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Revealing Outlaw's Returning Features & New Tactics

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27
Jul 2023
27
Jul 2023
Darktrace's investigation of the latest Outlaw crypto-mining operation, covering the resurgence of old tactics along with the emergence of new ones.

What is Outlaw Cryptocurrency Mining Operation?

The cybersecurity community has been aware of the threat of Outlaw cryptocurrency mining operation, and its affiliated activities since as early as 2018. Despite its prominence, Outlaw remains largely elusive to researchers and analysts due to its ability to adapt its tactics, procedures, and payloads.

Outlaw gained notoriety in 2018 as security researchers began observing the creation of affiliated botnets.[1][2]  Researchers gave Outlaw  its name based on the English translation of the “Haiduc” tool observed during their initial activity on compromised devices.[3],[4] By 2019, much of the initial Outlaw activity  focused on the targeting of Internet of Things (IoT) devices and other internet facing servers, reportedly focusing operations in China and on Chinese devices.[5],[6]  From the outset, mining operations featured as a core element of botnets created by the group.[7] This initial focus may have been a sign of caution by threat actors or a preliminary means of testing procedures and operation efficacy. Regardless, Outlaw actors inevitably expanded scope, targeting larger organizations and a wider range of internet facing devices across geographic scope.

Following a short period of inactivity, security researchers began to observe new Outlaw activity, showcasing additional capabilities such as the ability to kill existing crypto-mining processes on devices, thereby reclaiming devices already compromised by crypto-jacking. [8],[9]

Latest News on Outlaw

Although the more recently observed incidents of Outlaw did demonstrate some new tactics, many of its procedures remained the same, including its unique bundling of payloads that combine crypto-mining and botnet capabilities. [10] In conjunction, the continued use of mining-specific payloads and growth of affiliated botnets has bolstered the belief that Outlaw actors historically prioritizes financial gain, in lieu of overt political objectives.

Given the tendency for malicious actors to share tools and capabilities, true attribution of threat or threat group is extremely difficult in the wild. As such, a genuine survey of activity from the group across a customer base has not always been possible. Therefore, we will present an updated look into more recent activity associated with Outlaw detected across the Darktrace customer base.  

Darktrace vs Outlaw

Since late 2022, Darktrace has observed a rise in probable cyber incidents involving indicators of compromise (IoCs) associated with Outlaw. Given its continued prevalence and relative dearth of information, it is essential to take a renewed look at the latest campaign activity associated with threats like Outlaw to avoid making erroneous assumptions and to ensure the threat posed is correctly characterized.

While being aware of previous IoCs and tactics known to be employed in previous campaigns will go some way to protecting against future Outlaw attacks, it is paramount for organizations to arm themselves with an autonomous intelligent decision maker that can identify malicious activity, based on recognizing deviations from expected patterns of behavior, and take preventative action to effectively defend against such a versatile threat.

Darktrace’s anomaly-based approach to threat detection means it is uniquely positioned to detect novel campaign activity by recognizing subtle deviations in affected devices’ behavior that would have gone unnoticed by traditional security tools relying on rules, signatures and known IoCs.

Outlaw Attack Overview & Darktrace Coverage

From late 2022 through early 2023, Darktrace identified multiple cyber events involving IP addresses, domains, and payloads associated with Outlaw on customer networks. In this recent re-emergence of campaign activity, Darktrace identified numerous attack vectors and IoCs that had previously been associated with Outlaw, however it also observed significant deviations from previous campaigns.

Returning Features

As outlined in a previous blog, past iterations of Outlaw compromises include four identified, distinct phases:

1. Targeting of internet facing devices via SSH brute-forcing

2. Initiation of crypto-mining operations

3. Download of shell script and/or botnet malware payloads

4. Outgoing external SSH scanning to propagate the botnet

Nearly all affected devices analyzed by Darktrace were tagged as internet facing, as identified in previous campaigns, supporting the notion that Outlaw continues to focus on easily exposed devices. In addition to this, Darktrace observed three other core returning features from previous Outlaw campaigns in affected devices between late 2022 and early 2023:

1. Gzip and/or Script Download

2. Beaconing Activity (Command and Control)

3. Crypto-mining

Gzip and/or Script Download

Darktrace observed numerous devices downloading the Dota malware, a strain that is previously known to have been associated with the Outlaw botnet, as either a gzip file or a shell script from rare external hosts.

In some examples, IP addresses that provided the payload were flagged by open-source intelligence (OSINT) sources as having engaged in widespread SSH brute-forcing activities. While the timing of the payload transfer to the device was not consistent, download of gzip files featured prominently during directly observed or potentially affiliated activity. Moreover, Darktrace detected multiple devices performing HTTP requests for shell scripts (.sh) according to detected connection URIs. Darktrace DETECT was able to identify these anomalous connections due to the rarity of the endpoint, payloads, and connectivity for the devices.

Figure 1: Darktrace Cyber AI Analyst technical details summary from an incident during the analysis timeframe that highlights a breach device retrieving the anomalous shell scripts using wget.

Beaconing Activity – Command and Control (C2) Endpoint

Across all Outlaw activity identified by Darktrace, devices engaged in some form of beaconing behavior, rather than one-off connections to IPs associated with Outlaw. While the use of application protocol was not uniform, repeated connectivity to rare external IP addresses related to Outlaw occurred across many analyzed incidents. Darktrace’s Self-Learning AI understood that this beaconing activity represented devices deviating from their expected patterns of life and was able to bring it to the immediate attention of customer security teams.

Figure 2: Model breach log details showing sustained, repeated connectivity to Outlaw affiliated endpoint over port 443, indicating potential C2 activity.

Crypto-mining

In almost every incident of Outlaw identified across the fleet, Darktrace detected some form of cryptocurrency mining activity. Devices affected by Outlaw were consistently observed making anomalous connections to external endpoints associated with crypto-mining operations. Furthermore, the Minergate protocol appeared consistently across hosts; even when devices did not make direct crypto-mining commands, such hosts attempted connections to external entities that were known to support crypto-mining operations.

Figure 3: Advanced Search results showing a sudden spike in mining activity from a device observed connecting to Outlaw-affiliated IP addresses. Such crypto-mining activity was observed consistently across analyzed incidents.

Is Outlaw Using New Tactics?

While in the past, Outlaw activity was identified through a systematic kill chain, recent investigations conducted by Darktrace show significant deviations from this.

For instance, affected devices do not necessarily follow the previously outlined kill chain directly as they did previously. Instead, Darktrace observed affected devices exhibiting these phases in differing orders, repeating steps, or missing out attack phases entirely.

It is essential to study such variation in the kill chain to learn more about the threat of Outlaw and how threat actors are continuing to use it is varying ways. These discrepancies in kill chain elements are likely impacted by visibility into the networks and devices of Darktrace customers, with some relevant activity falling outside of Darktrace’s purview. This is particularly true for internet-exposed devices and hosts that repeatedly performed the same anomalous activity (such as making Minergate requests). Moreover, some devices involved in Outlaw activity may have already been compromised prior to Darktrace’s visibility into the network. As such, these conclusions must be evaluated with a degree of uncertainty.

SSH Activity

Although external SSH connectivity was apparent in some of the incidents detected by Darktrace, it was not directly related to brute-forcing activity. Affected devices did receive anomalous incoming SSH connections, however, wide ranging SSH failed connectivity following the initiation of mining operations by compromised devices was not readily apparent across analyzed compromises. Connections over port 22 were more frequently associated with beaconing and/or C2 activity to endpoints associated with Outlaw, than with potential brute-forcing. As such, Darktrace could not, with high confidence correlate such SSH activity to brute-forcing. This could suggest that threat actors are now portioning or rotation of botnet devices for different operations, for example dividing between botnet expansion and mining operations.

Command line tools

In cases of Outlaw investigated by Darktrace, there was also a degree of variability involving the tools used to retrieve payloads. On the networks of customers affected by Outlaw, Darktrace DETECT identified the use of user agents and command line tools that it considered to be out of character for the network and its devices.

When retrieving the Dota malware payload or shell script data, compromised devices frequently relied on numerous versions of wget and curl user agents. Although the use of such tools as a tactic cannot be definitively linked to the crypto-mining campaign, the employment of varying and/or outdated native command line tools attests to the procedural flexibility of Outlaw campaigns, and its potential for continued evolution.

Figure 4: Breach log data showing use of curl and wget tools to connect to IP addresses associated with Outlaw.

Outlaw in 2023

Given Outlaw’s widespread notoriety and its continued activities, it is likely to remain a prominent threat to organizations and security teams across the threat landscape in 2023 and beyond.

As Darktrace has observed within its customer base from late 2022 through early 2023, activity linked with the Outlaw cryptocurrency mining campaign continues to transpire, offering security teams and research a renewed look at how it has evolved and adapted over the years. While many of its features and tactics appear to have remained consistent, Darktrace has identified numerous signs of Outlaw deviating from its previously known activities.

While relying on previously established IoCs and known tactics from previous campaigns will go some way to protecting an organization’s network from Outlaw compromises, there is a greater need than ever to go further than this. Rather than depending on a list of known-bads or traditional signatures and rules, Darktrace’s anomaly-based approach to threat detection and unparallel autonomous response capabilities mean it is uniquely positioned to DETECT and RESPOND to Outlaw activity, regardless of how it evolves in the future.

Credit to: Adam Potter, Cyber Analyst, Nahisha Nobregas, SOC Analyst, and Ryan Traill, Threat Content Lead

Relevant DETECT Model Breaches:

Compliance / Incoming SSH  

Device / New User Agent and New IP

Device / New User Agent  

Anomalous Connection / New User Agent to IP Without Hostname  

Compromise / Crypto Currency Mining Activity  

Anomalous File / Internet Facing System File Download  

Anomalous Server Activity / New User Agent from Internet Facing System  

Anomalous File / Zip or Gzip from Rare External Location  

Anomalous File / Script from Rare External Location  

Anomalous Connection / Multiple Failed Connections to Rare Endpoint  

Compromise / Large Number of Suspicious Failed Connections  

Anomalous Server Activity / Outgoing from Server  

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Indicators of Compromise

Indicator - Type - Description

/dota3.tar.gz​

File  URI​

Outlaw  payload​

/tddwrt7s.sh​

File  URI​

Outlaw  payload​

73e5dbafa25946ed636e68d1733281e63332441d​

SHA1  Hash​

Outlaw  payload​

debian-package[.]center​

Hostname​

Outlaw  C2 endpoint​

161.35.236[.]24​

IP  address​

Outlaw  C2 endpoint​

138.68.115[.]96​

IP  address​

Outlaw C2  endpoint​

67.205.134[.]224​

IP  address​

Outlaw C2  endpoint​

138.197.212[.]204​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]59 ​

IP  address​

Possible  Outlaw C2 endpoint​

45.9.148[.]117​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]125​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]129​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]99 ​

IP  address​

Outlaw C2  endpoint​

45.9.148[.]234​

IP  address​

Possible  Outlaw C2 endpoint​

45.9.148[.]236​

IP  address​

Possible  Outlaw C2 endpoint​

159.203.102[.]122​

IP  address​

Outlaw C2  endpoint​

159.203.85[.]196​

IP  address​

Outlaw C2  endpoint​

159.223.235[.]198​

IP  address​

Outlaw C2  endpoint​

MITRE ATT&CK Mapping

Tactic -Technique

Initial Access -T1190  Exploit - Public Facing Application

Command and Control - T1071 - Application - Layer Protocol

T1071.001 - Application Layer Protocol: Web Protocols

Impact - T1496 Resource Hijacking

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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Adam Potter
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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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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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