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October 3, 2024

Introducing Real-Time Multi-Cloud Detection & Response Powered by AI

This blog announces the general availability of Microsoft Azure support for Darktrace / CLOUD, enabling real-time cloud detection and response across dynamic multi-cloud environments. Read more to discover how Darktrace is pioneering AI-led real-time cloud detection and response.
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
Adam Stevens
Director of Product, Cloud Security
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03
Oct 2024

We are delighted to announce the general availability of Microsoft Azure support for Darktrace / CLOUD, enabling real-time cloud detection and response across dynamic multi-cloud environments. Built on Self-Learning AI, Darktrace / CLOUD leverages Microsoft’s new virtual network flow logs (VNet flow) to offer an agentless-first approach that dramatically simplifies detection and response within Azure, unifying cloud-native security with Darktrace’s innovative ActiveAI Security Platform.

As organizations increasingly adopt multi-cloud architectures, the need for advanced, real-time threat detection and response is critical to keep pace with evolving cloud threats. Security teams face significant challenges, including increased complexity, limited visibility, and siloed tools. The dynamic nature of multi-cloud environments introduces ever-changing blind spots, while traditional security tools struggle to provide real-time insights, often offering static snapshots of risk. Additionally, cloud security teams frequently operate in isolation from SOC teams, leading to fragmented visibility and delayed responses. This lack of coordination, especially in hybrid environments, hinders effective threat detection and response. Compounding these challenges, current security solutions are split between agent-based and agentless approaches, with agentless solutions often lacking real-time awareness and agent-based options adding complexity and scalability concerns. Darktrace / CLOUD helps to solve these challenges with real-time detection and response designed specifically for dynamic cloud environments like Azure and AWS.

Pioneering AI-led real-time cloud detection & response

Darktrace has been at the forefront of real-time detection and response for over a decade, continually pushing the boundaries of AI-driven cybersecurity. Our Self-Learning AI uniquely positions Darktrace with the ability to automatically understand and instantly adapt to changing cloud environments. This is critical in today’s landscape, where cloud infrastructures are highly dynamic and ever-changing.  

Built on years of market-leading network visibility, Darktrace / CLOUD understands ‘normal’ for your unique business across clouds and networks to instantly reveal known, unknown, and novel cloud threats with confidence. Darktrace Self-Learning AI continuously monitors activity across cloud assets, containers, and users, and correlates it with detailed identity and network context to rapidly detect malicious activity. Platform-native identity and network monitoring capabilities allow Darktrace / CLOUD to deeply understand normal patterns of life for every user and device, enabling instant, precise and proportionate response to abnormal behavior - without business disruption.

Leveraging platform-native Autonomous Response, AI-driven behavioral containment neutralizes malicious activity with surgical accuracy while preventing disruption to cloud infrastructure or services. As malicious behavior escalates, Darktrace correlates thousands of data points to identify and instantly respond to unusual activity by blocking specific connections and enforcing normal behavior.

Figure 1: AI-driven behavioral containment neutralizes malicious activity with surgical accuracy while preventing disruption to cloud infrastructure or services.

Unparalleled agentless visibility into Azure

As a long-term trusted partner of Microsoft, Darktrace leverages Azure VNet flow logs to provide agentless, high-fidelity visibility into cloud environments, ensuring comprehensive monitoring without disrupting workflows. By integrating seamlessly with Azure, Darktrace / CLOUD continues to push the envelope of innovation in cloud security. Our Self-learning AI not only improves the detection of traditional and novel threats, but also enhances real-time response capabilities and demonstrates our commitment to delivering cutting-edge, AI-powered multi-cloud security solutions.

  • Integration with Microsoft Virtual network flow logs for enhanced visibility
    Darktrace / CLOUD integrates seamlessly with Azure to provide agentless, high-fidelity visibility into cloud environments. VNet flow logs capture critical network traffic data, allowing Darktrace to monitor Azure workloads in real time without disrupting existing workflows. This integration significantly reduces deployment time by 95%1 and cloud security operational costs by up to 80%2 compared to traditional agent-based solutions. Organizations benefit from enhanced visibility across dynamic cloud infrastructures, scaling security measures effortlessly while minimizing blind spots, particularly in ephemeral resources or serverless functions.
  • High-fidelity agentless deployment
    Agentless deployment allows security teams to monitor and secure cloud environments without installing software agents on individual workloads. By using cloud-native APIs like AWS VPC flow logs or Azure VNet flow logs, security teams can quickly deploy and scale security measures across dynamic, multi-cloud environments without the complexity and performance overhead of agents. This approach delivers real-time insights, improving incident detection and response while reducing disruptions. For organizations, agentless visibility simplifies cloud security management, lowers operational costs, and minimizes blind spots, especially in ephemeral resources or serverless functions.
  • Real-time visibility into cloud assets and architectures
    With real-time Cloud Asset Enumeration and Dynamic Architecture Modeling, Darktrace / CLOUD generates up-to-date architecture diagrams, giving SecOps and DevOps teams a unified view of cloud infrastructures. This shared context enhances collaboration and accelerates threat detection and response, especially in complex environments like Kubernetes. Additionally, Cyber AI Analyst automates the investigation process, correlating data across networks, identities, and cloud assets to save security teams valuable time, ensuring continuous protection and efficient cloud migrations.
Figure 2: Real-time visibility into Azure assets and architectures built from network, configuration and identity and access roles.

Unified multi-cloud security at scale

As organizations increasingly adopt multi-cloud strategies, the complexity of managing security across different cloud providers introduces gaps in visibility. Darktrace / CLOUD simplifies this by offering agentless, real-time monitoring across multi-cloud environments. Building on our innovative approach to securing AWS environments, our customers can now take full advantage of robust real-time detection and response capabilities for Azure. Darktrace is one of the first vendors to leverage Microsoft’s virtual network flow logs to provide agentless deployment in Azure, enabling unparalleled visibility without the need for installing agents. In addition, Darktrace / CLOUD offers automated Cloud Security Posture Management (CSPM) that continuously assesses cloud configurations against industry standards.  Security teams can identify and prioritize misconfigurations, vulnerabilities, and policy violations in real-time. These capabilities give security teams a complete, live understanding of their cloud environments and help them focus their limited time and resources where they are needed most.

This approach offers seamless integration into existing workflows, reducing configuration efforts and enabling fast, flexible deployment across cloud environments. By extending its capabilities across multiple clouds, Darktrace / CLOUD ensures that no blind spots are left uncovered, providing holistic, multi-cloud security that scales effortlessly with your cloud infrastructure. diagrams, visualizes cloud assets, and prioritizes risks across cloud environments.

Figure 3: Unified view of AWS and Azure cloud posture and compliance over time.

The future of cloud security: Real-time defense in an unpredictable world

Darktrace / CLOUD’s support for Microsoft Azure, powered by Self-Learning AI and agentless deployment, sets a new standard in multi-cloud security. With real-time detection and autonomous response, organizations can confidently secure their Azure environments, leveraging innovation to stay ahead of the constantly evolving threat landscape. By combining Azure VNet flow logs with Darktrace’s AI-driven platform, we can provide customers with a unified, intelligent solution that transforms how security is managed across the cloud.

Unlock advanced cloud protection

Darktrace / CLOUD solution brief screenshot

Download the Darktrace / CLOUD solution brief to discover how autonomous, AI-driven defense can secure your environment in real-time.

  • Achieve 60% more accurate detection of unknown and novel cloud threats.
  • Respond instantly with autonomous threat response, cutting response time by 90%.
  • Streamline investigations with automated analysis, improving ROI by 85%.
  • Gain a 30% boost in cloud asset visibility with real-time architecture modeling.
  • Learn More:

    References

    1. Based on internal research and customer data

    2. Based on internal research

    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
    Adam Stevens
    Director of Product, Cloud Security

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    July 10, 2025

    Crypto Wallets Continue to be Drained in Elaborate Social Media Scam

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    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

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    July 9, 2025

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

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    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

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