Blog
/
OT
/
May 11, 2023

Securing OT Systems: The Limits of the Air Gap Approach

Air-gapped security measures are not enough for resilience against cyber attacks. Read about how to gain visibility & reduce your cyber vulnerabilities.
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
Max Lesser
Head of U.S. Policy Analysis and Engagement
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
11
May 2023

At a Glance:

  • Air gaps reduce cyber risk, but they do not prevent modern cyber attacks
  • Having visibility into an air-gapped network is better than assuming your defenses are impenetrable and having zero visibility
  • Darktrace can provide visibility and resiliency without jeopardizing the integrity of the air gap

What is an 'Air Gap'?

Information technology (IT) needs to fluidly connect with the outside world in order channel a flow of digital information across everything from endpoints and email systems to cloud and hybrid infrastructures. At the same time, this high level of connectivity makes IT systems particularly vulnerable to cyber-attacks.  

Operational technology (OT), which controls the operations of physical processes, are considerably more sensitive. OT often relies on a high degree of regularity to maintain continuity of operations. Even the slightest disturbance can lead to disastrous results. Just a few seconds of delay on a programmable logic controller (PLC), for example, can significantly disrupt a manufacturing assembly line, leading to downtime at a considerable cost. In worst-case scenarios, disruptions to OT can even threaten human safety. 

An air gap is a ‘digital moat’ where data cannot enter or leave OT environments unless it is transferred manually.

Organizations with OT have traditionally tried to reconcile this conflict between IT and OT by attempting to separate them completely. Essentially, the idea is to let IT do what IT does best — facilitate activities like communication and data transfer at rapid speeds, thus allowing people to connect with each other and access information and applications in an efficient capacity. But at the same time, erect an air gap between IT and OT so that any cyber threats that slip into IT systems do not then spread laterally into highly sensitive, mission-critical OT systems. This air gap is essentially a ‘digital moat’ where data cannot enter or leave OT environments unless it is transferred manually.

Limitations of the Air Gap

The air gap approach makes sense, but it is far from perfect. First, many organizations that believe they have completely air-gapped systems in fact have unknown points of IT/OT convergence, that is, connections between IT and OT networks of which they are unaware. 

Many organizations today are also intentionally embracing IT/OT convergence to reap the benefits of digital transformation of their OT, in what is often called Industry 4.0. Examples include the industrial cloud (or ICSaaS), the industrial internet of things (IIoT), and other types of cyber-physical systems that offer increased efficiency and expanded capabilities when compared to more traditional forms of OT. Organizations may also embrace IT/OT convergence due to a lack of human capital, as convergence can make processes simpler and more efficient.

Even when an organization does have a true air gap (which is nearly impossible to confirm without full visibility across IT and OT environments), the fact is that there are a variety of ways for attackers to ‘jump the air gap'. Full visibility across IT and OT ecosystems in a single pane of glass is thus essential for organizations seeking to secure their OT. This is not only to illuminate any points of IT/OT convergence and validate the fact that an air gap exists in the first place, but also to see when an attack slips through the air gap.

Figure 1: Darktrace/OT's unified view of IT and OT environments.

Air Gap Attack Vectors

Even a perfect air gap will be vulnerable to a variety of different attack vectors, including (but not limited to) the following: 

  • Physical compromise: An adversary bypasses physical security and gains access directly to the air-gapped network devices. Physical access is by far the most effective and obvious technique.
  • Insider threats: Someone who is part of an organization and has access to air-gapped secure systems intentionally or unintentionally compromises a system.
  • Supply chain compromise: A vendor with legitimate access to air-gapped systems unwittingly is compromised and brings infected devices into a network. 
  • Misconfiguration: Misconfiguration of access controls or permissions allows an attacker to access the air-gapped system through a separate device on the network.
  • Social engineering (media drop): If an attacker was able to successfully conduct a malicious USB/media drop and an employee was to use that media within the air-gapped system, the network could be compromised. 
  • Other advanced tactics: Thermal manipulation, covert surface vibrations, LEDs, ultrasonic transmissions, radio signals, and magnetic fields are among a range of advanced tactics documented and demonstrated by researchers at Ben Gurion University. 

Vulnerabilities of Air-Gapped Systems

Aside from susceptibility to advanced techniques, tactics, and procedures (TTPs) such as thermal manipulation and magnetic fields, more common vulnerabilities associated with air-gapped environments include factors such as unpatched systems going unnoticed, lack of visibility into network traffic, potentially malicious devices coming on the network undetected, and removable media being physically connected within the network. 

Once the attack is inside OT systems, the consequences can be disastrous regardless of whether there is an air gap or not. However, it is worth considering how the existence of the air gap can affect the time-to-triage and remediation in the case of an incident. For example, the existence of an air gap may seriously limit an incident response vendor’s ability to access the network for digital forensics and response. 

Kremlin Hackers Jumping the Air Gap 

In 2018, the U.S. Department of Homeland Security (DHS) issued an alert documenting the TTPs used by Russian threat actors known as Dragonfly and Energetic Bear. Further reporting alleged that these groups ‘jumped the air gap,’ and, concerningly, gained the ability to disable the grid at the time of their choosing. 

These attackers successfully gained access to sensitive air-gapped systems across the energy sector and other critical infrastructure sectors by targeting vendors and suppliers through spear-phishing emails and watering hole attacks. These vendors had legitimate access to air-gapped systems, and essentially brought the infection into these systems unintentionally when providing support services such as patch deployment.

This incident reveals that even if a sensitive OT system has complete digital isolation, this robust air gap still cannot fully eliminate one of the greatest vulnerabilities of any system—human error. Human error would still hold if an organization went to the extreme of building a faraday cage to eliminate electromagnetic radiation. Air-gapped systems are still vulnerable to social engineering, which exploits human vulnerabilities, as seen in the tactics that Dragonfly and Energetic Bear used to trick suppliers, who then walked the infection right through the front door. 

Ideally, a technology would be able to identify an attack regardless of whether it is caused by a compromised supplier, radio signal, or electromagnetic emission. By spotting subtle deviations from a device, human, or network’s normal ‘pattern of life’, Self-Learning AI detects even the most nuanced forms of threatening behavior as they emerge — regardless of the source or cause of the threat.

Darktrace/OT for Air-Gapped Environments

Darktrace/OT for air-gapped environments is a physical appliance that deploys directly to the air-gapped system. Using raw digital data from an OT network to understand the normal pattern of life, Darktrace/OT does not need any data or threat feeds from external sources because the AI builds an innate understanding of self without third-party support. 

Because all data-processing and analytics are performed locally on the Darktrace appliance, there is no requirement for Darktrace to have a connection out to the internet. As a result, Darktrace/OT provides visibility and threat detection to air-gapped or highly segmented networks without jeopardizing their integrity. If a human or machine displays even the most nuanced forms of threatening behavior, the solution can illuminate this in real time. 

Security professionals can then securely access Darktrace alerts from anywhere within the network, using a web browser and encrypted HTTPS, and in line with your organization’s network policies.

Figure 2: Darktrace/OT detecting anomalous connections to a SCADA ICS workstation.

With this deployment, Darktrace offers all the critical insights demonstrated in other Darktrace/OT deployments, including (but not limited to) the following:

Organizations seeking to validate whether they have an air gap in the first place and maintain the air gap as their IT and OT environments evolve will greatly benefit from the comprehensive visibility and continuous situational awareness offered by Darktrace’s Self-Learning AI. Also, organizations looking to poke holes in their air gap to embrace the benefits of IT/OT convergence will find that Self-Learning AI’s vigilance spots cyber-attacks that slip through. 

Whatever your organizations goals—be it embracing IIoT or creating a full-blown DMZ—by learning ‘you’, Darktrace’s Self-Learning AI can help you achieve them safely and securely. 

Learn more about Darktrace/OT

Credit to: Daniel Simonds and Oakley Cox for their contribution to this blog.

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
Max Lesser
Head of U.S. Policy Analysis and Engagement

More in this series

No items found.

Blog

/

Cloud

/

July 10, 2025

Crypto Wallets Continue to be Drained in Elaborate Social Media Scam

password on computer screenDefault blog imageDefault blog image

Overview

Continued research by Darktrace has revealed that cryptocurrency users are being targeted by threat actors in an elaborate social engineering scheme that continues to evolve. In December 2024, Cado Security Labs detailed a campaign targeting Web 3 employees in the Meeten campaign. The campaign included threat actors setting up meeting software companies to trick users into joining meetings and installing the information stealer Realst disguised as video meeting software.

The latest research from Darktrace shows that this campaign is still ongoing and continues to trick targets to download software to drain crypto wallets. The campaign features:

  • Threat actors creating fake startup companies with AI, gaming, video meeting software, web3 and social media themes.
  • Use of compromised X (formerly Twitter) accounts for the companies and employees - typically with verification to contact victims and create a facade of a legitimate company.
  • Notion, Medium, Github used to provide whitepapers, project roadmaps and employee details.
  • Windows and macOS versions.
  • Stolen software signing certificates in Windows versions for credibility and defense evasion.
  • Anti-analysis techniques including obfuscation, and anti-sandboxing.

To trick as many victims as possible, threat actors try to make the companies look as legitimate as possible. To achieve this, they make use of sites that are used frequently with software companies such as Twitter, Medium, Github and Notion. Each company has a professional looking website that includes employees, product blogs, whitepapers and roadmaps. X is heavily used to contact victims, and to increase the appearance of legitimacy. Some of the observed X accounts appear to be compromised accounts that typically are verified and have a higher number of followers and following, adding to the appearance of a real company.

Example of a compromised X account to create a “BuzzuAI” employee.
Figure 1: Example of a compromised X account to create a “BuzzuAI” employee.

The threat actors are active on these accounts while the campaign is active, posting about developments in the software, and product marketing. One of the fake companies part of this campaign, “Eternal Decay”, a blockchain-powered game, has created fake pictures pretending to be presenting at conferences to post on social media, while the actual game doesn’t exist.

From the Eternal Decay X account, threat actors have altered a photo from an Italian exhibition (original on the right) to make it look like Eternal Decay was presented.
Figure 2: From the Eternal Decay X account, threat actors have altered a photo from an Italian exhibition (original on the right) to make it look like Eternal Decay was presented.

In addition to X, Medium is used to post blogs about the software. Notion has been used in various campaigns with product roadmap details, as well as employee lists.

Notion project team page for Swox.
Figure 3: Notion project team page for Swox.

Github has been used to detail technical aspects of the software, along with Git repositories containing stolen open-source projects with the name changed in order to make the code look unique. In the Eternal Decay example, Gitbook is used to detail company and software information. The threat actors even include company registration information from Companies House, however they have linked to a company with a similar name and are not a real registered company.

 From the Eternal Decay Gitbook linking to a company with a similar name on Companies House.
Figure 4: From the Eternal Decay Gitbook linking to a company with a similar name on Companies House.
Gitbook for “Eternal Decay” listing investors.
Figure 5: Gitbook for “Eternal Decay” listing investors.
Gameplay images are stolen from a different game “Zombie Within” and posted pretending to be Eternal Decay gameplay.
Figure 6: Gameplay images are stolen from a different game “Zombie Within” and posted pretending to be Eternal Decay gameplay.

In some of the fake companies, fake merchandise stores have even been set up. With all these elements combined, the threat actors manage to create the appearance of a legitimate start-up company, increasing their chances of infection.

Each campaign typically starts with a victim being contacted through X messages, Telegram or Discord. A fake employee of the company will contact a victim asking to test out their software in exchange for a cryptocurrency payment. The victim will be directed to the company website download page, where they need to enter a registration code, provided by the employee to download a binary. Depending on their operating system, the victim will be instructed to download a macOS DMG (if available) or a Windows Electron application.

Example of threat actor messaging a victim on X with a registration code.
Figure 7: Example of threat actor messaging a victim on X with a registration code.

Windows Version

Similar to the aforementioned Meeten campaign, the Windows version being distributed by the fake software companies is an Electron application. Electron is an open-source framework used to run Javascript apps as a desktop application. Once the user follows directions sent to them via message, opening the application will bring up a Cloudflare verification screen.

Cloudflare verification screen.
Figure 8: Cloudflare verification screen.

The malware begins by profiling the system, gathering information like the username, CPU and core count, RAM, operating system, MAC address, graphics card, and UUID.

Code from the Electron app showing console output of system profiling.
Figure 9: Code from the Electron app showing console output of system profiling.

A verification process occurs with a captcha token extracted from the app-launcher URL and sent along with the system info and UUID. If the verification is successful, an executable or MSI file is downloaded and executed quietly. Python is also retrieved and stored in /AppData/Temp, with Python commands being sent from the command-and-control (C2) infrastructure.

Code from the Electron app looping through Python objects.
Figure 10: Code from the Electron app looping through Python objects.

As there was no valid token, this process did not succeed. However, based on previous campaigns and reports from victims on social media, an information stealer targeting crypto wallets is executed at this stage. A common tactic in the observed campaigns is the use of stolen code signing certificates to evade detection and increase the appearance of legitimate software. The certificates of two legitimate companies Jiangyin Fengyuan Electronics Co., Ltd. and Paperbucketmdb ApS (revoked as of June 2025) were used during this campaign.

MacOS Version

For companies that have a macOS version of the malware, the user is directed to download a DMG. The DMG contains a bash script and a multiarch macOS binary. The bash script is obfuscated with junk, base64 and is XOR’d.

Obfuscated Bash script.
Figure 11: Obfuscated Bash script.

After decoding, the contents of the script are revealed showing that AppleScript is being used. The script looks for disk drives, specifically for the mounted DMG “SwoxApp” and moves the hidden .SwoxApp binary to /tmp/ and makes it executable. This type of AppleScript is commonly used in macOS malware, such as Atomic Stealer.

AppleScript used to mount the malware and make it executable.
Figure 12: AppleScript used to mount the malware and make it executable.

The SwoxApp binary is the prominent macOS information stealer Atomic Stealer. Once executed the malware performs anti-analysis checks for QEMU, VMWare and Docker-OSX, the script exits if these return true.  The main functionality of Atomic Stealer is to steal data from stores including browser data, crypto wallets, cookies and documents. This data is compressed into /tmp/out.zip and sent via POST request to 45[.]94[.]47[.]167/contact. An additional bash script is retrieved from 77[.]73[.]129[.]18:80/install.sh.

Additional Bash script ”install.sh”.
Figure 13: Additional Bash script ”install.sh”.

Install.sh, as shown in Figure 13, retrieves another script install_dynamic.sh from the server https://mrajhhosdoahjsd[.]com. Install_dynamic.sh downloads and extracts InstallerHelper.app, then sets up persistence via Launch Agent to run at login.

Persistence added via Plist configuration.
Figure 14: Persistence added via Plist configuration.

This plist configuration installs a macOS LaunchAgent that silently runs the app at user login. RunAtLoad and KeepAlive keys are used to ensure the app starts automatically and remains persistent.

The retrieved binary InstallerHelper is an Objective-C/Swift binary that logs active application usage, window information, and user interaction timestamps. This data is written to local log files and periodically transmits the contents to https://mrajhhoshoahjsd[.]com/collect-metrics using scheduled network requests.

List of known companies

Darktrace has identified a number of the fake companies used in this scam. These can be found in the list below:

Pollens AI
X: @pollensapp, @Pollens_app
Website: pollens.app, pollens.io, pollens.tech
Windows: 02a5b35be82c59c55322d2800b0b8ccc
Notes: Posing as an AI software company with a focus on “collaborative creation”.

Buzzu
X: @BuzzuApp, @AI_Buzzu, @AppBuzzu, @BuzzuApp
Website: Buzzu.app, Buzzu.us, buzzu.me, Buzzu.space
Windows: 7d70a7e5661f9593568c64938e06a11a
Mac: be0e3e1e9a3fda76a77e8c5743dd2ced
Notes: Same as Pollens including logo but with a different name.

Cloudsign
X: @cloudsignapp
Windows: 3a3b13de4406d1ac13861018d74bf4b2
Notes: Claims to be a document signing platform.

Swox
X: @SwoxApp, @Swox_AI, @swox_app, @App_Swox, @AppSwox, @SwoxProject, @ProjectSwox
Website: swox.io, swox.app, swox.cc, swoxAI.com, swox.us
Windows: d50393ba7d63e92d23ec7d15716c7be6
Mac: 81996a20cfa56077a3bb69487cc58405ced79629d0c09c94fb21ba7e5f1a24c9
Notes: Claims to be a “Next gen social network in the WEB3”. Same GitHub code as Pollens.

KlastAI
X: Links to Pollens X account
Website: Links to pollens.tech
Notes: Same as Pollens, still shows their branding on its GitHub readme page.

Wasper
X: @wasperAI, @WasperSpace
Website: wasper.pro, wasper.app, wasper.org, wasper.space
Notes: Same logo and GitHub code as Pollens.

Lunelior
Website: lunelior.net, Lunelior.app, lunelior.io, lunelior.us
Windows: 74654e6e5f57a028ee70f015ef3a44a4
Mac: d723162f9197f7a548ca94802df74101

BeeSync
X: @BeeSyncAI, @AIBeeSync
Website: beesync.ai, beesync.cc
Notes: Previous alias of Buzzu, Git repo renamed January 2025.

Slax
X: @SlaxApp, @Slax_app, @slaxproject
Website: slax.tech, slax.cc, slax.social, slaxai.app

Solune
X: @soluneapp
Website: solune.io, solune.me
Windows: 22b2ea96be9d65006148ecbb6979eccc

Eternal Decay
X: @metaversedecay
Website: eternal-decay.xyz
Windows: 558889183097d9a991cb2c71b7da3c51
Mac: a4786af0c4ffc84ff193ff2ecbb564b8

Dexis
X: @DexisApp
Website: dexis.app
Notes: Same branding as Swox.

NexVoo
X: @Nexvoospace
Website: nexvoo.app, Nexvoo.net, Nexvoo.us

NexLoop
X: @nexloopspace
Website: nexloop.me

NexoraCore
Notes: Rename of the Nexloop Git repo.

YondaAI
X: @yondaspace
Website: yonda.us

Traffer Groups

A “traffer” malware group is an organized cybercriminal operation that specializes in directing internet users to malicious content typically information-stealing malware through compromised or deceptive websites, ads, and links. They tend to operate in teams with hierarchical structures with administrators recruiting “traffers” (or affiliates) to generate traffic and malware installs via search engine optimization (SEO), YouTube ads, fake software downloads, or owned sites, then monetize the stolen credentials and data via dedicated marketplaces.

A prominent traffer group “CrazyEvil” was identified by Recorded Future in early 2025. The group, who have been active since at least 2021, specialize in social engineering attacks targeted towards cryptocurrency users, influencers, DeFi professionals, and gaming communities. As reported by Recorded Future, CrazyEvil are estimated to have made millions of dollars in revenue from their malicious activity. CrazyEvil and their sub teams create fake software companies, similar to the ones described in this blog, making use of Twitter and Medium to target victims. As seen in this campaign, CrazyEvil instructs users to download their software which is an info stealer targeting both macOS and Windows users.

While it is unclear if the campaigns described in this blog can be attributed to CrazyEvil or any sub teams, the techniques described are similar in nature. This campaign highlights the efforts that threat actors will go to make these fake companies look legitimate in order to steal cryptocurrency from victims, in addition to use of newer evasive versions of malware.

Indicators of Compromise (IoCs)

Manboon[.]com

https://gaetanorealty[.]com

Troveur[.]com

Bigpinellas[.]com

Dsandbox[.]com

Conceptwo[.]com

Aceartist[.]com

turismoelcasco[.]com

Ekodirect[.]com

https://mrajhhosdoahjsd[.]com

https://isnimitz.com/zxc/app[.]zip

http://45[.]94[.]47[.]112/contact

45[.]94[.]47[.]167/contact

77[.]73[.]129[.]18:80

Domain Keys associated with the C2s

YARA Rules

rule Suspicious_Electron_App_Installer

{

  meta:

      description = "Detects Electron apps collecting HWID, MAC, GPU info and executing remote EXEs/MSIs"

      date = "2025-06-18"

  strings:

      $electron_require = /require\(['"]electron['"]\)/

      $axios_require = /require\(['"]axios['"]\)/

      $exec_use = /exec\(.*?\)/

      $url_token = /app-launcher:\/\/.*token=/

      $getHWID = /(Get-CimInstance Win32_ComputerSystemProduct).UUID/

      $getMAC = /details\.mac && details\.mac !== '00:00:00:00:00:00'/

      $getGPU = /wmic path win32_VideoController get name/

      $getInstallDate = /InstallDate/

      $os_info = /os\.cpus\(\)\[0\]\.model/

      $downloadExe = /\.exe['"]/

      $runExe = /msiexec \/i.*\/quiet \/norestart/

      $zipExtraction = /AdmZip\(.*\.extractAllTo/

  condition:

      (all of ($electron_require, $axios_require, $exec_use) and

       3 of ($getHWID, $getMAC, $getGPU, $getInstallDate, $os_info) and

       2 of ($downloadExe, $runExe, $zipExtraction, $url_token))

}

Continue reading
About the author
Tara Gould
Threat Researcher

Blog

/

Identity

/

July 9, 2025

Defending the Cloud: Stopping Cyber Threats in Azure and AWS with Darktrace

fingerprintDefault blog imageDefault blog image

Real-world intrusions across Azure and AWS

As organizations pursue greater scalability and flexibility, cloud platforms like Microsoft Azure and Amazon Web Services (AWS) have become essential for enabling remote operations and digitalizing corporate environments. However, this shift introduces a new set of security risks, including expanding attack surfaces, misconfigurations, and compromised credentials frequently exploited by threat actors.

This blog dives into three instances of compromise within a Darktrace customer’s Azure and AWS environment which Darktrace.

  1. The first incident took place in early 2024 and involved an attacker compromising a legitimate user account to gain unauthorized access to a customer’s Azure environment.
  2. The other two incidents, taking place in February and March 2025, targeted AWS environments. In these cases, threat actors exfiltrated corporate data, and in one instance, was able to detonate ransomware in a customer’s environment.

Case 1 - Microsoft Azure

Simplified timeline of the attack on a customer’s Azure environment.
Figure 1: Simplified timeline of the attack on a customer’s Azure environment.

In early 2024, Darktrace identified a cloud compromise on the Azure cloud environment of a customer in the Europe, the Middle East and Africa (EMEA) region.

Initial access

In this case, a threat actor gained access to the customer’s cloud environment after stealing access tokens and creating a rogue virtual machine (VM). The malicious actor was found to have stolen access tokens belonging to a third-party external consultant’s account after downloading cracked software.

With these stolen tokens, the attacker was able to authenticate to the customer’s Azure environment and successfully modified a security rule to allow inbound SSH traffic from a specific IP range (i.e., securityRules/AllowCidrBlockSSHInbound). This was likely performed to ensure persistent access to internal cloud resources.

Detection and investigation of the threat

Darktrace / IDENTITY recognized that this activity was highly unusual, triggering the “Repeated Unusual SaaS Resource Creation” alert.

Cyber AI Analyst launched an autonomous investigation into additional suspicious cloud activities occurring around the same time from the same unusual location, correlating the individual events into a broader account hijack incident.

Cyber AI Analyst’s investigation into unusual cloud activity performed by the compromised account.
Figure 2: Cyber AI Analyst’s investigation into unusual cloud activity performed by the compromised account.
Figure 2: Surrounding resource creation events highlighted by Cyber AI Analyst.
Figure 3: Surrounding resource creation events highlighted by Cyber AI Analyst.
Figure 4: Surrounding resource creation events highlighted by Cyber AI Analyst.

“Create resource service limit” events typically indicate the creation or modification of service limits (i.e., quotas) for a specific Azure resource type within a region. Meanwhile, “Registers the Capacity Resource Provider” events refer to the registration of the Microsoft Capacity resource provider within an Azure subscription, responsible for managing capacity-related resources, particularly those related to reservations and service limits. These events suggest that the threat actor was looking to create new cloud resources within the environment.

Around ten minutes later, Darktrace detected the threat actor creating or modifying an Azure disk associated with a virtual machine (VM), suggesting an attempt to create a rogue VM within the environment.

Threat actors can leverage such rogue VMs to hijack computing resources (e.g., by running cryptomining malware), maintain persistent access, move laterally within the cloud environment, communicate with command-and-control (C2) infrastructure, and stealthily deliver and deploy malware.

Persistence

Several weeks later, the compromised account was observed sending an invitation to collaborate to an external free mail (Google Mail) address.

Darktrace deemed this activity as highly anomalous, triggering a compliance alert for the customer to review and investigate further.

The next day, the threat actor further registered new multi-factor authentication (MFA) information. These actions were likely intended to maintain access to the compromised user account. The customer later confirmed this activity by reviewing the corresponding event logs within Darktrace.

Case 2 – Amazon Web Services

Simplified timeline of the attack on a customer’s AWS environment
Figure 5: Simplified timeline of the attack on a customer’s AWS environment

In February 2025, another cloud-based compromised was observed on a UK-based customer subscribed to Darktrace’s Managed Detection and Response (MDR) service.

How the attacker gained access

The threat actor was observed leveraging likely previously compromised credential to access several AWS instances within customer’s Private Cloud environment and collecting and exfiltrating data, likely with the intention of deploying ransomware and holding the data for ransom.

Darktrace alerting to malicious activity

This observed activity triggered a number of alerts in Darktrace, including several high-priority Enhanced Monitoring alerts, which were promptly investigated by Darktrace’s Security Operations Centre (SOC) and raised to the customer’s security team.

The earliest signs of attack observed by Darktrace involved the use of two likely compromised credentials to connect to the customer’s Virtual Private Network (VPN) environment.

Internal reconnaissance

Once inside, the threat actor performed internal reconnaissance activities and staged the Rclone tool “ProgramData\rclone-v1.69.0-windows-amd64.zip”, a command-line program to sync files and directories to and from different cloud storage providers, to an AWS instance whose hostname is associated with a public key infrastructure (PKI) service.

The threat actor was further observed accessing and downloading multiple files hosted on an AWS file server instance, notably finance and investment-related files. This likely represented data gathering prior to exfiltration.

Shortly after, the PKI-related EC2 instance started making SSH connections with the Rclone SSH client “SSH-2.0-rclone/v1.69.0” to a RockHoster Virtual Private Server (VPS) endpoint (193.242.184[.]178), suggesting the threat actor was exfiltrating the gathered data using the Rclone utility they had previously installed. The PKI instance continued to make repeated SSH connections attempts to transfer data to this external destination.

Darktrace’s Autonomous Response

In response to this activity, Darktrace’s Autonomous Response capability intervened, blocking unusual external connectivity to the C2 server via SSH, effectively stopping the exfiltration of data.

This activity was further investigated by Darktrace’s SOC analysts as part of the MDR service. The team elected to extend the autonomously applied actions to ensure the compromise remained contained until the customer could fully remediate the incident.

Continued reconissance

Around the same time, the threat actor continued to conduct network scans using the Nmap tool, operating from both a separate AWS domain controller instance and a newly joined device on the network. These actions were accompanied by further internal data gathering activities, with around 5 GB of data downloaded from an AWS file server.

The two devices involved in reconnaissance activities were investigated and actioned by Darktrace SOC analysts after additional Enhanced Monitoring alerts had triggered.

Lateral movement attempts via RDP connections

Unusual internal RDP connections to a likely AWS printer instance indicated that the threat actor was looking to strengthen their foothold within the environment and/or attempting to pivot to other devices, likely in response to being hindered by Autonomous Response actions.

This triggered multiple scanning, internal data transfer and unusual RDP alerts in Darktrace, as well as additional Autonomous Response actions to block the suspicious activity.

Suspicious outbound SSH communication to known threat infrastructure

Darktrace subsequently observed the AWS printer instance initiating SSH communication with a rare external endpoint associated with the web hosting and VPS provider Host Department (67.217.57[.]252), suggesting that the threat actor was attempting to exfiltrate data to an alternative endpoint after connections to the original destination had been blocked.

Further investigation using open-source intelligence (OSINT) revealed that this IP address had previously been observed in connection with SSH-based data exfiltration activity during an Akira ransomware intrusion [1].

Once again, connections to this IP were blocked by Darktrace’s Autonomous Response and subsequently these blocks were extended by Darktrace’s SOC team.

The above behavior generated multiple Enhanced Monitoring alerts that were investigated by Darktrace SOC analysts as part of the Managed Threat Detection service.

Enhanced Monitoring alerts investigated by SOC analysts as part of the Managed Detection and Response service.
Figure 5: Enhanced Monitoring alerts investigated by SOC analysts as part of the Managed Detection and Response service.

Final containment and collaborative response

Upon investigating the unusual scanning activity, outbound SSH connections, and internal data transfers, Darktrace analysts extended the Autonomous Response actions previously triggered on the compromised devices.

As the threat actor was leveraging these systems for data exfiltration, all outgoing traffic from the affected devices was blocked for an additional 24 hours to provide the customer’s security team with time to investigate and remediate the compromise.

Additional investigative support was provided by Darktrace analysts through the Security Operations Service, after the customer's opened of a ticket related to the unfolding incident.

Simplified timeline of the attack
Figure 8: Simplified timeline of the attack

Around the same time of the compromise in Case 2, Darktrace observed a similar incident on the cloud environment of a different customer.

Initial access

On this occasion, the threat actor appeared to have gained entry into the AWS-based Virtual Private Cloud (VPC) network via a SonicWall SMA 500v EC2 instance allowing inbound traffic on any port.

The instance received HTTPS connections from three rare Vultr VPS endpoints (i.e., 45.32.205[.]52, 207.246.74[.]166, 45.32.90[.]176).

Lateral movement and exfiltration

Around the same time, the EC2 instance started scanning the environment and attempted to pivot to other internal systems via RDP, notably a DC EC2 instance, which also started scanning the network, and another EC2 instance.  

The latter then proceeded to transfer more than 230 GB of data to the rare external GTHost VPS endpoint 23.150.248[.]189, while downloading hundreds of GBs of data over SMB from another EC2 instance.

Cyber AI Analyst incident generated following the unusual scanning and RDP connections from the initial compromised device.
Figure 7: Cyber AI Analyst incident generated following the unusual scanning and RDP connections from the initial compromised device.

The same behavior was replicated across multiple EC2 instances, whereby compromised instances uploaded data over internal RDP connections to other instances, which then started transferring data to the same GTHost VPS endpoint over port 5000, which is typically used for Universal Plug and Play (UPnP).

What Darktrace detected

Darktrace observed the threat actor uploading a total of 718 GB to the external endpoint, after which they detonated ransomware within the compromised VPC networks.

This activity generated nine Enhanced Monitoring alerts in Darktrace, focusing on the scanning and external data activity, with the earliest of those alerts triggering around one hour after the initial intrusion.

Darktrace’s Autonomous Response capability was not configured to act on these devices. Therefore, the malicious activity was not autonomously blocked and escalated to the point of ransomware detonation.

Conclusion

This blog examined three real-world compromises in customer cloud environments each illustrating different stages in the attack lifecycle.

The first case showcased a notable progression from a SaaS compromise to a full cloud intrusion, emphasizing the critical role of anomaly detection when legitimate credentials are abused.

The latter two incidents demonstrated that while early detection is vital, the ability to autonomously block malicious activity at machine speed is often the most effective way to contain threats before they escalate.

Together, these incidents underscore the need for continuous visibility, behavioral analysis, and machine-speed intervention across hybrid environments. Darktrace's AI-driven detection and Autonomous Response capabilities, combined with expert oversight from its Security Operations Center, give defenders the speed and clarity they need to contain threats and reduce operational disruption, before the situation spirals.

Credit to Alexandra Sentenac (Senior Cyber Analyst) and Dylan Evans (Security Research Lead)

References

[1] https://www.virustotal.com/gui/ip-address/67.217.57.252/community

Case 1

Darktrace / IDENTITY model alerts

IaaS / Compliance / Uncommon Azure External User Invite

SaaS / Resource / Repeated Unusual SaaS Resource Creation

IaaS / Compute / Azure Compute Resource Update

Cyber AI Analyst incidents

Possible Unsecured AzureActiveDirectory Resource

Possible Hijack of Office365 Account

Case 2

Darktrace / NETWORK model alerts

Compromise / SSH Beacon

Device / Multiple Lateral Movement Model Alerts

Device / Suspicious SMB Scanning Activity

Device / SMB Lateral Movement

Compliance / SSH to Rare External Destination

Device / Anomalous SMB Followed By Multiple Model Alerts

Device / Anonymous NTLM Logins

Anomalous Connection / SMB Enumeration

Device / New or Uncommon SMB Named Pipe Device / Network Scan

Device / Suspicious Network Scan Activity

Device / New Device with Attack Tools

Device / RDP Scan Device / Attack and Recon Tools

Compliance / High Priority Compliance Model Alert

Compliance / Outgoing NTLM Request from DC

Compromise / Large Number of Suspicious Successful Connections

Device / Large Number of Model Alerts

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Unusual Activity / Internal Data Transfer

Anomalous Connection / Unusual Internal Connections

Device / Anomalous RDP Followed By Multiple Model Alerts

Unusual Activity / Unusual External Activity

Unusual Activity / Enhanced Unusual External Data Transfer

Unusual Activity / Unusual External Data Transfer

Unusual Activity / Unusual External Data to New Endpoint

Anomalous Connection / Multiple Connections to New External TCP Port

Darktrace / Autonomous Response model alerts

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / Manual / Quarantine Device

Antigena / MDR / MDR-Quarantined Device

Antigena / MDR / Model Alert on MDR-Actioned Device

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

Antigena / Network / Insider Threat / Antigena Internal Data Transfer Block

Cyber AI Analyst incidents

Possible Application Layer Reconnaissance Activity

Scanning of Multiple Devices

Unusual Repeated Connections

Unusual External Data Transfer

Case 3

Darktrace / NETWORK model alerts

Unusual Activity / Unusual Large Internal Transfer

Compliance / Incoming Remote Desktop

Unusual Activity / High Volume Server Data Transfer

Unusual Activity / Internal Data Transfer

Anomalous Connection / Unusual Internal Remote Desktop

Anomalous Connection / Unusual Incoming Data Volume

Anomalous Server Activity / Domain Controller Initiated to Client

Device / Large Number of Model Alerts

Anomalous Connection / Possible Flow Device Brute Force

Device / RDP Scan

Device / Suspicious Network Scan Activity

Device / Network Scan

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous Connection / Download and Upload

Unusual Activity / Unusual External Data Transfer

Unusual Activity / High Volume Client Data Transfer

Unusual Activity / Unusual External Activity

Anomalous Connection / Uncommon 1 GiB Outbound

Device / Increased External Connectivity

Compromise / Large Number of Suspicious Successful Connections

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Low and Slow Exfiltration to IP

Unusual Activity / Enhanced Unusual External Data Transfer

Anomalous Connection / Multiple Connections to New External TCP Port

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / Multiple Connections to New External UDP Port

Anomalous Connection / Possible Data Staging and External Upload

Unusual Activity / Unusual External Data to New Endpoint

Device / Large Number of Model Alerts from Critical Network Device

Compliance / External Windows Communications

Anomalous Connection / Unusual Internal Connections

Cyber AI Analyst incidents

Scanning of Multiple Devices

Extensive Unusual RDP Connections

MITRE ATT&CK mapping

(Technique name – Tactic ID)

Case 1

Defense Evasion - Modify Cloud Compute Infrastructure: Create Cloud Instance

Persistence – Account Manipulation

Case 2

Initial Access - External Remote Services

Execution - Inter-Process Communication

Persistence - External Remote Services

Discovery - System Network Connections Discovery

Discovery - Network Service Discovery

Discovery - Network Share Discovery

Lateral Movement - Remote Desktop Protocol

Lateral Movement - Remote Services: SMB/Windows Admin Shares

Collection - Data from Network Shared Drive

Command and Control - Protocol Tunneling

Exfiltration - Exfiltration Over Asymmetric Encrypted Non-C2 Protocol

Case 3

Initial Access - Exploit Public-Facing Application

Discovery - Remote System Discovery

Discovery - Network Service Discovery

Lateral Movement - Remote Services

Lateral Movement - Remote Desktop Protocol  

Collection - Data from Network Shared Drive

Collection - Data Staged: Remote Data Staging

Exfiltration - Exfiltration Over C2 Channel

Command and Control - Non-Standard Port

Command and Control – Web Service

Impact - Data Encrypted for Impact

List of IoCs

IoC         Type      Description + Probability

193.242.184[.]178 - IP Address - Possible Exfiltration Server  

45.32.205[.]52  - IP Address  - Possible C2 Infrastructure

45.32.90[.]176 - IP Address - Possible C2 Infrastructure

207.246.74[.]166 - IP Address - Likely C2 Infrastructure

67.217.57[.]252 - IP Address - Likely C2 Infrastructure

23.150.248[.]189 - IP Address - Possible Exfiltration Server

Continue reading
About the author
Alexandra Sentenac
Cyber Analyst
Your data. Our AI.
Elevate your network security with Darktrace AI