Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Max Heinemeyer
Global Field CISO
Share
22
Apr 2021
What is APT35?
APT35, sometimes referred to as Charming Kitten, Imperial Kitten, or Tortoiseshell, is a notorious cyber-espionage group which has been active for nearly 10 years. Famous for stealing scripts from HBO’s Game of Thrones in 2017 and suspected of interfering in the U.S. presidential election last year, it has launched extensive campaigns against organizations and officials across North America and the Middle East. Public attribution has associated APT35 with an Iran-based nation state threat actor.
Darktrace regularly detects attacks by many known threat actors including Evil Corp and APT41, alongside large amounts of malicious but uncategorized activity from sophisticated attack groups. As Cyber AI doesn’t rely on pre-defined rules, signatures, or threat intelligence to detect cyber-attacks, it often detects new and previously unknown threats.
This blog post examines a real-world instance of APT35 activity in an organization in the EMEA region. Darktrace observed this activity last June, but due to ongoing investigations, details are only now being released with the wider community. It represents an interesting case for the value of self-learning AI in two key ways:
Identifying ‘low and slow’ attacks: How do you spot an attacker that is lying low and conducts very little detectable activity?
Detecting pre-existing infections without signatures: What if a threat actor is already inside the system when Cyber AI is activated?
Advanced Persistent Threats (APTs) lying low
APT35 had already infected a single corporate device, likely via a spear phishing email, when Cyber AI was deployed in the company’s digital estate for the first time.
The infected device exhibited no other signs of malicious activity beyond continued command and control (C2) beaconing, awaiting instructions from the attackers for several days. This is what we call ‘lying low’ – where the hacker stays present within a system, but remains under the radar, avoiding detection either intentionally, or because they’re focusing on another victim while being content with backdoor access into the organization.
Either way, this is a nightmare scenario for a security team and any security vendor: an APT which has established a foothold and is lying in wait to continue their attack – undetected.
Finding the infected device
When Darktrace’s AI was first activated, it spent five business days learning the unique ‘patterns of life’ for the organization. After this initial, short learning period, Darktrace immediately flagged the infected device and the C2 activity.
Although the breach device had been beaconing since before Darktrace was implemented, Cyber AI automatically clusters devices into ‘peer groups’ based on similar behavioral patterns, enabling Darktrace to identify the continued C2 traffic coming from the device as highly unusual in comparison to the wider, automatically identified peer group. None of its behaviorally close neighbors were doing anything remotely similar, and Darktrace was therefore able to determine that the activity was malicious, and that it represented C2 beaconing.
Darktrace detected the APT35 C2 activity without the use of any signatures or threat intelligence on multiple levels. Responding to the alerts, the internal security team quickly isolated the device and verified with the Darktrace system that no further reconnaissance, lateral movement, or data exfiltration had taken place.
APT35 ‘Charming Kitten’ analysis
Once the C2 was detected, Cyber AI Analyst immediately began analyzing the infected device. The Cyber AI Analyst only highlights the most severe incidents in any given environment and automates many of the typical level one and level two SOC tasks. This includes reviewing all alerts, investigating the scope and nature of each event, and reducing time to triage by 92%.
Figure 1: Similar Cyber AI Analyst report observing C2 communications
Numerous factors made the C2 activity stand out strongly to Darktrace. Combining all those small anomalies, Darktrace was able to autonomously prioritize this behavior and classify it as the most significant security incident in the week.
Figure 2: Example list of C2 detections for an APT35 attack
Some of the command and control destinations were known to threat intelligence and open-source intelligence (OSINT) – for instance, the domain cortanaservice[.]com is a known C2 domain for APT35.
However, the presence of a known malicious domain does not guarantee detection. In fact, the organization had a very mature security stack, yet they failed to discover the existing APT35 infection until Darktrace was activated in their environment.
Assessing the impact of the intrusion
Once an intrusion has been identified, it is important to understand the extent of it – such as whether lateral movement is occurring and what connectivity the infected device has in general. Asset management is never perfect, so it can be very hard for organizations to determine what damage a compromised device is capable of inflicting.
Darktrace presents this information in real time, and from a bird’s-eye perspective, making the assessment very simple. It immediately highlights which subnet the device is located in and any further context.
Figure 3: Darktrace’s Threat Visualizer displaying the connectivity of a device
Based on this information, the organization confirmed that it was a corporate device that had been infected by APT35. As Darktrace shows any credentials associated with the device, a quick assessment could be made of potentially compromised accounts.
Figure 4: Similar and associated credentials of a device
Luckily, only a single local user account was associated with the device.
The exact level of privileges and connectivity which the infected device had, as well as the extent to which the intrusion might have spread from the initially infected device, was still uncertain. By looking at the device’s event log, this became rapidly clear within minutes.
Filtering first for internal connections only (excluding any connections going to the Internet) gave a good idea of the level of connectivity of the device. A cursory glance showed that the device did indeed have some level of internal connectivity. It made DNS requests to the internal domain controller and was making successful NetBIOS connections over ports 135 and 139 internally.
By filtering further in the event log, it quickly became clear that in this time the device had not used any administrative channels, such as RDP, SSH, Telnet, or SMB. This is a strong indicator that no lateral movement over common channels had taken place.
It is more difficult to assess whether the device was performing any other suspicious activity, like stealthy reconnaissance or staging data from other internal devices. Darktrace provided another capability to assess this quickly – filtering the device’s network connections to show only unusual or new connections.
Figure 5: Event device log filtered to show unusual connections only
Darktrace assesses each individual connection for every entity observed in context, using its unsupervised machine learning to evaluate how unusual a given connection is. This could be a single new failed internal connection attempt, indicating stealthy reconnaissance, or a connection over SMB at an unusual time to a new internal destination, implying lateral movement or data staging.
By filtering for only unusual or new connections, Darktrace’s AI produces further leads that can be pursued extremely quickly, thanks to the context and added visibility.
No further suspicious internal connections were observed, strengthening the hypothesis that APT35 was lying low at that time.
Unprecedented but not unpreventable
Darktrace’s 24/7 monitoring service, Proactive Threat Notifications, would have alerted on and escalated the incident. Darktrace RESPOND would have responded autonomously and enforced normal activity for the device, preventing the C2 traffic without interrupting regular business workflows.
It is impossible to predefine where the next attack will come from. APT35 is just one of the many sophisticated threat actors on the scene, and with such a diverse and volatile threat landscape, unsupervised machine learning is crucial in spotting and defending against anomalies, no matter what form they take.
This case study helps illustrate how Darktrace detects pre-existing infections and ‘low and slow’ attacks, and further shows how Darktrace can be used to quickly understand the scope and extent of an intrusion.
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.
Threat actors frequently exploit ongoing world events to trick users into opening and executing malicious files. Darktrace security researchers recently identified a threat group using reports around the arrest of Venezuelan President Nicolàs Maduro on January 3, 2025, as a lure to deliver backdoor malware.
Technical Analysis
While the exact initial access method is unknown, it is likely that a spear-phishing email was sent to victims, containing a zip archive titled “US now deciding what’s next for Venezuela.zip”. This file included an executable named “Maduro to be taken to New York.exe” and a dynamic-link library (DLL), “kugou.dll”.
The binary “Maduro to be taken to New York.exe” is a legitimate binary (albeit with an expired signature) related to KuGou, a Chinese streaming platform. Its function is to load the DLL “kugou.dll” via DLL search order. In this instance, the expected DLL has been replaced with a malicious one with the same name to load it.
Figure 1: DLL called with LoadLibraryW.
Once the DLL is executed, a directory is created C:\ProgramData\Technology360NB with the DLL copied into the directory along with the executable, renamed as “DataTechnology.exe”. A registry key is created for persistence in “HKCU\Software\Microsoft\Windows\CurrentVersion\Run\Lite360” to run DataTechnology.exe --DATA on log on.
Figure 2. Registry key added for persistence.
Figure 3: Folder “Technology360NB” created.
During execution, a dialog box appears with the caption “Please restart your computer and try again, or contact the original author.”
Figure 4. Message box prompting user to restart.
Prompting the user to restart triggers the malware to run from the registry key with the command --DATA, and if the user doesn't, a forced restart is triggered. Once the system is reset, the malware begins periodic TLS connections to the command-and-control (C2) server 172.81.60[.]97 on port 443. While the encrypted traffic prevents direct inspection of commands or data, the regular beaconing and response traffic strongly imply that the malware has the ability to poll a remote server for instructions, configuration, or tasking.
Conclusion
Threat groups have long used geopolitical issues and other high-profile events to make malicious content appear more credible or urgent. Since the onset of the war in Ukraine, organizations have been repeatedly targeted with spear-phishing emails using subject lines related to the ongoing conflict, including references to prisoners of war [1]. Similarly, the Chinese threat group Mustang Panda frequently uses this tactic to deploy backdoors, using lures related to the Ukrainian war, conventions on Tibet [2], the South China Sea [3], and Taiwan [4].
The activity described in this blog shares similarities with previous Mustang Panda campaigns, including the use of a current-events archive, a directory created in ProgramData with a legitimate executable used to load a malicious DLL and run registry keys used for persistence. While there is an overlap of tactics, techniques and procedures (TTPs), there is insufficient information available to confidently attribute this activity to a specific threat group. Users should remain vigilant, especially when opening email attachments.
Credit to Tara Gould (Malware Research Lead) Edited by Ryan Traill (Analyst Content Lead)
Indicators of Compromise (IoCs)
172.81.60[.]97 8f81ce8ca6cdbc7d7eb10f4da5f470c6 - US now deciding what's next for Venezuela.zip 722bcd4b14aac3395f8a073050b9a578 - Maduro to be taken to New York.exe aea6f6edbbbb0ab0f22568dcb503d731 - kugou.dll
Under Medusa’s Gaze: How Darktrace Uncovers RMM Abuse in Ransomware Campaigns
What is Medusa Ransomware in 2025?
In 2025, the Medusa Ransomware-as-a-Service (RaaS) emerged as one of the top 10 most active ransomware threat actors [1]. Its growing impact prompted a joint advisory from the US Cybersecurity and Infrastructure Security Agency (CISA) and the Federal Bureau of Investigation (FBI) [3]. As of January 2026, more than 500 organizations have fallen victim to Medusa ransomware [2].
Darktrace previously investigated Medusa in a 2024 blog, but the group’s rapid expansion and new intelligence released in late 2025 has lead Darktrace’s Threat Research team to investigate further. Recent findings include Microsoft’s research on Medusa actors exploiting a vulnerability in Fortra’s GoAnywhere MFT License Servlet (CVE-2025-10035)[4] and Zencec’s report on Medusa’s abuse of flaws in SimpleHelp’s remote support software (CVE-2024-57726, CVE-2024-57727, CVE-2024-57728) [5].
Reports vary on when Medusa first appeared in the wild. Some sources mention June 2021 as the earliest sightings, while others point to late 2022, when its developers transitioned to the RaaS model, as the true beginning of its operation [3][11].
Madusa Ransomware history and background
The group behind Medusa is known by several aliases, including Storm-1175 and Spearwing [4] [7]. Like its mythological namesake, Medusa has many “heads,” collaborating with initial access brokers (IABs) and, according to some evidence, affiliating with Big Game Hunting (BGH) groups such as Frozen Spider, as well as the cybercriminal group UNC7885 [3][6][13].
Use of Cyrillic in its scripts, activity on Russian-language cybercrime forums, slang unique to Russian criminal subcultures, and avoidance of targets in Commonwealth of Independent States (CIS) countries suggest that Medusa operates from Russia or an allied state [11][12].
Medusa ransomware should not be confused with other similarly named malware, such as the Medusa Android Banking Trojan, the Medusa Botnet/Medusa Stealer, or MedusaLocker ransomware. It is easily distinguishable from these variants because it appends the extension .MEDUSA to encrypted files and drops the ransom note !!!READ_ME_MEDUSA!!!.txt on compromised systems [8].
Who does Madusa Ransomware target?
The group appears to show little restraint, indiscriminately attacking organizations across all sectors, including healthcare, and is known to employ triple extortion tactics whereby sensitive data is encrypted, victims are threatened with data leaks, and additional pressure is applied through DDoS attacks or contacting the victim’s customers, rather than the more common double extortion model [13].
Madusa Ransomware TTPs
To attain initial access, Medusa actors typically purchase access to already compromised devices or accounts via IABs that employ phishing, credential stuffing, or brute-force attacks, and also target vulnerable or misconfigured Internet-facing systems.
Between December 2023 and November 2025, Darktrace observed multiple cases of file encryption related to Medusa ransomware across its customer base. When enabled, Darktrace’s Autonomous Response capability intervened early in the attack chain, blocking malicious activity before file encryption could begin.
Some of the affected were based in Europe, the Middle East and Africa (EMEA), others in the Americas (AMS), and the remainder in the Asia-Pacific and Japan region. The most impacted sectors were financial services and the automotive industry, followed by healthcare, and finally organizations in arts, entertainment and recreation, ICT, and manufacturing.
Remote Monitoring and Management (RMM) tool abuse
In most customer environments where Medusa file encryption attempts were observed, and in one case where the compromise was contained before encryption, unusual external HTTP connections associated with JWrapper were also detected. JWrapper is a legitimate tool designed to simplify the packaging, distribution, and management of Java applications, enabling the creation of executables that run across different operating systems. Many of the destination IP addresses involved in this activity were linked to SimpleHelp servers or associated with Atera.
Medusa actors appear to favor RMM tools such as SimpleHelp. Unpatched or misconfigured SimpleHelp RMM servers can serve as an initial access vector to the victims’ infrastructure. After gaining access to SimpleHelp management servers, the threat actors edit server configuration files to redirect existing SimpleHelp RMM agents to communicate with unauthorized servers under their control.
The SimpleHelp tool is not only used for command-and-control (C2) and enabling persistence but is also observed during lateral movement within the network, downloading additional attack tools, data exfiltration, and even ransomware binary execution. Other legitimate remote access tools abused by Medusa in a similar manner to evade detection include Atera, AnyDesk, ScreenConnect, eHorus, N-able, PDQ Deploy/Inventory, Splashtop, TeamViewer, NinjaOne, Navicat, and MeshAgent [4][5][15][16][17].
Data exfiltration
Another correlation among Darktrace customers affected by Medusa was observed during the data exfiltration phase. In several environments, data was exfiltrated to the endpoints erp.ranasons[.]com or pruebas.pintacuario[.]mx (143.110.243[.]154, 144.217.181[.]205) over ports 443, 445, and 80. erp.ranasons[.]com was seemingly active between November 2024 and September 2025, while pruebas.pintacuario[.]mx was seen from November 2024 to March 2025. Evidence suggests that pruebas.pintacuario[.]mx previously hosted a SimpleHelp server [22][23].
Apart from RMM tools, Medusa is also known to use Rclone and Robocopy for data exfiltration [3][19]. During one Medusa compromise detected in mid-2024, the customer’s data was exfiltrated to external destinations associated with the Ngrok proxy service using an SSH-2.0-rclone client.
Medusa Compromise Leveraging SimpleHelp
In Q4 2025, Darktrace assisted a European company impacted by Medusa ransomware. The organization had partial Darktrace / NETWORK coverage and had configured Darktrace’s Autonomous Response capability to require manual confirmation for all actions. Despite these constraints, data received through the customer’s security integration with CrowdStrike Falcon enabled Darktrace analysts to reconstruct the attack chain, although the initial access vector remains unclear due to limited visibility.
In late September 2025, a device out of the scope of Darktrace's visibility began scanning the network and using RDP, NTLM/SMB, DCE_RPC, and PowerShell for lateral movement.
CrowdStrike “Defense Evasion: Disable or Modify Tools” alerts related to a suspicious driver (c:\windows\[0-9a-b]{4}.exe) and a PDQ Deploy executable (share=\\<device_hostname>\ADMIN$ file=AdminArsenal\PDQDeployRunner\service-1\exec\[0-9a-b]{4}.exe) suggest that the attackers used the Bring Your Own Vulnerable Driver (BYOVD) technique to terminate antivirus processes on network devices, leveraging tools such as KillAV or AbyssWorker along with the PDQ Software Deployment solution [19][26].
A few hours later, Darktrace observed the same device that had scanned the network writing Temp\[a-z]{2}.exe over SMB to another device on the same subnet. According to data from the CrowdStrike alert, this executable was linked to an RMM application located at C:\Users\<compromised_user>\Documents\[a-z]{2}.exe. The same compromised user account later triggered a CrowdStrike “Command and Control: Remote Access Tools” alert when accessing C:\ProgramData\JWrapper-Remote Access\JWrapper-Remote Access Bundle-[0-9]{11}\JWrapperTemp-[0-9]{10}-[0-9]{1}-app\bin\windowslauncher.exe [27].
Figure 1: An executable file associated with the SimpleHelp RMM tool being written to other devices using the SMB protocol, as detected by Darktrace.
Soon after, the destination device and multiple other network devices began establishing connections to 31.220.45[.]120 and 213.183.63[.]41, both of which hosted malicious SimpleHelp RMM servers. These C2 connections continued for more than 20 days after the initial compromise.
CrowdStrike integration alerts for the execution of robocopy . "c:\windows\\" /COPY:DT /E /XX /R:0 /W:0 /NP /XF RunFileCopy.cmd /IS /IT commands on several Windows servers, suggested that this utility was likely used to stage files in preparation for data exfiltration [19].
Around two hours later, Darktrace detected another device connecting to the attacker’s SimpleHelp RMM servers. This internal server had ‘doc’ in its hostname, indicating it was likely a file server. It was observed downloading documents from another internal server over SMB and uploading approximately 70 GiB of data to erp.ranasons[.]com (143.110.243[.]154:443).
Figure 2: Data uploaded to erp.ranasons[.]com and the number of model alerts from the exfiltrating device, represented by yellow and orange dots.
Darktrace’s Cyber AI Analyst autonomously investigated the unusual connectivity, correlating the separate C2 and data exfiltration events into a single incident, providing greater visibility into the ongoing attack.
Figure 3: Cyber AI Analyst identified a file server making C2 connections to an attacker-controlled SimpleHelp server (213.183.63[.]41) and exfiltrating data to erp.ranasons[.]com.
Figure 4: The same file server that connected to 213.183.63[.]41 and exfiltrated data to erp.ranasons[.]com was also observed attempting to connect to an IP address associated with Moscow, Russia (193.37.69[.]154:7070).
One of the devices connecting to the attacker's SimpleHelp RMM servers was also observed downloading 35 MiB from [0-9]{4}.filemail[.]com. Filemail, a legitimate file-sharing service, has reportedly been abused by Medusa actors to deliver additional malicious payloads [11].
Figure 5: A device controlled remotely via SimpleHelp downloading additional tooling from the Filemail file-sharing service.
Finally, integration alerts related to the ransomware binary, such as c:\windows\system32\gaze.exe and <device_hostname>\ADMIN$ file=AdminArsenal\PDQDeployRunner\service-1\exec\gaze.exe, along with “!!!READ_ME_MEDUSA!!!.txt” ransom notes were observed on network devices. This indicates that file encryption in this case was most likely carried out directly on the victim hosts rather than via the SMB protocol [3].
Conclusion
Threat actors, including nation-state actors and ransomware groups like Medusa, have long abused legitimate commercial RMM tools, typically used by system administrators for remote monitoring, software deployment, and device configuration, instead of relying on remote access trojans (RATs).
Attackers employ existing authorized RMM tools or install new remote administration software to enable persistence, lateral movement, data exfiltration, and ingress tool transfer. By mimicking legitimate administrative behavior, RMM abuse enables attackers to evade detection, as security software often implicitly trusts these tools, allowing attackers to bypass traditional security controls [28][29][30].
To mitigate such risks, organizations should promptly patch publicly exposed RMM servers and adopt anomaly-based detection solutions, like Darktrace / NETWORK, which can distinguish legitimate administrative activity from malicious behavior, applying rapid response measures through its Autonomous Response capability to stop attacks in their tracks.
Darktrace delivers comprehensive network visibility and Autonomous Response capabilities, enabling real-time detection of anomalous activity and rapid mitigation, even if an organization fall under Medusa’s gaze.
Credit to Signe Zaharka (Principal Cyber Analyst) and Emma Foulger (Global Threat Research Operations Lead
Edited by Ryan Traill (Analyst Content Lead)
Appendices
List of Indicators of Compromise (IoCs)
IoC - Type - Description + Confidence + Time Observed
185.108.129[.]62 IP address Malicious SimpleHelp server observed during Medusa attacks (High confidence) - March 7, 2023
185.126.238[.]119 IP address Malicious SimpleHelp server observed during Medusa attacks (High confidence) - November 26-27, 2024
213.183.63[.]41 IP address Malicious SimpleHelp server observed during Medusa attacks (High confidence) - November 28, 2024 - Sep 30, 2025
213.183.63[.]42 IP address Malicious SimpleHelp server observed during Medusa attacks (High confidence) - July 4 -9 , 2024
31.220.45[.]120 IP address Malicious SimpleHelp server observed during Medusa attacks (High confidence) - September 12 - Oct 20 , 2025
91.92.246[.]110 IP address Malicious SimpleHelp server observed during Medusa attacks (High confidence) - May 24, 2024
45.9.149[.]112:15330 IP address Malicious SimpleHelp server observed during Medusa attacks (High confidence) - June 21, 2024
89.36.161[.]12 IP address Malicious SimpleHelp server observed during Medusa attacks (High confidence) - June 26-28, 2024
193.37.69[.]154:7070 IP address Suspicious RU IP seen on a device being controlled via SimpleHelp and exfiltrating data to a Medusa related endpoint - September 30 - October 20, 2025
erp.ranasons[.]com·143.110.243[.]154 Hostname Data exfiltration destination - November 27, 2024 - September 30, 2025
pruebas.pintacuario[.]mx·144.217.181[.]205 - Hostname Data exfiltration destination - November 27, 2024 - March 26, 2025
lirdel[.]com · 44.235.83[.]125/a.msi (1b9869a2e862f1e6a59f5d88398463d3962abe51e19a59) File & hash Atera related file downloaded with PowerShell - June 20, 2024
wizarr.manate[.]ch/108.215.180[.]161:8585/$/1dIL5 File Suspicious file observed on one of the devices exhibiting unusual activity during a Medusa compromise - February 28, 2024
!!!READ_ME_MEDUSA!!!.txt" File - Ransom note
*.MEDUSA - File extension File extension added to encrypted files
gaze.exe – File - Ransomware binary
Darktrace Model Coverage
Darktrace / NETWORK model detections triggered during connections to attacker controlled SimpleHelp servers:
Anomalous Connection/Anomalous SSL without SNI to New External
Anomalous Connection/Multiple Connections to New External UDP Port
Anomalous Connection/New User Agent to IP Without Hostname
.jpeg)
![Data uploaded to erp.ranasons[.]com and the number of model alerts from the exfiltrating device, represented by yellow and orange dots.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/69601f43842f8016c3898afd_Screenshot%202026-01-08%20at%201.18.52%E2%80%AFPM.png)
![Cyber AI Analyst identified a file server making C2 connections to an attacker-controlled SimpleHelp server (213.183.63[.]41) and exfiltrating data to erp.ranasons[.]com.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/69601f524d01b4d269313c38_Screenshot%202026-01-08%20at%201.19.06%E2%80%AFPM.png)
![The same file server that connected to 213.183.63[.]41 and exfiltrated data to erp.ranasons[.]com was also observed attempting to connect to an IP address associated with Moscow, Russia (193.37.69[.]154:7070).](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/69601f6a598a3380572e284e_Screenshot%202026-01-08%20at%201.19.32%E2%80%AFPM.png)
