
On running the executable, it loads a malicious WinUpdateHelper.dll file, which redirects the user to file-hosting websites, under the disguise that they are missing crucial dependencies and tricks them into installing unrelated software, which is a distraction. Meanwhile, the DLL has already requested and executed a malicious PowerShell script from a command-and-control (C2) server.
This script infects the user’s system and downloads additional mining software, and abuses the system’s resources, or it downloads additional payloads such as SalatStealer or Mesh Agent, depending on the WinUpdateHelper.dll sample which infected the user.
In this PowerShell script, the presence of explanatory comments and structured sections strongly indicates the use of LLM models to generate this code.
Read more about this in the Using AI to generate malware? section below.
So far, we’ve observed the mining of Ravencoin, Zephyr, Monero, Bitcoin Gold, Ergo, andClorecryptocurrencies.
Due to the presence of hardcoded Bitcoin wallet credentials within these malware samples, we were able to trace on-chain transactions and identify wallets containing over $4,500 USD that are part of this campaign.
Since most of the mining activity targets privacy-focused cryptocurrencies such as Zephyr, Ravencoin and Monero, the real financial impact is likely to be nearly double the amount identified through Bitcoin tracing alone.
Geographical Prevalence
Figure 2: Geographical Prevalence
This malware campaign has specifically targeted users in the following counties, ranked by prevalence: The United States of America, followed by United Kingdom, India, Brazil, France, Canada, Australia.
Bottom Line
The availability of LLMs capable of generating code instantly, combined with the widespread accessibility of technical knowledge, has created a low-effort, high-reward environment, making malware deployment increasingly accessible.
At McAfee Labs, we have been doing hard work so that you don’t need to worry. But it always helps to be informed and educated on the latest threat that steps into the threat landscape. We will continue monitoring these campaigns to ensure our customers remain informed and protected across platforms.
Technical Analysis
Impersonated Applications
Here we see malware distribution at a large scale and by analyzing the filenames of these ZIP archives, we can infer to the users that are being targeted. These are some of the names we’ve witnessed in the wild.
Figure 3: Malware Impersonating gaming software
The attackers are actively impersonating video game cheats and game mods for popular titles, and well-known script executors for Roblox, such as Delta Executor and Solara as seen above.
Figure 4: Malware Impersonating tools, malware and drivers
Names such as Panther-Stealer and Zerotrace-Stealer indicate that even users looking for malware on the internet are not safe either, reinforcing the notion that there is truly no honor among thieves.
The campaign also leverages drivers and AI-themed tools as part of its lure portfolio among other tools. Interestingly, we see the name ‘DeepSeek.zip’, where attackers are exploiting a prominent LLM model, DeepSeek. McAfee had encountered these types of attacks in early 2025 and covered them extensively.
Once the user downloads the ZIP archive from Discord or any other website. They get the following set of files.
Figure 5: Files within the zip archive.
Here, the executable named ‘gta-5-online-mod-menu.exe’ (Highlighted in Blue) is a legitimate and clean file. Whereas the file named ‘WinUpdateHelper.dll’ (Highlighted in Red) is malicious.
Figure 6: Command Prompt misinforming the user
On executing ‘gta-5-online-mod-menu.exe’, the malicious DLL is loaded. The user is informed that they are missing dependencies, and they’re redirected to the following URL via default browser.
Here, within the URL, a tracker variable is used to identify which malware has infected the user. In this instance, it was ‘gta-5-online-mod-menu’.
Figure 7: Website prompting users to download dependencycore.zip
Dependecycore.zip is a setup file. On execution, it installs unrelated 3rd party software on the victim’s system.
Figure 8: Files dropped by Dependecycore.zip in temp folder
In this instance, iTop Easy Desktop was installed.
This unwanted installation is meant to subvert users’ attention. As, the WinUpdateHelper.dll has already connected to the C2 server and infected the system.
Stage 1 Payload – Malicious Functionality
Once the redirection code is executed, the malware executes the malicious code.
Figure 9: Malicious code within WinUpdateHelper.dll
In the above code snippet, which is present in the WinUpdateHelper.dll, we can see that a new service has been created under the name “Microsoft Console Host” to make it appear to be benign (Highlighted in Red). The parameters passed to this service ensure that it executes at system boot. This is done to maintain persistence in the system.
The service executes a PowerShell command that dynamically generates the C2 domain using the UNIX time stamp.
Using the following code, $([Math]::Floor([DateTimeOffset]::UtcNow.ToUnixTimeSeconds() / 5000000) * 5000000).xyz
It generates a domain name that changes once every 5,000,000 seconds or 58 days.
The latest C2 domain we’ve discovered that is up and running is 1770000000[.]xyz/script?id=fA9zQk2L0M&tag=WinUpdateHelper
During our analysis we observed the following domain 1765000000[.]xyz/script?id=fA9zQk2L0M&tag=WinUpdateHelper, which is present in the following images.
Here the id=fA9zQk2L0M is randomly generated, to uniquely identify the user and tag=WinUpdateHelper is used to identify the malware campaign.
The malware connects to the above-mentioned C2 server to download a PowerShell script and execute it in memory. This fileless execution ensures improved evasion against signature-based detections.
Stage 2 Payload – PowerShell Script
Figure 10: PowerShell downloaded from the C2 server
It is funny to note here, that the first comment of this script says “# I am forever sorry” which indicates that the attacks do carry some guilt regarding their actions, but not enough to stop the campaign. We found similar comments, such as “# sorry lol”, across multiple PowerShell scripts we discovered.
The first set of commands (Highlighted in Green) are used to delete windows services and scheduled tasks. This is done to remove older or conflicting persistence mechanisms and to avoid duplicate miners from running on the same system.
The second set of commands (Highlighted in Red) are registry modifications, that adds “C:\ProgramData” to Windows Defender exclusion paths. That is, ProgramData Folder won’t be scanned by Windows Defender anymore. This exclusion allows malware to drop additional payloads to disk, without the risk of them being detected and removed.
The third set of commands (Highlighted in Blue) does exactly that. It downloads the next level payload from the URL “hxxps://1765000000[.]xyz/download/xbhgjahddaa” and stored it at this path “C:\ProgramData\fontdrvhost.exe”.
Again the name ‘fontdrvhost.exe’ imitates a legitimate Windows binary, to masquerade its true intent. After the download, the file is decoded using a simple arithmetic decryption routine. This provides protection against static signature detection and network detection.
The payload is an XMRIG miner sample. In the next command, the miner is initialized and executed. Here, we see the miner connecting to “solo-zeph.2miners.com:4444” and start CPU based Zephyr coin mining using the following wallet address: ‘ZEPHsCY4zbcHGgz2U8PvkEjkWjopuPurPNv8nnSFnM5MN8hBas8kBN4hoNKmc7uMRfUQh4Fc9AHyGxL6NFARnc217m2vYgbKxf’.
Figure 11: PowerShell downloaded from the C2 server continued
In the second half of the script, we see another miner being set up and executed using the same technique (Highlighted in Red). This time the file is stored as “RuntimeBroker.exe” in the ProgramData folder. The miner is connecting to “solo-rvn.2miners.com:7070” to mine Ravencoin and it is using the system’s GPU instead of the CPU for mining (Highlighted in Blue).
This is the wallet address used for mining in this instance ‘bc1q9a59scnfwkdlm6wlcu5w76zm2uesjrqdy4fr8r’.
Hence, we see a dual coin-mining deployment infrastructure utilizing both CPU and GPU resources to optimize mining efficiency.
Bitcoin? Interesting…
What is interesting here is that attackers have used a bitcoin wallet address for mining Ravencoin, which indicates they are using multi-coin pools for mining. The attackers are using the victims’ machine to mine Ravencoin and automatically convert the mining rewards to Bitcoin before the payout.
This is done for a variety of reasons, such as, bitcoin offers higher liquidity and has broader acceptance, but most importantly, Ravencoin is computationally easier and economically viable to mine on victim’s system. Bitcoin requires specialized ASIC hardware for profitable mining and attempting to mine Bitcoin directly on infected systems would generate negligible returns. We’ve seen the same behaviour in multiple samples.
This is a smoking gun. Unlike Zephyr coin or Monero, Bitcoin’s blockchain is fully traceable. Every Satoshi, the smallest unit of Bitcoin, can be traced across the blockchain from the moment it was mined to its current holder. From there, it becomes easy to determine how much cryptocurrency the threat actor is receiving. More on this later.
Anti-Analysis Techniques
The attackers have meticulously designed the campaign and have implemented various anti-analysis techniques to thwart researchers.
The PowerShell script we’ve seen above is responsible for downloading and initializing the coin miner samples. It is only accessible via PowerShell. If we try to access the server via Curl, we get the following response.
Figure 12: 301 Response from the server
This indicates that the server is actively monitoring the User-Agent of incoming requests and deploys the payload only when the request originates from PowerShell.
Similarly, the URLs embedded within the PowerShell script that download the next payload are unique to each victim and remain active for 60 seconds. After that, they return a 404 Not Found error.
Figure 13: URLs within the PowerShell
These techniques are meant to confuse and disorient researchers, making the analysis difficult.
Using AI to generate malware?
While working on this malware campaign, we came across over 440 unique zip files. These same zip files were distributed with over 1700 different names, targeting various software.
Across these 440 zip files, we noticed 48 unique variants of WinUpdateHelper.dll. These 48 files can be clustered together into 17 distinct kill chains, each featuring their own C2 infrastructure, misleading installation setups, second-stage PowerShell scripts and final payloads, yet the cryptocurrency wallet credentials remain similar.
In the above technical analysis, we’ve only covered 1 kill chain. Yet, across these 17 kill chains, we’ve noticed the flow remain the same.
Figure 14: PowerShell Script with LLM-Generated Comments
Across multiple second stage payloads, we encounter multiple comments such as the following, embedded within the code:
# === Create and execute run.bat in C:\ProgramData ===
:: This batch file:
:: – Creates the hidden folder C:\ProgramData\cvtres if it doesn”t exist (using CMD attrib for hidden + system)
:: – Downloads cvtres.exe from your GitHub URL
:: – Saves it to C:\ProgramData\cvtres\cvtres.exe
:: – Executes it immediately
:: – Runs completely hidden/minimized (no window visible)
The presence of such explanatory-style comments indicates that large language models were likely used during the development of these scripts. Especially, the comment “Downloads cvtres.exe from your GitHub URL”, where ‘Your GitHub URL’ refers to the threat actor’s GitHub repository that is hosting the malware, which indicates potential vibe coding.
Tracking Bitcoin Across the Blockchain
During analysis of this malware campaign, we came across few instances where the final payload was Infostealer malware. In most cases it was coin miner samples. In these cases, we encountered wallet credentials and mining pool URLs for several alternative cryptocurrencies such as Ravencoin, Zephyr, Monero, which aren’t traceable.
Fortunately, we came across 7 bitcoin wallets that are part of this malware campaign and are actively receiving mined cryptocurrency.
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