Inside the SOC

Legitimate Services, Malicious Intentions: Getting the Drop on Phishing Attacks Abusing Dropbox 

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Mar 2024
Mar 2024
This blog discusses an example of a malicious actor utilizing the cloud storage service Dropbox in order to carry out a phishing attack against a Darktrace customer. Thanks to Darktrace/Email and Apps, this compromise was promptly brought to the attention of the customer and shut down.

Evolving Phishing Attacks

While email has long been the vector of choice for carrying out phishing attacks, threat actors, and their tactics, techniques, and procedures (TTPs), are continually adapting and evolving to keep pace with the emergence of new technologies that represent new avenues to exploit. As previously discussed by the Darktrace analyst team, several novel threats relating to the abuse of commonly used services and platforms were observed throughout 2023, including the rise of QR Code Phishing and the use of Microsoft SharePoint and Teams in phishing campaigns.

Dropbox Phishing Attacks

It should, therefore, come as no surprise that the malicious use of other popular services has gained traction in recent years, including the cloud storage platform Dropbox.

With over 700 million registered users [1], Dropbox has established itself as a leading cloud storage service celebrated for its simplicity in file storage and sharing, but in doing so it has also inadvertently opened a new avenue for threat actors to exploit. By leveraging the legitimate infrastructure of Dropbox, threat actors are able to carry out a range of malicious activities, from convincing their targets to unknowingly download malware to revealing sensitive information like login credentials.

Darktrace Detection of Dropbox Phishing Attack

Darktrace detected a malicious attempt to use Dropbox in a phishing attack in January 2024, when employees of a Darktrace customer received a seemingly innocuous email from a legitimate Dropbox address. Unbeknownst to the employees, however, a malicious link had been embedded in the contents of the email that could have led to a widespread compromise of the customer’s Software-as-a-Service (SaaS) environment. Fortunately for this customer, Darktrace/Email™ quickly identified the suspicious emails and took immediate actions to stop them from being opened. If an email was accessed by an employee, Darktrace/Apps™ was able to recognize any suspicious activity on the customer’s SaaS platform and bring it to the immediate detection of their security team.

Attack overview

Initial infection  

On January 25, 2024, Darktrace/Email observed an internal user on a customer’s SaaS environment receiving an inbound email from ‘no-reply@dropbox[.]com’, a legitimate email address used by the Dropbox file storage service.  Around the same time 15 other employees also received the same email.

The email itself contained a link that would lead a user to a PDF file hosted on Dropbox, that was seemingly named after a partner of the organization. Although the email and the Dropbox endpoint were both legitimate, Darktrace identified that the PDF file contained a suspicious link to a domain that had never previously been seen on the customer’s environment, ‘mmv-security[.]top’.  

Darktrace understood that despite being sent from a legitimate service, the email’s initiator had never previously corresponded with anyone at the organization and therefore treated it with suspicion. This tactic, whereby a legitimate service sends an automated email using a fixed address, such as ‘no-reply@dropbox[.]com’, is often employed by threat actors attempting to convince SaaS users to follow a malicious link.

As there is very little to distinguish between malicious or benign emails from these types of services, they can often evade the detection of traditional email security tools and lead to disruptive account takeovers.

As a result of this detection, Darktrace/Email immediately held the email, stopping it from landing in the employee’s inbox and ensuring the suspicious domain could not be visited. Open-source intelligence (OSINT) sources revealed that this suspicious domain was, in fact, a newly created endpoint that had been reported for links to phishing by multiple security vendors [2].

A few days later on January 29, the user received another legitimate email from ‘no-reply@dropbox[.]com’ that served as a reminder to open the previously shared PDF file. This time, however, Darktrace/Email moved the email to the user’s junk file and applied a lock link action to prevent the user from directly following a potentially malicious link.

Figure 1: Anomaly indicators associated with the suspicious emails sent by ’no.reply@dropbox[.]com’, and the corresponding actions performed by Darktrace/Email.

Unfortunately for the customer in this case, their employee went on to open the suspicious email and follow the link to the PDF file, despite Darktrace having previously locked it.

Figure 2: Confirmation that the SaaS user read the suspicious email and followed the link to the PDF file hosted on Dropbox, despite it being junked and link locked.

Darktrace/Network subsequently identified that the internal device associated with this user connected to the malicious endpoint, ‘mmv-security[.]top’, a couple of days later.

Further investigation into this suspicious domain revealed that it led to a fake Microsoft 365 login page, designed to harvest the credentials of legitimate SaaS account holders. By masquerading as a trusted organization, like Microsoft, these credential harvesters are more likely to appear trustworthy to their targets, and therefore increase the likelihood of stealing privileged SaaS account credentials.  

Figure 3: The fake Microsoft login page that the user was directed to after clicking the link in the PDF file.

Suspicious SaaS activity

In the days following the initial infection, Darktrace/Apps began to observe a string of suspicious SaaS activity being performed by the now compromised Microsoft 365 account.

Beginning on January 31, Darktrace observed a number of suspicious SaaS logins from multiple unusual locations that had never previously accessed the account, including 73.95.165[.]113. Then on February 1, Darktrace detected unusual logins from the endpoints 194.32.120[.]40 and 185.192.70[.]239, both of which were associated with ExpressVPN indicating that threat actors may have been using a virtual private network (VPN) to mask their true location.

FIgure 4: Graph Showing several unusual logins from different locations observed by Darktrace/Apps on the affected SaaS account.

Interestingly, the threat actors observed during these logins appeared to use a valid multi-factor authentication (MFA) token, indicating that they had successfully bypassed the customer’s MFA policy. In this case, it appears likely that the employee had unknowingly provided the attackers with an MFA token or unintentionally approved a login verification request. By using valid tokens and meeting the necessary MFA requirements, threat actors are often able to remain undetected by traditional security tools that view MFA as the silver bullet. However, Darktrace’s anomaly-based approach to threat detection allows it to quickly identify unexpected activity on a device or SaaS account, even if it occurs with legitimate credentials and successfully passed authentication requirements, and bring it to the attention of the customer’s security team.

Shortly after, Darktrace observed an additional login to the SaaS account from another unusual location, 87.117.225[.]155, this time seemingly using the HideMyAss (HMA) VPN service. Following this unusual login, the actor was seen creating a new email rule on the compromised Outlook account. The new rule, named ‘….’, was intended to immediately move any emails from the organization’s accounts team directly to the ‘Conversation History’ mailbox folder. This is a tactic often employed by threat actors during phishing campaigns to ensure that their malicious emails (and potential responses to them) are automatically moved to less commonly visited mailbox folders in order to remain undetected on target networks. Furthermore, by giving this new email rule a generic name, like ‘….’ it is less likely to draw the attention of the legitimate account holder or the organizations security team.

Following this, Darktrace/Email observed the actor sending updated versions of emails that had previously been sent by the legitimate account holder, with subject lines containing language like “Incorrect contract” and “Requires Urgent Review”, likely in an attempt to illicit some kind of follow-up action from the intended recipient.  This likely represented threat actors using the compromised account to send further malicious emails to the organization’s accounts team in order to infect additional accounts across the customer’s SaaS environment.

Unfortunately, Darktrace RESPOND™ was not deployed in the customer’s SaaS environment in this instance, meaning that the aforementioned malicious activity did not lead to any mitigative actions to contain the compromise. Had RESPOND been enabled in autonomous response mode at the time of the attack, it would have quickly moved to log out and disable the suspicious actor as soon as they had logged into the SaaS environment from an unusual location, effectively shutting down this account takeover attempt at the earliest opportunity.

Nevertheless, Darktrace/Email’s swift identification and response to the suspicious phishing emails, coupled with Darktrace/App’s detection of the unusual SaaS activity, allowed the customer’s security team to quickly identify the offending SaaS actor and take the account offline before the attack could escalate further


As organizations across the world continue to adopt third-party solutions like Dropbox into their day-to-day business operations, threat actors will, in turn, continue to seek ways to exploit these and add them to their arsenal. As illustrated in this example, it is relatively simple for attackers to abuse these legitimate services for malicious purposes, all while evading detection by endpoint users and security teams alike.

By leveraging these commonly used platforms, malicious actors are able to carry out disruptive cyber-attacks, like phishing campaigns, by taking advantage of legitimate, and seemingly trustworthy, infrastructure to host malicious files or links, rather than relying on their own infrastructure. While this tactic may bypass traditional security measures, Darktrace’s Self-Learning AI enables it to recognize unusual senders within an organization’s email environment, even if the email itself seems to have come from a legitimate source, and prevent them from landing in the target inbox. In the event that a SaaS account does become compromised, Darktrace is able to identify unusual login locations and suspicious SaaS activities and bring them to the attention of the customer for remediation.

In addition to the prompt identification of emerging threats, Darktrace RESPOND is uniquely placed to take swift autonomous action against any suspicious activity detected within a customer’s SaaS environment, effectively containing any account takeover attempts in the first instance.

Credit to Ryan Traill, Threat Content Lead, Emily Megan Lim, Cyber Security Analyst


Darktrace Model Detections  

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Unusual Activity::Multiple Unusual External Sources For SaaS Credential

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Unusual Activity::Multiple Unusual SaaS Activities

- Model Breach: SaaS / Unusual Activity::Unusual MFA Auth and SaaS Activity

- Model Breach: SaaS / Compromise::Unusual Login and New Email Rule

- Model Breach: SaaS / Compliance::Anomalous New Email Rule

- Model Breach: SaaS / Compliance::New Email Rule

- Model Breach: SaaS / Compromise::SaaS Anomaly Following Anomalous Login

- Model Breach: Device / Suspicious Domain

List of Indicators of Compromise (IoCs)

Domain IoC

mmv-security[.]top’ - Credential Harvesting Endpoint

IP Address

73.95.165[.]113 - Unusual Login Endpoint

194.32.120[.]40 - Unusual Login Endpoint

87.117.225[.]155 - Unusual Login Endpoint



T1078.004 - Cloud Accounts


T1538 - Cloud Service Dashboard


T1586 - Compromise Accounts


T1539 - Steal Web Session Cookie


T1137 - Outlook Rules


T156.002 Spearphishing Link

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.
Ryan Traill
Threat Content Lead
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Safeguarding Distribution Centers in the Digital Age

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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


Inside the SOC

Medusa Ransomware: Looking Cyber Threats in the Eye with Darktrace

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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.


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


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


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








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About the author
Maria Geronikolou
Cyber Analyst
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