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January 8, 2026

Under Medusa’s Gaze: How Darktrace Uncovers RMM Abuse in Ransomware Campaigns

Medusa ransomware increasingly exploits remote monitoring and management (RMM) tools for persistence, lateral movement, and data exfiltration. This blog explores Medusa’s tactics, real-world detections, and how anomaly-based solutions with Autonomous Response can stop attacks.
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
Signe Zaharka
Principal Cyber Analyst
medusa ransomwareDefault blog image
08
Jan 2026

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].

Medusa 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 Medusa 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].

Medusa 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.

In addition to the GoAnywhere MFT and SimpleHelp RMM flaws, other vulnerabilities exploited in Medusa attacks include ConnectWise ScreenConnect RMM (CVE-2024-1709), Microsoft Exchange Server (CVE-2021-34473, also known as ProxyShell), and Fortinet Enterprise Management Servers (CVE-2023-48788) [18][19][20][21][24][25].

Darktrace’s Coverage of Medusa Ransomware

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].

An executable file associated with the SimpleHelp RMM tool being written to other devices using the SMB protocol, as detected by Darktrace.
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).

Data uploaded to erp.ranasons[.]com and the number of model alerts from the exfiltrating device, represented by yellow and orange dots.
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.

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 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.
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).
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].

A device controlled remotely via SimpleHelp downloading additional tooling from the Filemail file-sharing service.
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

Anomalous Connection/Rare External SSL Self-Signed

Anomalous Connection/Suspicious Self-Signed SSL

Anomalous File/EXE from Rare External Location

Anomalous Server Activity/Anomalous External Activity from Critical Network Device

Anomalous Server Activity/New User Agent from Internet Facing System

Anomalous Server Activity/Outgoing from Server

Anomalous Server Activity/Rare External from Server

Compromise/High Volume of Connections with Beacon Score

Compromise/Large Number of Suspicious Failed Connections

Compromise/Ransomware/High Risk File and Unusual SMB

Device/New User Agent

Unusual Activity/Unusual External Data to New Endpoint

Unusual Activity/Unusual External Data Transfer

Darktrace / NETWORK Model Detections during the September/October 2025 Medusa attack:

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Download and Upload

Anomalous Connection / Low and Slow Exfiltration

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Uncommon 1 GiB Outbound

Anomalous Connection / Unusual Admin RDP Session

Anomalous Connection / Unusual Incoming Long Remote Desktop Session

Anomalous Connection / Unusual Long SSH Session

Anomalous File / EXE from Rare External Location

Anomalous File / Internal/Unusual Internal EXE File Transfer

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous Server Activity / Outgoing from Server

Anomalous Server Activity / Rare External from Server

Compliance / Default Credential Usage

Compliance / High Priority Compliance Model Alert

Compliance / Outgoing NTLM Request from DC

Compliance / Possible Unencrypted Password File On Server

Compliance / Remote Management Tool On Server

Compromise / Large Number of Suspicious Failed Connections

Compromise / Large Number of Suspicious Successful Connections

Compromise / Ransomware/High Risk File and Unusual SMB

Compromise / Suspicious Beaconing Behaviour

Compromise / Suspicious HTTP and Anomalous Activity

Compromise / Sustained SSL or HTTP Increase

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Device / ICMP Address Scan

Device / Increase in New RPC Services

Device / Initial Attack Chain Activity

Device / Large Number of Model Alert

Device / Large Number of Model Alerts from Critical Network Device

Device / Lateral Movement and C2 Activity

Device / Multiple C2 Model Alert

Device / Network Scan

Device / Possible SMB/NTLM Reconnaissance

Device / Spike in LDAP Activity

Device / Suspicious Network Scan Activity

Device / Suspicious SMB Scanning Activity

Security Integration / High Severity Integration Incident

Security Integration / Low Severity Integration Incident

Unusual Activity / Enhanced Unusual External Data Transfer

Unusual Activity / Internal Data Transfer

Unusual Activity / Unusual External Activity

Unusual Activity / Unusual External Data to New Endpoint

Unusual Activity / Unusual External Data Transfer

User / New Admin Credentials on Server

Autonomous Response Actions

Antigena / Network/External Threat/Antigena File then New Outbound Block

Antigena / Network/External Threat/Antigena Ransomware Block

Antigena / Network/External Threat/Antigena Suspicious Activity Block

Antigena / Network/External Threat/Antigena Suspicious File Block

Antigena / Network/Insider Threat/Antigena Internal Anomalous File Activity

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

Antigena / Network/Insider Threat/Antigena Large Data Volume Outbound Block

Antigena / Network/Insider Threat/Antigena Network Scan Block

Antigena / Network/Insider Threat/Antigena Unusual Privileged User Activities Block

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

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

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

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

Antigena / Network/Significant Anomaly/Repeated Antigena Alerts

MITRE ATT&CK Mapping

Technique Name, Tactic, ID, Sub-Technique

Application Layer Protocol , COMMAND AND CONTROL , T1071

Automated Collection , COLLECTION , T1119

Automated Exfiltration , EXFILTRATION , T1020

Brute Force , CREDENTIAL ACCESS , T1110

Client Configurations , RECONNAISSANCE , T1592.004 , T1592

Cloud Accounts , DEFENSE EVASION ,  PERSISTENCE ,  PRIVILEGE ESCALATION ,  INITIAL ACCESS , T1078.004 , T1078

Command-Line Interface , EXECUTION ICS , T0807

Credential Stuffing , CREDENTIAL ACCESS , T1110.004 , T1110

Data Encrypted for Impact , IMPACT , T1486

Data from Network Shared Drive , COLLECTION , T1039

Data Obfuscation , COMMAND AND CONTROL , T1001

Data Staged , COLLECTION , T1074

Data Transfer Size Limits , EXFILTRATION , T1030

Default Accounts , DEFENSE EVASION ,  PERSISTENCE ,  PRIVILEGE ESCALATION ,  INITIAL ACCESS , T1078.001 , T1078

Default Credentials , LATERAL MOVEMENT ICS , T0812

Distributed Component Object Model , LATERAL MOVEMENT , T1021.003 , T1021

Drive-by Compromise , INITIAL ACCESS ICS , T0817

Drive-by Compromise , INITIAL ACCESS , T1189

Email Collection , COLLECTION , T1114

Exfiltration Over Alternative Protocol , EXFILTRATION , T1048

Exfiltration Over C2 Channel , EXFILTRATION , T1041

Exfiltration to Cloud Storage , EXFILTRATION , T1567.002 , T1567

Exploit Public-Facing Application , INITIAL ACCESS , T1190

Exploitation for Privilege Escalation , PRIVILEGE ESCALATION , T0890

Exploitation of Remote Services , LATERAL MOVEMENT , T1210

Exploits , RESOURCE DEVELOPMENT , T1588.005 , T1588

File and Directory Discovery , DISCOVERY , T1083

File Deletion , DEFENSE EVASION , T1070.004 , T1070

Graphical User Interface , EXECUTION ICS , T0823

Ingress Tool Transfer , COMMAND AND CONTROL , T1105

Lateral Tool Transfer , LATERAL MOVEMENT , T1570

LLMNR/NBT-NS Poisoning and SMB Relay , CREDENTIAL ACCESS ,  COLLECTION , T1557.001 , T1557

Malware , RESOURCE DEVELOPMENT , T1588.001 , T1588

Network Service Scanning , DISCOVERY , T1046

Network Share Discovery , DISCOVERY , T1135

Non-Application Layer Protocol , COMMAND AND CONTROL , T1095

Non-Standard Port , COMMAND AND CONTROL , T1571

One-Way Communication , COMMAND AND CONTROL , T1102.003 , T1102

Pass the Hash , DEFENSE EVASION ,  LATERAL MOVEMENT , T1550.002 , T1550

Password Cracking , CREDENTIAL ACCESS , T1110.002 , T1110

Password Guessing , CREDENTIAL ACCESS , T1110.001 , T1110

Password Spraying , CREDENTIAL ACCESS , T1110.003 , T1110

Program Download , LATERAL MOVEMENT ICS , T0843

Program Upload , COLLECTION ICS , T0845

Remote Access Software , COMMAND AND CONTROL , T1219

Remote Desktop Protocol , LATERAL MOVEMENT , T1021.001 , T1021

Remote System Discovery , DISCOVERY , T1018

Scanning IP Blocks , RECONNAISSANCE , T1595.001 , T1595

Scheduled Transfer , EXFILTRATION , T1029

Spearphishing Attachment , INITIAL ACCESS ICS , T0865

Standard Application Layer Protocol , COMMAND AND CONTROL ICS , T0869

Supply Chain Compromise , INITIAL ACCESS ICS , T0862

User Execution , EXECUTION ICS , T0863

Valid Accounts , DEFENSE EVASION ,  PERSISTENCE ,  PRIVILEGE ESCALATION ,  INITIAL ACCESS , T1078

Valid Accounts , PERSISTENCE ICS ,  LATERAL MOVEMENT ICS , T0859

Vulnerabilities , RESOURCE DEVELOPMENT , T1588.006 , T1588

Vulnerability Scanning , RECONNAISSANCE , T1595.002 , T1595

Web Protocols , COMMAND AND CONTROL , T1071.001 , T1071

References

1. https://www.intel471.com/blog/threat-hunting-case-study-medusa-ransomware

2. https://www.ransomware.live/group/medusa

3. https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-071a

4. https://www.microsoft.com/en-us/security/blog/2025/10/06/investigating-active-exploitation-of-cve-2025-10035-goanywhere-managed-file-transfer-vulnerability/

5. https://zensec.co.uk/blog/how-rmm-abuse-fuelled-medusa-dragonforce-attacks/

6. https://www.checkpoint.com/cyber-hub/threat-prevention/ransomware/medusa-ransomware-group/

7. https://cyberpress.org/medusa-ransomware-attacks-spike-42/

8. https://blog.barracuda.com/2025/02/25/medusa-ransomware-and-its-cybercrime-ecosystem

10. https://www.cyberdaily.au/security/10021-more-monster-than-myth-unpacking-the-medusa-ransomware-operation

11. https://unit42.paloaltonetworks.com/medusa-ransomware-escalation-new-leak-site/

12. https://www.bitdefender.com/en-us/blog/businessinsights/medusa-ransomware-a-growing-threat-with-a-bold-online-presence

13. https://redpiranha.net/news/medusa-ransomware-everything-you-need-know

14.  https://www.theregister.com/2025/03/13/medusa_ransomware_infects_300_critical/

15. https://www.s-rminform.com/latest-thinking/cyber-threat-advisory-medusa-and-the-simplehelp-vulnerability

16. https://nagomisecurity.com/medusa-ransomware-us-cert-alert

17. https://arcticwolf.com/resources/blog/arctic-wolf-observes-campaign-exploiting-simplehelp-rmm-software-for-initial-access/

18. https://securityboulevard.com/2025/04/medusa-ransomware-inside-the-2025-resurgence-of-one-of-the-internets-most-aggressive-threats/

19. https://thehackernews.com/2025/03/medusa-ransomware-hits-40-victims-in.html

20.  https://www.quorumcyber.com/threat-intelligence/critical-alert-medusa-ransomware-threat-highlighted-by-fbi-cisa-and-ms-isac/

21. https://brandefense.io/blog/stone-gaze-in-depth-analysis-of-medusa-ransomware/

22. https://www.darktrace.com/ja/blog/2025-cyber-threat-landscape-darktraces-mid-year-review

23. https://www.joesandbox.com/analysis/1576447/0/html

24. https://blog.barracuda.com/2025/02/25/medusa-ransomware-and-its-cybercrime-ecosystem

25. https://shassit.mit.edu/news/medusa-ransomware-attacks-on-gmail/

26. https://thehackernews.com/2025/03/medusa-ransomware-uses-malicious-driver.html

27. https://www.cisa.gov/news-events/cybersecurity-advisories/aa25-163a

28. https://www.catonetworks.com/blog/cato-ctrl-investigation-of-rmm-tools/

29. https://redcanary.com/threat-detection-report/trends/rmm-tools/

30. https://www.proofpoint.com/us/blog/threat-insight/remote-monitoring-and-management-rmm-tooling-increasingly-attackers-first-choice

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
Signe Zaharka
Principal Cyber Analyst

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August 21, 2026

AI Agents: Securing the Path from Intent to Action

Photograph of AI data centerDefault blog imageDefault blog image

The UK’s National Cyber Security Centre (NCSC) recently published guidance on managing the cyber risk of agentic AI. While the document is framed as interim advice as more formal guidance is developed, the framing reflects the current state of the industry: organizations are already deploying agents into production environments while standards, controls, and operating models for autonomous systems remain unsettled. Governance is evolving alongside adoption rather than preceding it, a reality which underscores the importance of robust controls.  

The NCSC’s guidance recommends aligning controls to an agent's level of autonomy, assigning distinct identities, limiting permissions, constraining access to systems and data, monitoring activity, maintaining human oversight, and preserving the ability to intervene when necessary. Most of these recommendations will sound familiar to security teams. The challenge is not the novelty of the controls. It is the type of system those controls now need to govern.

The shift from model security to agent security

For several years, AI security discussions have focused heavily on models. Can a model be manipulated? Jailbroken? Trusted? Can it expose information it should not? Those questions remain important, but they capture only part of the problem. A model generating text is one thing. A system connected to identities, applications, tools, workflows, and business data is another.

The difference becomes clearer when comparing a chatbot that answers questions with an agent that can retrieve customer records, update tickets, invoke tools, trigger workflows, and interact with external systems. The underlying model may be identical. Its access is not. The security question begins to shift from what the model knows to what the system can do.

The same theme appears in the Five Eyes statement released earlier this year, describing AI as a force multiplier that is accelerating both offensive and defensive cyber operations. The NCSC guidance explores what that reality looks like when autonomous systems begin operating inside enterprise environments.

Securing AI agents in operation

The NCSC spends relatively little time debating model behavior and considerably more time discussing identity, permissions, monitoring, oversight, containment, and response. Agents are treated as participants within an environment rather than isolated pieces of technology.  

That's broadly consistent with how we think about the problem at Darktrace.

An agent should not be treated as an extension of a user account. It develops its own behavioral patterns. It accesses systems, interacts with data, invokes tools, and moves across workflows in ways that can be observed independently. Understanding what an agent is permitted to do matters. Understanding how it actually behaves once deployed, and whether that behavior aligns with business intent, matters just as much.

Identity provides an obvious example. The NCSC recommends assigning distinct identities to agents rather than allowing them to disappear into surrounding human or service accounts. Most importantly, assigning agents distinct identities enables independent behavioral monitoring.

Development assumptions vs. real-world behavior

The same principle extends to monitoring. NCSC guidance places agent activity within normal security operations rather than treating it as a separate AI governance function. Many of the controls described are put in place before an agent begins operating. Sandboxing, credential design, approval workflows and human oversight all reflect judgments about how the system is expected to behave and what risks it is likely to create.

Actual use may challenge those assumptions. Access patterns change. Workflows expand. Systems begin interacting with resources they have never touched before. Processes that appeared reasonable during design behave differently in production. Human oversight requirements may turn out to be either excessive or inadequate once the system is operating at scale and operating within the context of unique business processes.

The Five Eyes statement points to a similar issue: organizations need confidence that controls continue to work as intended once systems are exposed to real users, data, tools and operational pressures. Often, the question is not whether an agent is technically allowed to perform an action, but whether its behavior remains consistent with the role it was intended to play.

Monitoring and governance of AI agents go hand-in-hand

This problem is exactly why monitoring and governance should be treated as part of the same process. Governance sets the initial parameters for deployment, while monitoring provides evidence about whether those parameters remain appropriate. That evidence should, in turn, inform changes to permissions, controls and oversight.

This matters increasingly as autonomous systems are integrated into business processes. The relevant risk is shaped not only by the model or agent itself, but by what it can access, what actions it can take, and how its behavior changes in practice.

Developing continuous oversight of AI agent behavior

The implication is clear: governance cannot end at deployment. Organizations need a way to understand how agents behave after deployment, test whether controls remain appropriate, and adjust them as conditions change. That requires visibility not just into technical activity, but into whether that activity makes sense in the context of the business process the agent is intended to support.

This is where business-centric behavioral security can become critical. Risk does not emerge from the model itself: it emerges from the actions an autonomous system takes within the enterprise and the downstream consequences of those actions.  

An agent can operate exactly as intended and still create risk if it accesses sensitive information in an unexpected context, exercises permissions in ways that create unintended exposure, or influences business processes in ways that were not anticipated during design and review.

Traditional governance vs. behavioral analytics

Traditional governance frameworks provide assurance at a point in time. Behavioral security can provide ongoing visibility into how autonomous systems interact with the organization they are meant to serve. Rather than focusing exclusively on model performance or policy compliance, organizations need to understand whether an agent's behavior aligns with business intent, operational expectations, and acceptable risk tolerances as conditions change.

As enterprises move from isolated AI deployments to interconnected ecosystems of agents, visibility into behavior becomes as important as visibility into code. Governance determines what an autonomous system is permitted to do. Behavioral analytics helps determine what it is doing, what business outcomes it is producing, and whether those outcomes remain aligned with the organization's objectives.

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About the author
Margaret Cunningham, PhD
VP, Security & AI Strategy, Field CISO

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August 19, 2026

When AI Becomes the Lure: A Fake Gemini Installer Delivers Vidar

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Key takeaways

  • Darktrace observed a customer download a fake Google Gemini installer hosted on Google Colab, resulting in the execution of the Vidar information stealer.
  • Darktrace identified the compromise through behavioral indicators, including suspicious process activity, anomalous network communications, and indicators of credential theft, before autonomously containing the threat.
  • The incident highlights how threat actors are increasingly exploiting trusted platforms and a growing interest in AI tools to distribute malware through seemingly legitimate software acquisition workflows.

The Growing Abuse of Generative AI

As organizations are increasingly adopting generative AI tools into their daily workflows, attackers are adapting their distribution methods accordingly too. As part of their day-to-day work, users are now searching for AI assistants, programming tools, browser extensions, desktop applications, and productivity integrations.

Recent reports have highlighted campaigns that use fake AI software and AI-related installers to distribute malware and steal credentials [1]. Researchers have documented campaigns that exploit fake AI-themed websites and services to distribute information stealers and backdoors [2]. Security researchers have also observed attackers disguising malware as legitimate installers for AI software to increase the likelihood of victim interaction and execution [3].

In July 2026, Darktrace observed one such case within a customer environment in the Europe, Middle East and Africa (EMEA) region, where attackers used a fake generative AI installer to deliver the prolific information stealer Vidar. This incident highlights how threat actors are exploiting interest in AI services to distribute established malware using increasingly convincing social engineering techniques.

How a Fake Gemini Installer Delivered Vidar

Initial Access: From Search Result to Malware Download

Unlike many malware campaigns that begin with a phishing email, this activity appears to have originated from a user searching for and downloading software.

Darktrace first observed unusual activity on the customer network after a suspicious executable file was launched from a user’s Download folder. Further investigation revealed that the file purported to be a Google Gemini installer and was named “Download_Google_Gemini_For_Windows.exe”.

During the initial analysis, it was noted that the top search result for the suspicious filename associated pointed to a file hosted on Google Colab, a cloud-based Jupyter notebook platform, commonly used by developers, researchers, and data scientists to run code and machine learning workloads through a web browser. By leveraging another trusted Google platform, the attacker increased the likelihood that users would perceive the download as legitimate, making the lure more convincing to those searching for Gemini-related software.

Figure 1: The Google Colab page containing a download prompt for the fake Google Gemini installer.

Further investigation of the Google Colab page revealed that the download prompt redirected users to a secondary site, hxxps://micronsoftwares[.]com, which posed as a "Windows Software Hub" download page and offered the fake Gemini installer for download.

Figure 2: The secondary website posing as a "Windows Software Hub" download page, which likely hosted the fake Gemini installer.

While the investigation did not uncover any HTTP or file-download telemetry data that conclusively identified the download source, SSL communication sessions with Google Colab were detected immediately before the suspicious file was executed. The timing of these connections suggests that the user interacted with the Colab resource before being redirected to the secondary site from which the executable was downloaded.

The user was not simply tricked into opening an email attachment; instead, the attacker embedded malicious content into a process many users would consider entirely legitimate: searching for and downloading software associated with a trusted platform.

Weaponizing Trusted Platforms

At the time of review (July 15, 2026), Darktrace's Threat Research team confirmed that the Google Colab page was still active and prompting users to download a ZIP archive containing the binary file.

The archive also appeared to contain a README file instructing users to run the binary file with administrator privileges and add it to their antivirus software’s exception lists. These instructions suggest that the campaign relied heavily on social engineering, convincing users to take actions that would facilitate malware execution and potentially bypass security checks.

The use of a legitimate platform also complicates the user’s decision-making. Downloads associated with a trusted service are often perceived as less suspicious than those hosted on unfamiliar domains. When combined with the branding of a widely used AI tool, the lure becomes even more convincing.

Malware Analysis

Darktrace’s Threat Research team identified the executable file as the information-stealing malware Vidar. Analysis revealed that the binary file was a newer Go-compiled variant that communicated with Telegram-based infrastructure. Darktrace’s researchers also identified dtm[.]kijangturbo88[.]top as a command-and-control (C2) endpoint associated with the activity. While the malware itself was not novel, the lure and delivery mechanism was.

For a deeper look at the information stealer, see Darktrace’s 2023 analysis of Vidar.

Figure 3: Darktrace’s detection of the unusual outbound connection associated with the fake Gemini installer.

Shortly after execution, the process established communications with the external IP address 91.98.98[.]86 via port 443, directly linking the executable to suspicious network activity observed on the device. Subsequent open-source intelligence (OSINT) analysis of the revealed multiple malicious associations [5].

Additional Darktrace detections included unusual SSL activity from the affected device. Analysis of related SSL telemetry identified 91.98.111[.]49 as additional infrastructure associated  with the activity [6].

Subsequent alerts from the customer's Microsoft Defender for Endpoint integration later confirmed activity consistent with the theft of browser credentials and other sensitive data from the affected endpoint.

Taken together, these detections provided a clear picture of the attack, from the execution of a suspicious file and unusual network connections to indicators of C2 activity and credential theft.

Figure 4: Darktrace’s detection of anomalous activity following the execution of the fake Gemini installer, seen in the Model Alert Event Log.

Darktrace's Autonomous Response

Following the detection, Darktrace’s Autonomous Response took immediate containment action, including blocking communication with suspicious external infrastructure, including 91.98.98[.]86, and quarantining the compromised device.

Despite the apparent legitimacy of the activity, with the installer hosted on a trusted platform and resembling a routine software download, Darktrace was able to detect and contain the attack because the device's behavior deviated from its normal pattern.

Figure 5: Automated containment actions implemented by Darktrace's Autonomous Response following the detection of activity associated with the fake Gemini installer.

Conclusion

This investigation highlights how threat actors continue to adapt established malware delivery techniques to emerging technology trends. While the malware itself was not new, the distribution method was. By disguising Vidar as a Google Gemini installer and hosting the malicious content on a trusted platform, the attack aligned its lure with a growing behavioral trend: users actively searching for AI tools and services as part of their day-to-day work.

Although fake installers are not a new phenomenon, the rapid rise of generative AI has created new opportunities for threat actors. Rather than relying solely on traditional delivery methods, attackers can now target users who are actively searching for AI applications. As AI adoption continues to accelerate across enterprise environments, organizations should remain alert to campaigns that exploit this interest through fake applications, malicious websites, manipulated search results, the misuse of trusted platforms, and AI-themed social engineering.

Credit to Rushanth Ramanathan (Cyber Analyst) Joanna Ng (Detection Engineer)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Security Integration / C2 Activity and Integration Detection
  • Endpoint / New Suspicious Executable Launched
  • Endpoint / Process Connection / Unusual Connection from New Process
  • Anomalous Connection / Rare External SSL Self-Signed
  • Security Integration / High Severity Integration Detection
  • Antigena / Network / Significant Anomaly /  Antigena Significant Security Integration and Network Activity Block

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

List of Indicators of Compromise (IoCs)

IoC Type Description
Download_Google_Gemini_For_Windows.exe File Fake Gemini-themed installer observed during the investigation.
GoogleAppInstaller.exe File Related executable identified through endpoint telemetry.
91.98.98[.]86 IP Address External destination contacted by the malicious executable.
91.98.111[.]49 IP Address Related infrastructure identified through SSL certificate pivoting.
dtm[.]kijangturbo88[.]top Domain Command-and-control endpoint identified during malware analysis.
1e13c2c9eac72daf63fd00a9946878949e159ae6ec51b54ec64f942d79d61913 SHA256 Malware sample associated with the fake Gemini installer.

MITRE ATT@CK Mapping


MITRE ATT&CK Mapping Tactic Technique
Initial Access T1204 User Execution
Execution T1204.002 User Execution: Malicious File
Defence Evasion T1036 Masquerading
Credential Access T1555 Credentials from Password Stores
Credential Access T1555.003 Credentials from Web Browsers
Command and Control T1071 Application Layer Protocol
Exfiltration T1041 Exfiltration Over C2 Channel
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About the author
Rushanth Ramanathan
Cyber Analyst
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