September 20, 2022

Modern Extortion: Detecting Data Theft From the Cloud

Darktrace highlights a handful of data theft incidents on shared cloud platforms, showing that cloud computing can be a vulnerable place for modern extortion.

Written by

Adrianne Marques

Senior Research Analyst

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

Adrianne Marques

Senior Research Analyst

Sep 2022

Ransomware Industry

The ransomware industry has benefitted from a number of factors in recent years: inadequate cyber defenses, poorly regulated cryptocurrency markets, and geopolitical tensions have allowed gangs to extort increasingly large ransoms while remaining sheltered from western law enforcement [1]. However, one of the biggest success stories of the ransomware industry has been the adaptability and evolution of attacker TTPs (tactics, techniques and procedures). The WannaCry and NotPetya attacks of 2017 popularized a form of ransomware which used encryption algorithms to hold data to ransom in exchange for a decryption key. Last year in 2021, almost all ransomware strains evolved to use double extortion tactics: holding stolen data to ransom as well as encrypted data [2]. Now, some ransomware gangs have dropped encryption entirely, and are using data theft as their sole means of extortion.

Using data theft for extortion is not new. In 2020 the Finnish psychotherapy center Vastaamo had over 40,000 patient records stolen. Impacted patients were told that their psychiatric transcripts would be published online if they failed to pay a Bitcoin ransom. [3]. A later report by BlackFog in May 2021 predicted data theft extortion would become one of the key emerging cybersecurity trends that year [4]. Adoption of offline back-ups and endpoint detection had made encryption harder, while a large-scale move to Cloud and SaaS platforms offered new vectors for data theft. By moving from data encryption to data exfiltration, ransomware attackers pivoted from targeting data availability within the CIA triad (Confidentiality, Integrity, Availability) to threatening data confidentiality.

In November 2021, Darktrace detected a data theft incident following the compromise of two SaaS accounts within an American tech customer’s Office365 environment. The client was a longstanding user of Darktrace DETECT/Network, and was in the process of expanding their coverage by trialing Darktrace DETECT+RESPOND/ Apps + Cloud.

Attack Overview

On November 23^rd 2021, an Ask the Expert (ATE) ticket was raised prompting investigation into a breached SaaS model, ‘SaaS / Access / Unusual External Source for SaaS Credential Use’, and the activities of a user (censored as UserA) over the prior week.

1. Office365: UserA

The account UserA had been logging in from an unusual location in Nigeria on November 21^st. At the time of the incident there were no flags of malicious activity from this IP in widely used OSINT sources. It is also highly probable the attacker was not located in Nigeria but using Nigerian infrastructure in order to hide their true location. Regardless, the location of the login from this IP and ASN was considered highly unusual for users within the customer’s digital estate. The specific user in question most commonly accessed their account from IP ranges located in the US.

Figure 1: In the Geolocation tab of the External Sites Summary on the SaaS Console, UserA was seen logging in from Nigeria when previous logins were exclusively from USA

Further investigation revealed an additional anomaly in the Outlook Web activity of UserA. The account was using the Firefox browser to access their account for the first time in at least 4 weeks (the maximum period for which the customer stored such data). SaaS logs detailing the access of confidential folders and other suspicious actions were identified using the Advanced Search (AS) query:

@fields.saas_actor:"UserA@[REDACTED]" AND @fields.saas_software:"Firefox"

Most actions were ‘MailItemsAccessed’ events originating from IPs located in Nigeria [5,6] and one other potentially malicious IP located in the US [7].

‘MailItemsAccessed’ is part of the new Advanced Audit functionality from Microsoft and can be used to determine when email data is accessed by mail protocols and clients. A bind mail access type denotes an individual access to an email message [8].

Figure 2: AS logs shows UserA had not used Firefox to access Office365 for at least 4 weeks prior to the unusual login on the 21st November

Below are details of the main suspicious SaaS activities:

· Time: 2021-11-21 09:05:25 - 2021-11-22 16:57:39 UTC

· SaaS Actor: UserA@[REDACTED]

· SaaS Service: Office365

· SaaS Service Product: Exchange

· SaaS Software: Firefox

· SaaS Office365 Parent Folders:

          o   \Accounts/Passwords
          o   \Invoices
         o   \Sent Items
          o   \Inbox
          o   \Recoverable Items\Deletions

· SaaS Event:

          o   MailItemsAccessed
          o   UserLoggedIn
          o   Update

· SaaS Office365 Mail Access Type: Bind (47 times)

· Source IP addresses:

          o   105.112.59[.]83
          o   105.112.36[.]212
          o   154.6.17[.]16
          o   45.130.83[.]129

· SaaS User Agents:

o Client=OWA;Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:80.0) Gecko/20100101 Firefox/80.0;
o Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:80.0) Gecko/20100101 Firefox/80.0

· Total SaaS logs: 57

At the start of the month on the 5^th November, the user had also been seen logging in from a potentially malicious endpoint [9] in Europe, performing ‘MailItemsAccessed’ and ‘Updates’ events with subjects and a resource location related to invoices and wire transfers from the Sent items folder. This suggests the initial compromise had been earlier in the month, giving the threat actor time to make preparations for the final stages of the attack.

Figure 3: Event log showing the activity of UserA from IP 45.135.187[.]108

2. Office365: UserB

Looking into the model breach ‘SaaS / Access / Suspicious Credential Use And Login User-Agent’, it was seen that a second account, UserB, was also observed logging in from a rare and potentially malicious location in Bangladesh [7]. Similar to UserA, this user had previously logged in exclusively from the USA, and no other accounts within the digital estate had been observed interacting with the Bangladeshi IP address. The login event appeared to bypass MFA (Multi-factor Authentication) and a suspicious user agent, BAV2ROPC, was used. Against misconfigured accounts, this Microsoft user agent is commonly used by attackers to bypass MFA on Office365. It targets Exchange’s Basic Authentication (normally used in POP3/IMAP4 conditions) and results in an OAuth flow which circumvents the additional password security brought by MFA [10].

During the session, additional resources were accessed which appear to be associated with bill and invoice payments. In addition, on the 4^th November, two new suspicious email rules named “..” were created from rare IPs (107.10.56[.]48 and 76.189.202[.]66). This type of behavior is commonly seen during SaaS compromises to delete or forward emails. Typically, an email rule created by a human user will be named to reflect the change being made, such as ‘Move emails from Legal to Urgent’. In contrast, malicious email rules are often short and undescriptive. The rule “..” is likely to blend in without arousing suspicion, while also being easy for the attacker to create and remember.

Details of these rule changes are as follows:

·      Time: 2021-11-04 13:25:06, 2021-11-05 15:50:00 [UTC]
·      SaaS Service: Office365
·      SaaS Service Product: Exchange
·      SaaS Status Message: True
·      SaaS Source IP addresses: 107.10.56[.]48, 76.189.202[.]66
·      SaaS Account Name: O365
·      SaaS Actor: UserB@[REDACTED]
·      SaaS Event: SetInboxRule
·      SaaS Office365 Modified Property Names:
          o   AlwaysDeleteOutlookRulesBlob, Force, Identity, MoveToFolder, Name, FromAddressContainsWords, StopProcessingRules
          o   AlwaysDeleteOutlookRulesBlob, Force, Identity, Name, FromAddressContainsWords, StopProcessingRules
·      SaaS Resource Name: ..

During cloud account compromises, attackers will often use sync operations to download emails to their local email client. During the operations, these clients typically download a large set of mail items from the cloud to a local computer. If the attacker is able to sync all mail items to their mail client, the entire mailbox can be compromised. The attacker is able to disconnect from the account and review and search the email without generating additional event logs.

Both accounts UserA and UserB were observed using ‘MailItemsAccessed’ sync operations between the 1^st and 23^rd November when this attack occurred. However, based on the originating IP of the sync operations, the activity is likely to have been initiated by the legitimate, US-based users. Once the security team were able to confirm the events were expected and legitimate, they could establish that the contents of the mailbox were not a part of the data breach.

Accomplish Mission

After gaining access to the Office365 accounts, sensitive data was downloaded by the attackers to their local system. Either on or before 14^th December, the attacker had seemingly uploaded the documents onto a data leak website. In total, 130MB of data had been made available for download in two separate packages. The packages included audit and accounting financial documents, with file extensions such as DB, XLSX, and PDF.

Figure 4: The two data packages uploaded by the attacker and the extracted contents

In a sample of past SaaS activity of UserA, the subject and attachments appear related to the ‘OUTSTANDING PREPAY WIRES 2021’ excel document found from the data leak website in Figure 4, suggesting a further possibility that the account was associated with the leaked data.

Historic SaaS activity associated with UserA:

·      Time: 2021-11-05 21:21:18 [UTC]
·      SaaS Office365 Logon Type: Owner
·      Protocol: OFFICE365
·      SaaS Account Name: O365
·      SaaS Actor: UserA@[REDACTED].com
·      SaaS Event: Send
·      SaaS Service: Office365
·      SaaS Service Product: Exchange
·      SaaS Status Message: Succeeded
·      SaaS Office365 Attachment: WIRE 2021.xlsx (92406b); image.png (9084b); image.png (1454b); image.png (1648b); image.png (1691b); image.png (1909b); image.png (2094b)
·      SaaS Office365 Subject: Wires 11/8/21
·      SaaS Resource Location: \Drafts
·      SaaS User Agent: Client=OWA;Action=ViaProxy

Based on the available evidence, it is highly likely that the data packages contain the data stolen during the account compromise the previous month.

Once the credentials of an Office365 account are stolen, an attacker can not only access the user's mailbox, but also a full range of Office365 applications such as SharePoint folders, Teams Chat, or files in the user's OneDrive [11]. For example, files shared in Teams chat are stored in OneDrive for Business in a folder named Microsoft Teams Chat Files in the default Document library on SharePoint. One of the files visible on the data leak website, called ‘[REDACTED] CONTRACT.3.1.2020.pdf’, was also observed in the default document folder of a third user account (UserC) within the victim organization, suggesting the compromised accounts may have been able to access shared files stored on other accounts by moving laterally via other O365 applications such as Teams.

One example can be seen in the below AS logs:

·      Time: 2021-11-11 01:58:35 [UTC]
·      SaaS Resource Type: File
·      Protocol: OFFICE365
·      SaaS Account Name: 0365
·      SaaS Actor: UserC@[REDACTED]
·      SaaS Event: FilePreviewed
·      SaaS Service Product: OneDrive
·      SaaS Metric: ResourceViewed
·      SaaS Office365 Application Name: Media Analysis and Transformation Service
·      SaaS Office365 File Extension: pdf
·      SaaS Resource Location: https://[REDACTED]-my.sharepoint.com/personal/userC_[REDACTED]_com/Documents/Microsoft Teams Chat Files/[REDACTED] CONTRACT 3.1.2020.pdf
·      SaaS Resource Name: [REDACTED] CONTRACT 3.1.2020.pdf
·      SaaS Service: Office365
·      SaaS Service Product: OneDrive
·      SaaS User Agent: OneDriveMpc-Transform_Thumbnail/1.0

In the period between the 1^st and 30^th November, the customer’s Darktrace DETECT/Apps trial had raised multiple high-level alerts associated with SaaS account compromise, but there was no evidence of file encryption.

Establish Foothold

Looking back at the start of the attack, it is unclear exactly how the attacker evaded the customer’s pre-existing security stack. At the time of the incident, the victim was using a Barracuda email gateway and Microsoft 365 Threat Management for their cloud environment.

Darktrace detected no indication the accounts were compromised via credential bruteforcing, which would have enabled the attacker to bypass the Azure Active Directory smart lockout (if enabled). The credentials may have been harvested via a phishing campaign which successfully evaded the list of known ‘bad’ domains maintained by their email gateway.

Upon gaining access to the account, the Microsoft Defender for Cloud Apps anomaly detection policies would have been expected to raise an alert [12]. In this instance, the unusual login from Nigeria occurred over 16 hours after the previous login from the US, potentially evading anomaly detection policies such as the ‘Impossible Travel’ rule.

Figure 5: Event log showing the user accessing mail from USA a day before the suspicious usage from Nigeria

Darktrace Coverage

Darktrace DETECT

Throughout this event, high scoring model breaches associated with the attack were visible in the customer’s SaaS Console. In addition, there were two Cyber AI Analyst incidents for ‘Possible Account Hijack’ associated with the two compromised SaaS Office365 accounts, UserA and UserB. The visibility given by Darktrace DETECT also enabled the security team to confirm which files had been accessed and were likely part of the data leak.

Figure 6: Example Cyber AI Analyst incident of UserB SaaS Office365 account

Darktrace RESPOND

In this incident, the attackers successfully compromised O365 accounts in order to exfiltrate customer data. Whilst Darktrace RESPOND/Apps was being trialed and suggested several actions, it was configured in human confirmation mode. The following RESPOND/Apps actions were advised for these activities:

· ‘Antigena [RESPOND] Unusual Access Block’ triggered by the successful login from an unusual IP address, would have actioned the ‘Block IP’ inhibitor, preventing access to the account from the unusual IP for up to 24 hours
· ‘Suspicious Source Activity Block’, triggered by the suspicious user agent used to bypass MFA, would have actioned the ‘Disable User’ inhibitor, disabling the user account for up to 24 hours

During this incident, Darktrace RESPOND/Network was being used in fully autonomous mode in order to prevent the threat actor from pivoting into the network. The security team were unable to conclusively say if any attempts by the attacker to do this had been made.

Concluding Thoughts

Data theft extortion has become a widely used attack technique, and ransomware gangs may increasingly use this technique alone to target organizations without secure data encryption and storage policies.

This case study describes a SaaS data theft extortion incident which bypassed MFA and existing security tools. The attacker appeared to compromise credentials without bruteforce activity, possibly with the use of social engineering through phishing. However, from the first new login, Darktrace DETECT identified the unusual credential use in spite of it being an existing account. Had Darktrace RESPOND/Apps been configured, it would have autonomously responded to halt this login and prevent the attacker from accomplishing their data theft mission.

Thanks to Oakley Cox, Brianna Leddy and Shuh Chin Goh for their contributions.

Appendices

References

[1] https://securelist.com/new-ransomware-trends-in-2022/106457/

[2] https://www.itpro.co.uk/security/ransomware/367624/the-rise-of-double-extortion-ransomware

[3] https://www.malwarebytes.com/blog/news/2020/10/vastaamo-psychotherapy-data-breach-sees-the-most-vulnerable-victims-extorted

[4] https://www.blackfog.com/shift-from-ransomware-to-data-theft-extortion/

[5] https://www.abuseipdb.com/check/105.112.59.83

[6] https://www.abuseipdb.com/check/105.112.36.212

[7] https://www.abuseipdb.com/check/45.130.83.129

[8] https://docs.microsoft.com/en-us/microsoft-365/compliance/mailitemsaccessed-forensics-investigations?view=o365-worldwide

[9] https://www.abuseipdb.com/check/45.135.187.108

[10] https://www.virustotal.com/gui/ip-address/45.137.20.65/details

[11] https://tidorg.com/new-bec-phishing-attack-steals-office-365-credentials-and-bypasses-mfa/

[12] https://docs.microsoft.com/en-us/microsoft-365/security/office-365-security/responding-to-a-compromised-email-account?view=o365-worldwide

Written by

Adrianne Marques

Senior Research Analyst

Inside the SOC

Written by

Adrianne Marques

Senior Research Analyst

Senior Product Marketing Manager, Email

Watch the NIS2 Webinar

Blog

Cloud

July 10, 2025

Crypto Wallets Continue to be Drained in Elaborate Social Media Scam

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.

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.

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.

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.

Figure 4: From the Eternal Decay Gitbook linking to a company with a similar name on Companies House.

Figure 5: Gitbook for “Eternal Decay” listing investors.

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.

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.

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.

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.

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.

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.

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.

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.

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))

}

‍

About the author

Tara Gould

Threat Researcher

Blog

Identity

July 9, 2025

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

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.

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.
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

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.

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

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.

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.

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.

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

About the author

Alexandra Sentenac

Cyber Analyst

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Attack Overview

Accomplish Mission

Establish Foothold

Darktrace Coverage

Darktrace DETECT

Darktrace RESPOND

Concluding Thoughts

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

Real-world intrusions across Azure and AWS

Case 1 - Microsoft Azure

Initial access

Detection and investigation of the threat

Persistence

Case 2 – Amazon Web Services

How the attacker gained access

Darktrace alerting to malicious activity

Internal reconnaissance

Darktrace’s Autonomous Response

Continued reconissance

Lateral movement attempts via RDP connections

Suspicious outbound SSH communication to known threat infrastructure

Final containment and collaborative response

Initial access

Lateral movement and exfiltration

What Darktrace detected

Conclusion

References

Case 1

Darktrace / IDENTITY model alerts

Cyber AI Analyst incidents

Case 2

Darktrace / NETWORK model alerts

Cyber AI Analyst incidents

Case 3

Darktrace / NETWORK model alerts

MITRE ATT&CK mapping

Case 1

Case 2

Case 3

List of IoCs