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June 12, 2024

Meeten Malware: A Cross-Platform Threat to Crypto Wallets on macOS and Windows

Cado Security Labs (now part of Darktrace) identified a "Meeten" campaign deploying a cross-platform (macOS/Windows) infostealer called Realst. Threat actors create fake Web3 companies with AI-generated content and social media to trick targets into downloading malicious meeting applications.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Tara Gould
Threat Researcher
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12
Jun 2024

Introduction: Meeten malware

Researchers from Cado Security Labs (now part of Darktrace) have identified a new sophisticated scam targeting people who work in Web3. The campaign includes cryptostealer Realst that has both macOS and Windows variants, and has been active for around four months. Research shows that the threat actors behind the malware have set up fake companies using AI to make them increase legitimacy. The company, which is currently going by the name “Meetio”, has cycled through various names over the past few months. In order to appear as a legitimate company, the threat actors created a website with AI-generated content, along with social media accounts. The company reaches out to targets to set up a video call, prompting the user to download the meeting application from the website, which is Realst info stealer. 

Meeten

Screenshot of fake company homepage
Figure 1: Fake company homepage

“Meeten” is the application that is attempting to scam users into downloading an information stealer. The company regularly changes names, and has also gone by Clusee[.]com, Cuesee, Meeten[.]gg, Meeten[.]us, Meetone[.]gg and is currently going by the name Meetio. In order to gain credibility, the threat actors set up full company websites, with AI-generated blog and product content and social media accounts including Twitter and Medium.

Based on public reports from targets (withheld from this post for privacy), the scam is conducted in multiple ways. In one reported instance, a user was contacted on Telegram by someone they knew who wanted to discuss a business opportunity and to schedule a call. However, the Telegram account was created to impersonate a contact of the target. Even more interestingly, the scammer sent an investment presentation from the target’s company to him, indicating a sophisticated and targeted scam. Other reports of targeted users report being on calls related to Web3 work, downloading the software and having their cryptocurrency stolen.

After initial contact, the target would be directed to the Meeten website to download the product. In addition to hosting information stealers, the Meeten websites contain Javascript to steal cryptocurrency that is stored in web browsers, even before installing any malware. 

Script
Figure 2: Script

Technical analysis

macOS version

Name: CallCSSetup.pkg

Meeten downloads page
Figure 3: Downloads page on Meeten

Once the victim is directed to the “Meeten” website, the downloads page offers macOS or Windows/Linux. In this iteration of the website, all download links lead to the macOS version. The package file contains a 64-bit binary named “fastquery”, however other versions of the malware are distributed as a DMG with a multi-arch binary. The binary is written in Rust, with the main functionality being information stealing. 

When opened, two error messages appear. The first one states “Cannot connect to the server. Please reinstall or use a VPN.” with a continue button. Osascript, the macOS command-line tool for running AppleScript and JavaScript is used to prompt the user for their password, as commonly seen in macOS malware. [1]

Pop up
Figure 4: Popup that requests users password
Code
Figure 5

The malware iterates through various data stores, grabs sensitive information, creates a folder where the data is stored, and then exfiltrates the data as a zip. 

Folders
Figure 6: Folders and files created by Meeten

Realst Stealer looks for and exfiltrates if available:

  • Telegram credentials
  • Banking card details
  • Keychain credentials
  • Browser cookies and autofill credentials from Google Chrome, Opera, Brave, Microsoft Edge, Arc, CocCoc and Vivaldi
  • Ledger Wallets
  • Trezor Wallets

The data is sent to 139[.]162[.]179.170:8080/new_analytics with “log_id”, “anal_data” and “archive”. This contains the zip data to be exfiltrated along with analytics that include build name, build version, with system information. 

System information
Figure 7: System information that is sent as a log

Build information is also sent to 139[.]162[.]179.170:8080/opened along with metrics sent to /metrics. Following the data exfiltration, the created temporary directories are removed from the system. 

Windows version

Name: MeetenApp.exe

Meeten Setup Install
Figure 8: Meeten Setup install

While analyzing the macOS version of Meeten, Cado Security Labs identified a Windows version of the malware. The binary, “MeetenApp.exe” is a Nullsoft Scriptable Installer System (NSIS) file, with a legitimate signature from “Brys Software” that has likely been stolen.

Digital signature details
Figure 9: Digital Signature of Meeten

After extracting the files from the installer, there are two folders $PLUGINDIR and $R0. Inside $PLUGINDIR is a 7zip archive named “app-64” that contains resources, assets, binaries and an app.asar file, indicating this is an Electron application. Electron applications are built on the Electron framework that is used to develop cross-platform desktop applications with web languages such as Javascript. App.asar files are used by Electron runtime, and is a virtual file system containing application code, assets, and dependencies.

File structure
Figure 10: Electron application meeten structure
Meeten's app .asar file
Figure 11: Structure of Meeten's App.asar file
package.json
Figure 12: Package.json

After extracting the contents of app.asar, we can see the main script points to index.js containing:

"use strict"; 
require("./bytecode-loader.cjs"); 
require("./index.jsc"); 

Both of these are Bytenode Compiled Javascript files. Bytenode is a tool that compiles JavaScript code into V8 bytecode, allowing the execution of JavaScript without exposing the source code. The bytecode is a low-level representation of the JavaScript code that can be executed by the V8 JavaScript engine which powers Node.js. Since the Javascript is compiled, reverse engineering of the files is more difficult, and less likely to be detected by security tools. 

While the file is compiled, there is still some information we can see as plain text. Similarly to the macOS version, a log with system information is sent to a remote server. A secondary password protected archive , “AdditionalFilesForMeet.zip” is retrieved from deliverynetwork[.]observer into a temporary directory “temp03241242”.

URL
Figure 13

From AdditionalFilesForMeet.zip is a binary named “MicrosoftRuntimeComponentsX86.exe” This binary gathers system information including HWID, geo IP, hostname, OS, users, cores, RAM, disk size and running processes. 

Exfiltrated system information
Figure 14: System information exfiltrated by Meeten

This data is sent to 172[.]104.133.212/opened, along with the build version of Meeten. 

Data
Figure 15

An additional payload is retrieved “UpdateMC.zip” from “deliverynetwork[.]observer/qfast” into AppData/Local/Temp. The archive file extracts to UpdateMC.exe. 

UpdateMC

UpdateMC.exe is a Rust-based binary, with similar functionality to the macOS version. The stealer searches in various data stores to collect and exfiltrate sensitive data as a zip. Meeten has the ability to steal data from:

  • Telegram credentials
  • Banking card details
  • Browser cookies, history and autofill credentials from Google Chrome, Opera, Brave, Microsoft Edge, Arc, CocCoc and Vivaldi
  • Ledger Wallets
  • Trezor Wallets
  • Phantom Wallets
  • Binance Wallets

The data is stored inside a folder named after the users’ HWID inside AppData/Local/Temp directory before being exfiltrated to 172[.]104.133.212. 

Domains.txt
Figure 16

For persistence, a registry key is added to HKEY_CURRENT_USER\SOFTWARE\Microsoft\Windows\CurrentVersion\Run to ensure that the stealer is run each time the machine is started. 

Code
Figure 17: Disassembled code where 0xFFFFFFFF80000001 = HKEY_CURRENT_USER
Code
Figure 18: Meeten uses RegSetValueExW call to set registry key
Computer folder
Figure 19

Key takeaways 

This blog highlights a sophisticated campaign that uses AI to social engineer victims into downloading low detected malware that has the ability to steal financial information. Although the use of malicious Electron applications is relatively new, there has been an increase of threat actors creating malware with Electron applications. [2] As Electron apps become increasingly common, users must remain vigilant by verifying sources, implementing strict security practices, and monitoring for suspicious activity.

While much of the recent focus has been on the potential of AI to create malware, threat actors are increasingly using AI to generate content for their campaigns. Using AI enables threat actors to quickly create realistic website content that adds legitimacy to their scams, and makes it more difficult to detect suspicious websites. This shift shows how AI can be used as a powerful tool in social engineering. As a result, users need to exercise caution when being approached about business opportunities, especially through Telegram. Even if the contact appears to be an existing contact, it is important to verify the account and always be diligent when opening links. 

Indicators of compromise (IoCs)

http://172[.]104.133.212:8880/new_analytics

http://172[.]104.133.212:8880/opened

http://172[.]104.133.212:8880/metrics

http://172[.]104.133.212:8880/sede

139[.]162[.]179.170:8080

deliverynetwork[.]observer/qfast/UpdateMC.zip

deliverynetwork[.]observer/qfast/AdditionalFilesForMeet.zip

www[.]meeten.us

www[.]meetio.one

www[.]meetone.gg

www[.]clusee.com

199[.]247.4.86

File / md5

CallCSSetup.pkg  9b2d4837572fb53663fffece9415ec5a  

Meeten.exe  6a925b71afa41d72e4a7d01034e8501b  

UpdateMC.exe  209af36bb119a5e070bad479d73498f7  

MicrosoftRuntimeComponentsX64.exe d74a885545ec5c0143a172047094ed59  

CluseeApp.pkg 09b7650d8b4a6d8c8fbb855d6626e25d

MITRE ATT&CK

Technique name / ID

T1204  User Execution  

T1555.001  Credentials From Password Stores: Keychain  

T1555.003 Credentials From Password Stores: Credentials from Web Browsers  

T1539  Steal Web Session Cookie  

T1217 Browser Information Discovery  

T1082  System Information Discovery  

T1016 System Network Configuration Discovery  

T1033  System Owner/User Discovery  

T1005 Data from Local System

T1074  Local Data Staging  

T1071.001 Application Layer Protocol: Web Protocols  

T1041 Exfiltration Over C2 Channel  

T1657 Financial Theft  

T1070.004 File Deletion  

T1553.001 Subvert Trust Controls: Gatekeeper Bypass  

T1553.002  Subvert Trust Controls: Code Signing  

T1547.001 Boot or Logon Autostart Execution: Registry Run Folder  

T1497.001  Virtualization/Sandbox Evasion: System Checks  

T1058.001 Command and Scripting Interpreter: Powershell  

T1016 Network Configuration Discovery  

T1007 System Service Discovery

References

  1. https://www.darktrace.com/blog/from-the-depths-analyzing-the-cthulhu-stealer-malware-for-macos
  2. https://research.checkpoint.com/2022/new-malware-capable-of-controlling-social-media-accounts-infects-5000-machines-and-is-actively-being-distributed-via-gaming-applications-on-microsofts-official-store/  
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Tara Gould
Threat Researcher

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December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

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December 22, 2025

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

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Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

  • Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
  • Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

  • No labeling burden; no regex/rule maintenance
  • Works when tags are missing, wrong, or stale
  • Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

  • Flags risk without predefined rules or IOCs
  • Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
  • Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

  • Low-latency, on-send enforcement
  • Non-generative for predictable, explainable outcomes
  • Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.  

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

  • Can it operate label free when tags are missing or wrong? 
  • Does it truly learn per user behavior (no shortcuts)? 
  • Is there a domain specific model behind the content understanding (not a generic LLM)? 
  • Does it explain decisions to both analysts and end users? 
  • Will it integrate with your label program and SOC workflows rather than duplicate them? 

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

[related-resource]

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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