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3CX Supply Chain Compromise: How Darktrace Uncovered A “Smooth Operator”

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19
Jun 2023
19
Jun 2023
This blog discusses how Darktrace detected examples of the 3CX supply chain compromise, the first known cascading supply chain compromise. Leveraging integrations with security vendors like CrowdStrike and SentinelOne, Darktrace was able to successfully identify and prevent multiple cases of the 3CX supply chain compromise across its customer base.

Ever since the discovery of the SolarWinds hack that affected tens of thousands of organizations around the world in 2020, supply chain compromises have remained at the forefront of the minds of security teams and continue to pose a significant threat to their business operations. 

Supply chain compromises can have far-reaching implications, from disrupting an organization’s daily operations, incurring huge financial and reputational damage, to affecting the critical infrastructure of entire countries. As such, it is essential for organizations to have effective security measures in place able to identify and halt these attacks at the earliest possible stage.

In March 2023 the 3CX Desktop application became the latest victim of a supply chain compromise dubbed as the “SmoothOperator” by SentinelOne. This application is used by over 600,000 companies worldwide and the customer list contains high-profile customers across a variety of industries [2]. The 3CX Desktop application is a Voice over Internet Protocol (VoIP) communication software for enterprises that allows for chats, video calls, and voice calls. [3] The 3CX installers for both Windows and macOS systems were affected by information stealing malware. Researchers were able to discern that threat actors also known as UNC 4736 related to financially motivated North Korean operators also known as AppleJeus were responsible for the supply chain compromise.  Researchers have also linked it to another supply chain compromise that occurred prior on the Trading Technologies X_TRADER platform, making this the first known cascading software supply chain compromise used to distribute malware on a wide scale and still be able to align operator interests. [3] Customer reports following the compromise began to surface about the 3CX software being picked up as malicious by several cybersecurity vendors such as CrowdStrike, SentinelOne, and Palo Alto Networks. [6] 

By leveraging integrations with other security vendors like CrowdStrike and SentinelOne, Darktrace DETECT™ was able to identify activity from the “SmoothOperator” across the customer base at multiple stages of the kill chain in March 2023. Darktrace RESPOND™ was then able to autonomously intervene against these emerging threats, preventing significant disruption to customer networks. 

Background on the first known cascading supply chain attack 

Initial Access

In April 2023, security researchers identified the initial target in this story was not the 3CX desktop application, rather, it was another software application called X_TRADER by Trading Technologies. [3] Trading Technologies is a provider that offers high-performance financial trading packages, allowing financial professionals to analyze and trade assets within the stock market more efficiently. Unfortunately, a compromise already existed in the supply chain for this organization. The X_TRADER installer, which had been retired in 2020, still had its code signing certificate set to expire in October 2022. This code signing certificate was exploited by attackers to digitally sign the malicious software. [3] It also inopportunely led to 3CX when an employee unknowingly downloaded a trojanized installer for the X_TRADER software from Trading Technologies prior to the certificate’s expiration. [4]. This compromise of 3CX via X_TRADER was the first case of a cascading supply chain attack reported on within the wider threat landscape. 

Persistence and Privilege Escalation 

Following these findings, researchers were able to identify the likely kill chain that occurred on Windows systems, beginning with the download of the 3CX DesktopApp installer that executed an executable (.exe) file before dropping two trojanized Data Link Libraries (DLLs) alongside a benign executable that was used to sideload malicious DLLs. These DLLs contained and used SIGFLIP and DAVESHELL; both publicly available projects. [3] In this case, the DLLs were used to decrypt using an RC4 key and load a payload into the memory of a compromised system. [3] SIGFLIP and DAVESHELL also extract and decrypt the modular backdoor named VEILEDSIGNAL, which also contains a command and control (C2) configuration. This malware allowed the North Korean threat operators to gain administrative control to the 3CX employee’s device. [3] This was followed by access to the employee’s corporate credentials, ultimately leading to access to 3CX systems. [4] 

Lateral Movement and C2 activity

Security researchers were also able to identify other malware families that were mainly utilized in the supply chain attack to move laterally within the 3CX environment, and allow for C2 communication [3], these malware families are detailed below:

  • TaxHaul: when executed it decrypts shellcode payload, observed by Mandiant to persist via DLL search-order hijacking.
  • Coldcat: complex downloader, which also beacons to a C2 infrastructure.
  • PoolRat: collects system information and executes commands. This is the malware that was found to affect macOS systems.
  • IconicStealer: served as a third stage payload on 3CX systems to steal data or information.

Furthermore, it was also reported early on by Kaspersky that a backdoor named Gopuram, routinely used by the North Korean threat actors Lazarus and typically used against cryptocurrency companies, was also used as a second stage payload on a limited number of 3CX’s customers compromised systems. [5]

3CX detections observed by Darktrace

CrowdStrike and SentinelOne, two of the major detection platforms with which Darktrace partners through security integrations, initially revealed that their platforms had identified the campaign appeared to be targeting 3CXDesktopApp customers in March 2023. 

At this time, Darktrace was also observing this activity and alerting customers to unusual behavior on their networks. [1][7] Darktrace DETECT identified activity related to the supply chain compromise primarily through host-level alerts associated with CrowdStrike and SentinelOne integrations, as well as model breaches related to lateral movement and C2 activity. 

Some of the activity related to the 3CX supply chain compromise that Darktrace detected was observed solely via integration models picking up executable and Microsoft Software Installer (msi) file downloads for the 3CXDesktopApp, suggesting the compromise likely was stopped at the endpoint device. 

CrowdStrike integration model breach identifying 3CXDesktopApp[.]exe as possible malware
Figure 1: CrowdStrike integration model breach identifying 3CXDesktopApp[.]exe as possible malware on March 30, 2023.
showcases the Model Breach Event Log for the CrowdStrike integration model breach
Figure 2: The above figure, showcases the Model Breach Event Log for the CrowdStrike integration model breach shown in Figure 1.

In another case highlighted in Figure 3 and 4, security platforms were associating 3CX as malicious. The device in these figures was observed downloading a 3CXDesktopApp executable followed by an msi file about an hour later. This pattern of activity correlates with the compromise process that had been on reported, where the “SmoothOperator” malware that affected 3CX systems was able to persist through DLL side-loading of malicious DLL files delivered with benign executable files, making it difficult for traditional security tools to detect. [2][3][7]

The activity in this case was detected by the DETECT integration model, ‘High Severity Integration Malware Detection’ and was later blocked by the Darktrace RESPOND/Network model, ‘Antigena Significant Anomaly from Client Block’ which applied the “Enforce Pattern of Life” action to intercept the malicious download that was taking place. Darktrace RESPOND uses AI to learn every devices normal pattern of life and act autonomously to enforce its normal activity. In this event, RESPOND would not only intercept the malicious download that was taking place on the device, but also not allow the device to significantly deviate from its normal pattern of activity.

The Model Breach Event log for the device displays the moment in which the SentinelOne integration model breached for the 3CXDesktopApp.exe file
Figure 3: The Model Breach Event log for the device displays the moment in which the SentinelOne integration model breached for the 3CXDesktopApp.exe file followed subsequently by the RESPOND model, ‘Antigena Significant Anomaly from Client Block’, on March 29, 2023.
Another ‘High Severity Integration Malware Detection’ breached
Figure 4: Another ‘High Severity Integration Malware Detection’ breached for the same device in Figure 3 approximately one hour later because of the msi file, 3CXDesktopApp-18.12.416.msi, which also led to the Darktrace RESPOND model, ‘Antigena Significant Anomaly from Client Block’, on March 29, 2023.

In a separate case, Darktrace also detected a device performing unusual SMB drive writes for the file ‘3CXDesktopApp-18.10.461.msi’. This breached the DETECT model ‘SMB Drive Write’. This model detects when a device starts writing files to another internal device it does not usually communicate with via the SMB protocol using the admin$ or drive shares.

This Model Breach Event log highlights the moment Darktrace captured the msi application file for the 3CXDesktopApp being transferred internally on this customer’s network
Figure 5: This Model Breach Event log highlights the moment Darktrace captured the msi application file for the 3CXDesktopApp being transferred internally on this customer’s network, this was picked up as new activity for the device on March 28, 2023. 

In a couple of other cases observed by Darktrace, connections detected were made from affected devices to 3CX compromise related endpoints. In Figure 6, the device in question was detected connecting to the endpoint, journalide[.]org. This breached the model, ‘Suspicious Self-Signed SSL’, which looks for connections being made to an endpoint with a self-signed SSL certificate which is designed to look legitimate, as self-signed certificates are often used in malware communication.

Model Breach Event log for connections to the 3CX C2 related endpoint
Figure 6: Model Breach Event log for connections to the 3CX C2 related endpoint, journalide[.]org, these connections breached the model Suspicious Self-Signed SSL on April 24, 2023.

On another Darktrace customer environment, a 3CX C2 endpoint, pbxphonenetwork[.]com, had already been added to the Watched Domains list around the time reports of the 3CX application software being malicious had been reported. The Watched Domains list allows Darktrace to detect if any device on the network makes connections to these domains with more scrutiny and breach a model for further visibility of threats on the network. Activity in this case was detected and subsequently blocked by a Darktrace RESPOND action, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443”, blocking the device from connecting to this 3CX C2 endpoints on the spot (see Figure 7). This activity subsequently breached the RESPOND model, ‘Antigena Watched Domain Block’. 

Figure 7: History log of the Darktrace RESPOND action applied to the device breaching the Darktrace RESPOND model, Antigena Watched Domain Block and applying the action, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443” on March 31, 2023.

Darktrace Coverage 

Utilizing integrations with Darktrace such as those with CrowdStrike and SentinelOne, Darktrace was able to detect and respond to activity identified as malicious 3CX activity by CrowdStrike and SentinelOne as seen in Figures 1, 2, 3, and 4. This activity breached the following Darktrace DETECT models: 

  • Integration / CrowdStrike Alert
  • Security Integration / High Severity Integration Malware Detection

Darktrace was also able to identify lateral movement activity such as in the case illustrated in Figure 5.

  • Compliance / SMB Drive Write

Lastly, C2 beaconing activity from malicious endpoints associated with the 3CX compromise was also detected as seen in Figure 6, this activity breached the following Darktrace DETECT model:

  • Anomalous Connection / Suspicious Self-Signed SSL

For customers with Darktrace RESPOND configured in autonomous response mode, Darktrace RESPOND models also breached to activity related to the 3CX supply chain compromise as seen in Figures 3, 4, and 7. Below are the models that breached and the following autonomous actions that were applied:

  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block, “Enforce pattern of life”
  • Antigena / Network / External Threat / Antigena Watched Domain Block, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443”

Conclusion 

The first known cascading supply chain compromise occurred inopportunely for 3CX but conveniently for UNC 4736 North Korean threat actors. This “SmoothOperator” compromise was detected by endpoint security platforms such as CrowdStrike who was at the cusp of this discovery when it became one of the first platforms to report on malicious activity related to the 3CX DesktopApp supply chain compromise.  

Although still novel at the time and largely without reported indicators of compromise, Darktrace was able to capture and identify activity related to the 3CX compromise across its customer base, as well as respond autonomously to contain it. Darktrace was able to amplify security integrations with CrowdStrike and SentinelOne, and via anomaly-based model breaches, contribute unique insights by highlighting activity in varied parts of the 3CX supply chain compromise kill chain. The “SmoothOperator” supply chain attack proves that the Darktrace suite of products, including DETECT and RESPOND, can not only act autonomously to identify and respond to novel threats, but also work with security integrations to further amplify intervention and prevent cyber disruption on customer networks. 

Credit to Nahisha Nobregas, SOC Analyst and Trent Kessler, SOC Analyst.

Appendices

MITRE ATT&CK Framework

Resource Development

  • T1588 Obtain Capabilities  
  • T1588.004 Digital Certificates
  • T1608 Stage Capabilities  
  • T1608.003 Install Digital Certificate

Initial Access

  • T1190 Exploit Public-Facing Application
  • T1195 Supply Chain Compromise  
  • T1195.002 Compromise Software Supply Chain

Persistence

  • T1574 Hijack Execution Flow
  • T1574.002 DLL Side-Loading

Privilege Escalation

  • T1055 Process Injection
  • T1574 Hijack Execution Flow  
  • T1574.002 DLL Side-Loading

Command and Control

  • T1071 Application Layer Protocol
  • T1071.001 Web Protocols
  • T1071.004 DNS  
  • T1105 Ingress Tool Transfer
  • T1573 Encrypted Channel

List of IOCs

C2 Hostnames

  • journalide[.]org
  • pbxphonenetwork[.]com

Likely C2 IP address

  • 89.45.67[.]160

References

  1. https://www.crowdstrike.com/blog/crowdstrike-detects-and-prevents-active-intrusion-campaign-targeting-3cxdesktopapp-customers/
  2. https://www.bleepingcomputer.com/news/security/3cx-confirms-north-korean-hackers-behind-supply-chain-attack/
  3. https://www.mandiant.com/resources/blog/3cx-software-supply-chain-compromise
  4. https://www.securityweek.com/cascading-supply-chain-attack-3cx-hacked-after-employee-downloaded-trojanized-app/
  5. https://securelist.com/gopuram-backdoor-deployed-through-3cx-supply-chain-attack/109344/
  6. https://www.bleepingcomputer.com/news/security/3cx-hack-caused-by-trading-software-supply-chain-attack/
  7. https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/
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.
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ABOUT ThE AUTHOR
Nahisha Nobregas
SOC Analyst
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Safeguarding Distribution Centers in the Digital Age

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12
Jun 2024

Challenges securing distribution centers

For large retail providers, e-commerce organizations, logistics & supply chain organizations, and other companies who rely on the distribution of goods to consumers cybersecurity efforts are often focused on an immense IT infrastructure. However, there's a critical, often overlooked segment of infrastructure that demands vigilant monitoring and robust protection: distribution centers.

Distribution centers play a critical role in the business operations of supply chains, logistics, and the retail industry. They serve as comprehensive logistics hubs, with many organizations operating multiple centers worldwide to meet consumer needs. Depending on their size and hours of operation, even just one hour of downtime at these centers can result in significant financial losses, ranging from tens to hundreds of thousands of dollars per hour.

Due to the time-sensitive nature and business criticality of distribution centers, there has been a rise in applying modern technologies now including AI applications to enhance efficiency within these facilities. Today’s distribution centers are increasingly connected to Enterprise IT networks, the cloud and the internet to manage every stage of the supply chain. Additionally, it is common for organizations to allow 3rd party access to the distribution center networks and data for reasons including allowing them to scale their operations effectively.

However, this influx of new technologies and interconnected systems across IT, OT and cloud introduces new risks on the cybersecurity front. Distribution center networks include industrial operational technologies ICS/OT, IoT technologies, enterprise network technology, and cloud systems working in coordination. The convergence of these technologies creates a greater chance that blind spots exist for security practitioners and this increasing presence of networked technology increases the attack surface and potential for vulnerability. Thus, having cybersecurity measures that cover IT, OT or Cloud alone is not enough to secure a complex and dynamic distribution center network infrastructure.  

The OT network encompasses various systems, devices, hardware, and software, such as:

  • Enterprise Resource Planning (ERP)
  • Warehouse Execution System (WES)
  • Warehouse Control System (WCS)
  • Warehouse Management System (WMS)
  • Energy Management Systems (EMS)
  • Building Management Systems (BMS)
  • Distribution Control Systems (DCS)
  • Enterprise IT devices
  • OT and IoT: Engineering workstations, ICS application and management servers, PLCs, HMI, access control, cameras, and printers
  • Cloud applications

Distribution centers: An expanding attack surface

As these distribution centers have become increasingly automated, connected, and technologically advanced, their attack surfaces have inherently increased. Distribution centers now have a vastly different potential for cyber risk which includes:  

  • More networked devices present
  • Increased routable connectivity within industrial systems
  • Externally exposed industrial control systems
  • Increased remote access
  • IT/OT enterprise to industrial convergence
  • Cloud connectivity
  • Contractors, vendors, and consultants on site or remoting in  

Given the variety of connected systems, distribution centers are more exposed to external threats than ever before. Simultaneously, distribution center’s business criticality has positioned them as interesting targets to cyber adversaries seeking to cause disruption with significant financial impact.

Increased connectivity requires a unified security approach

When assessing the unique distribution center attack surface, the variety of interconnected systems and devices requires a cybersecurity approach that can cover the diverse technology environment.  

From a monitoring and visibility perspective, siloed IT, OT or cloud security solutions cannot provide the comprehensive asset management, threat detection, risk management, and response and remediation capabilities across interconnected digital infrastructure that a solution natively covering IT, cloud, OT, and IoT can provide.  

The problem with using siloed cybersecurity solutions to cover a distribution center is the visibility gaps that are inherently created when using multiple solutions to try and cover the totality of the diverse infrastructure. What this means is that for cross domain and multi-stage attacks, depending on the initial access point and where the adversary plans on actioning their objectives, multiple stages of the attack may not be detected or correlated if they security solutions lack visibility into OT, IT, IoT and cloud.

Comprehensive security under one solution

Darktrace leverages Self-Learning AI, which takes a new approach to cybersecurity. Instead of relying on rules and signatures, this AI trains on the specific business to learn a ‘pattern of life’ that models normal activity for every device, user, and connection. It can be applied anywhere an organization has data, and so can natively cover IT, OT, IoT, and cloud.  

With these models, Darktrace /OT provides improved visibility, threat detection and response, and risk management for proactive hardening recommendations.  

Visibility: Darktrace is the only OT security solution that natively covers IT, IoT and OT in unison. AI augmented workflows ensure OT cybersecurity analysts and operation engineers can manage IT and OT environments, leveraging a live asset inventory and tailored dashboards to optimize security workflows and minimize operator workload.

Threat detection, investigation, and response: The AI facilitates anomaly detection capable of detecting known, unknown, and insider threats and precise response for OT environments that contains threats at their earliest stages before they can jeopardize control systems. Darktrace immediately understands, identifies, and investigates all anomalous activity in OT networks, whether human or machine driven and uses Explainable AI to generate investigation reports via Darktrace’s Cyber AI Analyst.

Proactive risk identification: Risk management capabilities like attack path modeling can prioritize remediation and mitigation that will most effectively reduce derived risk scores. Rather than relying on knowledge of past attacks and CVE lists and scores, Darktrace AI learns what is ‘normal’ for its environment, discovering previously unknown threats and risks by detecting subtle shifts in behavior and connectivity. Through the application of Darktrace AI for OT environments, security teams can investigate novel attacks, discover blind spots, get live-time visibility across all their physical and digital assets, and reduce the time to detect, respond to, and triage security events.

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About the author
Daniel Simonds
Director of Operational Technology

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Inside the SOC

Medusa Ransomware: Looking Cyber Threats in the Eye with Darktrace

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10
Jun 2024

What is Living off the Land attack?

In the face of increasingly vigilant security teams and adept defense tools, attackers are continually looking for new ways to circumvent network security and gain access to their target environments. One common tactic is the leveraging of readily available utilities and services within a target organization’s environment in order to move through the kill chain; a popular method known as living off the land (LotL). Rather than having to leverage known malicious tools or write their own malware, attackers are able to easily exploit the existing infrastructure of their targets.

The Medusa ransomware group in particular are known to extensively employ LotL tactics, techniques and procedures (TTPs) in their attacks, as one Darktrace customer in the US discovered in early 2024.

What is Medusa Ransomware?

Medusa ransomware (not to be confused with MedusaLocker) was first observed in the wild towards the end of 2022 and has been a popular ransomware strain amongst threat actors since 2023 [1]. Medusa functions as a Ransomware-as-a-Service (RaaS) platform, providing would-be attackers, also know as affiliates, with malicious software and infrastructure required to carry out disruptive ransomware attacks. The ransomware is known to target organizations across many different industries and countries around the world, including healthcare, education, manufacturing and retail, with a particular focus on the US [2].

How does medusa ransomware work?

Medusa affiliates are known to employ a number of TTPs to propagate their malware, most prodominantly gaining initial access by exploiting vulnerable internet-facing assets and targeting valid local and domain accounts that are used for system administration.

The ransomware is typically delivered via phishing and spear phishing campaigns containing malicious attachments [3] [4], but it has also been observed using initial access brokers to access target networks [5]. In terms of the LotL strategies employed in Medusa compromises, affiliates are often observed leveraging legitimate services like the ConnectWise remote monitoring and management (RMM) software and PDQ Deploy, in order to evade the detection of security teams who may be unable to distinguish the activity from normal or expected network traffic [2].

According to researchers, Medusa has a public Telegram channel that is used by threat actors to post any data that may have been stolen, likely in an attempt to extort organizations and demand payment [2].  

Darktrace’s Coverage of Medusa Ransomware

Established Foothold and C2 activity

In March 2024, Darktrace /NETWORK identified over 80 devices, including an internet facing domain controller, on a customer network performing an unusual number of activities that were indicative of an emerging ransomware attack. The suspicious behavior started when devices were observed making HTTP connections to the two unusual endpoints, “wizarr.manate[.]ch” and “go-sw6-02.adventos[.]de”, with the PowerShell and JWrapperDownloader user agents.

Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the connections and was able to connect the seemingly separate events into one wider incident spanning multiple different devices. This allowed the customer to visualize the activity in chronological order and gain a better understanding of the scope of the attack.

At this point, given the nature and rarity of the observed activity, Darktrace /NETWORK's autonomous response would have been expected to take autonomous action against affected devices, blocking them from making external connections to suspicious locations. However, autonomous response was not configured to take autonomous action at the time of the attack, meaning any mitigative actions had to be manually approved by the customer’s security team.

Internal Reconnaissance

Following these extensive HTTP connections, between March 1 and 7, Darktrace detected two devices making internal connection attempts to other devices, suggesting network scanning activity. Furthermore, Darktrace identified one of the devices making a connection with the URI “/nice ports, /Trinity.txt.bak”, indicating the use of the Nmap vulnerability scanning tool. While Nmap is primarily used legitimately by security teams to perform security audits and discover vulnerabilities that require addressing, it can also be leveraged by attackers who seek to exploit this information.

Darktrace / NETWORK model alert showing the URI “/nice ports, /Trinity.txt.bak”, indicating the use of Nmap.
Figure 1: Darktrace /NETWORK model alert showing the URI “/nice ports, /Trinity.txt.bak”, indicating the use of Nmap.

Darktrace observed actors using multiple credentials, including “svc-ndscans”, which was also seen alongside DCE-RPC activity that took place on March 1. Affected devices were also observed making ExecQuery and ExecMethod requests for IWbemServices. ExecQuery is commonly utilized to execute WMI Query Language (WQL) queries that allow the retrieval of information from WI, including system information or hardware details, while ExecMethod can be used by attackers to gather detailed information about a targeted system and its running processes, as well as a tool for lateral movement.

Lateral Movement

A few hours after the first observed scanning activity on March 1, Darktrace identified a chain of administrative connections between multiple devices, including the aforementioned internet-facing server.

Cyber AI Analyst was able to connect these administrative connections and separate them into three distinct ‘hops’, i.e. the number of administrative connections made from device A to device B, including any devices leveraged in between. The AI Analyst investigation was also able to link the previously detailed scanning activity to these administrative connections, identifying that the same device was involved in both cases.

Cyber AI Analyst investigation into the chain of lateral movement activity.
Figure 2: Cyber AI Analyst investigation into the chain of lateral movement activity.

On March 7, the internet exposed server was observed transferring suspicious files over SMB to multiple internal devices. This activity was identified as unusual by Darktrace compared to the device's normal SMB activity, with an unusual number of executable (.exe) and srvsvc files transferred targeting the ADMIN$ and IPC$ shares.

Cyber AI Analyst investigation into the suspicious SMB write activity.
Figure 3: Cyber AI Analyst investigation into the suspicious SMB write activity.
Graph highlighting the number of successful SMB writes and the associated model alerts.
Figure 4: Graph highlighting the number of successful SMB writes and the associated model alerts.

The threat actor was also seen writing SQLite3*.dll files over SMB using a another credential this time. These files likely contained the malicious payload that resulted in the customer’s files being encrypted with the extension “.s3db”.

Darktrace’s visibility over an affected device performing successful SMB writes.
Figure 5: Darktrace’s visibility over an affected device performing successful SMB writes.

Encryption of Files

Finally, Darktrace observed the malicious actor beginning to encrypt and delete files on the customer’s environment. More specifically, the actor was observed using credentials previously seen on the network to encrypt files with the aforementioned “.s3db” extension.

Darktrace’s visibility over the encrypted files.
Figure 6: Darktrace’s visibility over the encrypted files.


After that, Darktrace observed the attacker encrypting  files and appending them with the extension “.MEDUSA” while also dropping a ransom note with the file name “!!!Read_me_Medusa!!!.txt”

Darktrace’s detection of threat actors deleting files with the extension “.MEDUSA”.
Figure 7: Darktrace’s detection of threat actors deleting files with the extension “.MEDUSA”.
Darktrace’s detection of the Medusa ransom note.
Figure 8: Darktrace’s detection of the Medusa ransom note.

At the same time as these events, Darktrace observed the attacker utilizing a number of LotL techniques including SSL connections to “services.pdq[.]tools”, “teamviewer[.]com” and “anydesk[.]com”. While the use of these legitimate services may have bypassed traditional security tools, Darktrace’s anomaly-based approach enabled it to detect the activity and distinguish it from ‘normal’’ network activity. It is highly likely that these SSL connections represented the attacker attempting to exfiltrate sensitive data from the customer’s network, with a view to using it to extort the customer.

Cyber AI Analyst’s detection of “services.pdq[.]tools” usage.
Figure 9: Cyber AI Analyst’s detection of “services.pdq[.]tools” usage.

If this customer had been subscribed to Darktrace's Proactive Threat Notification (PTN) service at the time of the attack, they would have been promptly notified of these suspicious activities by the Darktrace Security Operation Center (SOC). In this way they could have been aware of the suspicious activities taking place in their infrastructure before the escalation of the compromise. Despite this, they were able to receive assistance through the Ask the Expert service (ATE) whereby Darktrace’s expert analyst team was on hand to assist the customer by triaging and investigating the incident further, ensuring the customer was well equipped to remediate.  

As Darktrace /NETWORK's autonomous response was not enabled in autonomous response mode, this ransomware attack was able to progress to the point of encryption and data exfiltration. Had autonomous response been properly configured to take autonomous action, Darktrace would have blocked all connections by affected devices to both internal and external endpoints, as well as enforcing a previously established “pattern of life” on the device to stop it from deviating from its expected behavior.

Conclusion

The threat actors in this Medusa ransomware attack attempted to utilize LotL techniques in order to bypass human security teams and traditional security tools. By exploiting trusted systems and tools, like Nmap and PDQ Deploy, attackers are able to carry out malicious activity under the guise of legitimate network traffic.

Darktrace’s Self-Learning AI, however, allows it to recognize the subtle deviations in a device’s behavior that tend to be indicative of compromise, regardless of whether it appears legitimate or benign on the surface.

Further to the detection of the individual events that made up this ransomware attack, Darktrace’s Cyber AI Analyst was able to correlate the activity and collate it under one wider incident. This allowed the customer to track the compromise and its attack phases from start to finish, ensuring they could obtain a holistic view of their digital environment and remediate effectively.

Credit to Maria Geronikolou, Cyber Analyst, Ryan Traill, Threat Content Lead

Appendices

Darktrace DETECT Model Detections

Anomalous Connection / SMB Enumeration

Device / Anomalous SMB Followed By Multiple Model Alerts

Device / Suspicious SMB Scanning Activity

Device / Attack and Recon Tools

Device / Suspicious File Writes to Multiple Hidden SMB Share

Compromise / Ransomware / Ransom or Offensive Words Written to SMB

Device / Internet Facing Device with High Priority Alert

Device / Network Scan

Anomalous Connection / Powershell to Rare External

Device / New PowerShell User Agent

Possible HTTP Command and Control

Extensive Suspicious DCE-RPC Activity

Possible SSL Command and Control to Multiple Endpoints

Suspicious Remote WMI Activity

Scanning of Multiple Devices

Possible Ransom Note Accessed over SMB

List of Indicators of Compromise (IoCs)

IoC – Type – Description + Confidence

207.188.6[.]17      -     IP address   -      C2 Endpoint

172.64.154[.]227 - IP address -        C2 Endpoint

wizarr.manate[.]ch  - Hostname -       C2 Endpoint

go-sw6-02.adventos[.]de.  Hostname  - C2 Endpoint

.MEDUSA             -        File extension     - Extension to encrypted files

.s3db               -             File extension    -  Created file extension

SQLite3-64.dll    -        File           -               Used tool

!!!Read_me_Medusa!!!.txt - File -   Ransom note

Svc-ndscans         -         Credential     -     Possible compromised credential

Svc-NinjaRMM      -       Credential      -     Possible compromised credential

MITRE ATT&CK Mapping

Discovery  - File and Directory Discovery - T1083

Reconnaissance    -  Scanning IP            -          T1595.001

Reconnaissance -  Vulnerability Scanning -  T1595.002

Lateral Movement -Exploitation of Remote Service -  T1210

Lateral Movement - Exploitation of Remote Service -   T1210

Lateral Movement  -  SMB/Windows Admin Shares     -    T1021.002

Lateral Movement   -  Taint Shared Content          -            T1080

Execution   - PowerShell     - T1059.001

Execution  -   Service Execution   -    T1059.002

Impact   -    Data Encrypted for Impact  -  T1486

References

[1] https://unit42.paloaltonetworks.com/medusa-ransomware-escalation-new-leak-site/

[2] https://thehackernews.com/2024/01/medusa-ransomware-on-rise-from-data.html

[3] https://www.trustwave.com/en-us/resources/blogs/trustwave-blog/unveiling-the-latest-ransomware-threats-targeting-the-casino-and-entertainment-industry/

[4] https://www.sangfor.com/farsight-labs-threat-intelligence/cybersecurity/security-advisory-for-medusa-ransomware

[5] https://thehackernews.com/2024/01/medusa-ransomware-on-rise-from-data.html

[6]https://any.run/report/8be3304fec9d41d44012213ddbb28980d2570edeef3523b909af2f97768a8d85/e4c54c9d-12fd-477f-8cbb-a20f8fb98912

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About the author
Maria Geronikolou
Cyber Analyst
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