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February 1, 2021

Explore AI Email Security Approaches with Darktrace

Stay informed on the latest AI approaches to email security. Explore Darktrace's comparisons to find the best solution for your cybersecurity needs!
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Dan Fein
VP, Product
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01
Feb 2021

Innovations in artificial intelligence (AI) have fundamentally changed the email security landscape in recent years, but it can often be hard to determine what makes one system different to the next. In reality, under that umbrella term there exists a significant distinction in approach which may determine whether the technology provides genuine protection or simply a perceived notion of defense.

One backward-looking approach involves feeding a machine thousands of emails that have already been deemed to be malicious, and training it to look for patterns in these emails in order to spot future attacks. The second approach uses an AI system to analyze the entirety of an organization’s real-world data, enabling it to establish a notion of what is ‘normal’ and then spot subtle deviations indicative of an attack.

In the below, we compare the relative merits of each approach, with special consideration to novel attacks that leverage the latest news headlines to bypass machine learning systems trained on data sets. Training a machine on previously identified ‘known bads’ is only advantageous in certain, specific contexts that don’t change over time: to recognize the intent behind an email, for example. However, an effective email security solution must also incorporate a self-learning approach that understands ‘normal’ in the context of an organization in order to identify unusual and anomalous emails and catch even the novel attacks.

Signatures – a backward-looking approach

Over the past few decades, cyber security technologies have looked to mitigate risk by preventing previously seen attacks from occurring again. In the early days, when the lifespan of a given strain of malware or the infrastructure of an attack was in the range of months and years, this method was satisfactory. But the approach inevitably results in playing catch-up with malicious actors: it always looks to the past to guide detection for the future. With decreasing lifetimes of attacks, where a domain could be used in a single email and never seen again, this historic-looking signature-based approach is now being widely replaced by more intelligent systems.

Training a machine on ‘bad’ emails

The first AI approach we often see in the wild involves harnessing an extremely large data set with thousands or millions of emails. Once these emails have come through, an AI is trained to look for common patterns in malicious emails. The system then updates its models, rules set, and blacklists based on that data.

This method certainly represents an improvement to traditional rules and signatures, but it does not escape the fact that it is still reactive, and unable to stop new attack infrastructure and new types of email attacks. It is simply automating that flawed, traditional approach – only instead of having a human update the rules and signatures, a machine is updating them instead.

Relying on this approach alone has one basic but critical flaw: it does not enable you to stop new types of attacks that it has never seen before. It accepts that there has to be a ‘patient zero’ – or first victim – in order to succeed.

The industry is beginning to acknowledge the challenges with this approach, and huge amounts of resources – both automated systems and security researchers – are being thrown into minimizing its limitations. This includes leveraging a technique called “data augmentation” that involves taking a malicious email that slipped through and generating many “training samples” using open-source text augmentation libraries to create “similar” emails – so that the machine learns not only the missed phish as ‘bad’, but several others like it – enabling it to detect future attacks that use similar wording, and fall into the same category.

But spending all this time and effort into trying to fix an unsolvable problem is like putting all your eggs in the wrong basket. Why try and fix a flawed system rather than change the game altogether? To spell out the limitations of this approach, let us look at a situation where the nature of the attack is entirely new.

The rise of ‘fearware’

When the global pandemic hit, and governments began enforcing travel bans and imposing stringent restrictions, there was undoubtedly a collective sense of fear and uncertainty. As explained previously in this blog, cyber-criminals were quick to capitalize on this, taking advantage of people’s desire for information to send out topical emails related to COVID-19 containing malware or credential-grabbing links.

These emails often spoofed the Centers for Disease Control and Prevention (CDC), or later on, as the economic impact of the pandemic began to take hold, the Small Business Administration (SBA). As the global situation shifted, so did attackers’ tactics. And in the process, over 130,000 new domains related to COVID-19 were purchased.

Let’s now consider how the above approach to email security might fare when faced with these new email attacks. The question becomes: how can you train a model to look out for emails containing ‘COVID-19’, when the term hasn’t even been invented yet?

And while COVID-19 is the most salient example of this, the same reasoning follows for every single novel and unexpected news cycle that attackers are leveraging in their phishing emails to evade tools using this approach – and attracting the recipient’s attention as a bonus. Moreover, if an email attack is truly targeted to your organization, it might contain bespoke and tailored news referring to a very specific thing that supervised machine learning systems could never be trained on.

This isn’t to say there’s not a time and a place in email security for looking at past attacks to set yourself up for the future. It just isn’t here.

Spotting intention

Darktrace uses this approach for one specific use which is future-proof and not prone to change over time, to analyze grammar and tone in an email in order to identify intention: asking questions like ‘does this look like an attempt at inducement? Is the sender trying to solicit some sensitive information? Is this extortion?’ By training a system on an extremely large data set collected over a period of time, you can start to understand what, for instance, inducement looks like. This then enables you to easily spot future scenarios of inducement based on a common set of characteristics.

Training a system in this way works because, unlike news cycles and the topics of phishing emails, fundamental patterns in tone and language don’t change over time. An attempt at solicitation is always an attempt at solicitation, and will always bear common characteristics.

For this reason, this approach only plays one small part of a very large engine. It gives an additional indication about the nature of the threat, but is not in itself used to determine anomalous emails.

Detecting the unknown unknowns

In addition to using the above approach to identify intention, Darktrace uses unsupervised machine learning, which starts with extracting and extrapolating thousands of data points from every email. Some of these are taken directly from the email itself, while others are only ascertainable by the above intention-type analysis. Additional insights are also gained from observing emails in the wider context of all available data across email, network and the cloud environment of the organization.

Only after having a now-significantly larger and more comprehensive set of indicators, with a more complete description of that email, can the data be fed into a topic-indifferent machine learning engine to start questioning the data in millions of ways in order to understand if it belongs, given the wider context of the typical ‘pattern of life’ for the organization. Monitoring all emails in conjunction allows the machine to establish things like:

  • Does this person usually receive ZIP files?
  • Does this supplier usually send links to Dropbox?
  • Has this sender ever logged in from China?
  • Do these recipients usually get the same emails together?

The technology identifies patterns across an entire organization and gains a continuously evolving sense of ‘self’ as the organization grows and changes. It is this innate understanding of what is and isn’t ‘normal’ that allows AI to spot the truly ‘unknown unknowns’ instead of just ‘new variations of known bads.’

This type of analysis brings an additional advantage in that it is language and topic agnostic: because it focusses on anomaly detection rather than finding specific patterns that indicate threat, it is effective regardless of whether an organization typically communicates in English, Spanish, Japanese, or any other language.

By layering both of these approaches, you can understand the intention behind an email and understand whether that email belongs given the context of normal communication. And all of this is done without ever making an assumption or having the expectation that you’ve seen this threat before.

Years in the making

It’s well established now that the legacy approach to email security has failed – and this makes it easy to see why existing recommendation engines are being applied to the cyber security space. On first glance, these solutions may be appealing to a security team, but highly targeted, truly unique spear phishing emails easily skirt these systems. They can’t be relied on to stop email threats on the first encounter, as they have a dependency on known attacks with previously seen topics, domains, and payloads.

An effective, layered AI approach takes years of research and development. There is no single mathematical model to solve the problem of determining malicious emails from benign communication. A layered approach accepts that competing mathematical models each have their own strengths and weaknesses. It autonomously determines the relative weight these models should have and weighs them against one another to produce an overall ‘anomaly score’ given as a percentage, indicating exactly how unusual a particular email is in comparison to the organization’s wider email traffic flow.

It is time for email security to well and truly drop the assumption that you can look at threats of the past to predict tomorrow’s attacks. An effective AI cyber security system can identify abnormalities with no reliance on historical attacks, enabling it to catch truly unique novel emails on the first encounter – before they land in the inbox.

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Dan Fein
VP, Product

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November 5, 2025

Tracking a Dragon: Investigating a DragonForce-affiliated ransomware attack with Darktrace

Tracking a Dragon: Investigating a DragonForce-affiliated ransomware attack with Darktrace Default blog imageDefault blog image

What is DragonForce?

DragonForce is a Ransomware-as-a-Service (RaaS) platform that emerged in late 2023, offering broad-scale capabilities and infrastructure to threat actors. Recently, DragonForce has been linked to attacks targeting the UK retail sector, resulting in several high-profile cases [1][2]. Moreover, the group launched an affiliate program offering a revenue share of roughly 20%, significantly lower than commissions reported across other RaaS platforms [3].

This Darktrace case study examines a DragonForce-linked RaaS infection within the manufacturing industry. The earliest signs of compromise were observed during working hours in August 2025, where an infected device started performing network scans and attempted to brute-force administrative credentials. After eight days of inactivity, threat actors returned and multiple devices began encrypting files via the SMB protocol using a DragonForce-associated file extension. Ransom notes referencing the group were also dropped, suggesting the threat actor is claiming affiliation with DragonForce, though this has not been confirmed.

Despite Darktrace’s detection of the attack in its early stages, the customer’s deployment did not have Darktrace’s Autonomous Response capability configured, allowing the threat to progress to data exfiltration and file encryption.

Darktrace's Observations

While the initial access vector was not clearly defined in this case study, it was likely achieved through common methods previously employed out by DragonForce affiliates. These include phishing emails leveraging social engineering tactics, exploitation of public-facing applications with known vulnerabilities, web shells, and/or the abuse of remote management tools.

Darktrace’s analysis identified internal devices performing internal network scanning, brute-forcing credentials, and executing unusual Windows Registry operations. Notably, Windows Registry events involving "Schedule\Taskcache\Tasks" contain subkeys for individual tasks, storing GUIDs that can be used to locate and analyze scheduled tasks. Additionally, Control\WMI\Security holds security descriptors for WMI providers and Event Tracing loggers that use non-default security settings respectively.

Furthermore, Darktrace identified data exfiltration activity over SSH, including connections to an ASN associated with a malicious hosting service geolocated in Russia.

1. Network Scan & Brute Force

Darktrace identified anomalous behavior in late August to early September 2025, originating from a source device engaging in internal network scanning followed by brute-force attempts targeting administrator credential, including “administrator”, “Admin”, “rdpadmin”, “ftpadmin”.

Upon further analysis, one of the HTTP connections seen in this activity revealed the use of the user agent string “OpenVAS-VT”, suggesting that the device was using the OpenVAS vulnerability scanner. Subsequently, additional devices began exhibiting network scanning behavior. During this phase, a file named “delete.me” was deleted by multiple devices using SMB protocol. This file is commonly associated with network scanning and penetration testing tool NetScan.

2. Windows Registry Key Update

Following the scanning phase, Darktrace observed the initial device then performing suspicious Winreg operations. This included the use of the ”BaseRegOpenKey” function across multiple registry paths.

Additional operations such as “BaseRegOpenKey” and “BaseRegQueryValue” were also seen around this time. These operations are typically used to retrieve specific registry key values and allow write operations to registry keys.

The registry keys observed included “SYSTEM\CurrentControlSet\Control\WMI\Security” and “Software\Microsoft\Windows NT\CurrentVersion\Schedule\Taskcache\Tasks”. These keys can be leveraged by malicious actors to update WMI access controls and schedule malicious tasks, respectively, both of which are common techniques for establishing persistence within a compromised system.

3. New Administrator Credential Usage

Darktrace subsequently detected the device using a highly privileged credential, “administrator”, via a successful Kerberos login for the first time. Shortly after, the same credential was used again for a successful SMB session.

These marked the first instances of authentication using the “administrator” credential across the customer’s environment, suggesting potential malicious use of the credential following the earlier brute-force activity.

Darktrace’s detection of administrator credentials being used in Kerberos login events by an infected device.
Figure 1: Darktrace’s detection of administrator credentials being used in Kerberos login events by an infected device.
Darktrace’s detection of administrator credentials being used in SMB sessions by an infected device.
Figure 2: Darktrace’s detection of administrator credentials being used in SMB sessions by an infected device.

4. Data Exfiltration

Prior to ransomware deployment, several infected devices were observed exfiltrating data to the malicious IP 45.135.232[.]229 via SSH connections [7][8]. This was followed by the device downloading data from other internal devices and transferring an unusually large volume of data to the same external endpoint.

The IP address was first seen on the network on September 2, 2025 - the same date as the observed data exfiltration activity preceding ransomware deployment and encryption.

Further analysis revealed that the endpoint was geolocated in Russia and registered to the malicious hosting provider Proton66. Multiple external researchers have reported malicious activity involving the same Proton66 ASN (AS198953 Proton66 OOO) as far back as April 2025. These activities notably included vulnerability scanning, exploitation attempts, and phishing campaigns, which ultimately led to malware [4][5][6].

Data Exfiltration Endpoint details.

  • Endpoint: 45.135.232[.]229
  • ASN: AS198953 Proton66 OOO
  • Transport protocol: TCP
  • Application protocol: SSH
  • Destination port: 22
Darktrace’s summary of the external IP 45.135.232[.]229, first detected on September 2, 2025. The right-hand side showcases model alerts triggered related to this endpoint including multiple data exfiltration related model alerts.
Figure 3: Darktrace’s summary of the external IP 45.135.232[.]229, first detected on September 2, 2025. The right-hand side showcases model alerts triggered related to this endpoint including multiple data exfiltration related model alerts.

Further investigation into the endpoint using open-source intelligence (OSINT) revealed that it led to a Microsoft Internet Information Services (IIS) Manager console webpage. This interface is typically used to configure and manage web servers. However, threat actors have been known to exploit similar setups, using fake certificate warnings to trick users into downloading malware, or deploying malicious IIS modules to steal credentials.

Live screenshot of the destination (45.135.232[.]229), captured via OSINT sources, displaying a Microsoft IIS Manager console webpage.
Figure 4: Live screenshot of the destination (45.135.232[.]229), captured via OSINT sources, displaying a Microsoft IIS Manager console webpage.

5. Ransomware Encryption & Ransom Note

Multiple devices were later observed connecting to internal devices via SMB and performing a range of actions indicative of file encryption. This suspicious activity prompted Darktrace’s Cyber AI Analyst to launch an autonomous investigation, during which it pieced together associated activity and provided concrete timestamps of events for the customer’s visibility.

During this activity, several devices were seen writing a file named “readme.txt” to multiple locations, including network-accessible webroot paths such as inetpub\ and wwwroot\. This “readme.txt” file, later confirmed to be the ransom note, claimed the threat actors were affiliated with DragonForce.

At the same time, devices were seen performing SMB Move, Write and ReadWrite actions involving files with the “.df_win” extension across other internal devices, suggesting that file encryption was actively occurring.

Darktrace’s detection of SMB events (excluding Read events) where the device was seen moving or writing files with the “.df_win” extension.
Figure 5: Darktrace’s detection of SMB events (excluding Read events) where the device was seen moving or writing files with the “.df_win” extension.
Darktrace’s detection of a spike in SMB Write events with the filename “readme.txt” on September 9, indicating the start of file encryption.
Figure 6: Darktrace’s detection of a spike in SMB Write events with the filename “readme.txt” on September 9, indicating the start of file encryption.

Conclusion

The rise of Ransomware-as-a-Service (RaaS) and increased attacker customization is fragmenting tactics, techniques, and procedures (TTPs), making it increasingly difficult for security teams to prepare for and defend against each unique intrusion. RaaS providers like DragonForce further complicate this challenge by enabling a wide range of affiliates, each with varying levels of sophistication [9].

In this instance, Darktrace was able to identify several stages of the attack kill chain, including network scanning, the first-time use of privileged credentials, data exfiltration, and ultimately ransomware encryption. Had the customer enabled Darktrace’s Autonomous Response capability, it would have taken timely action to interrupt the attack in its early stages, preventing the eventual data exfiltration and ransomware detonation.

Credit to Justin Torres, Senior Cyber Analyst, Nathaniel Jones, VP, Security & AI Strategy, FCISO, & Emma Foulger, Global Threat Research Operations Lead.

Edited by Ryan Traill (Analyst Content Lead)

Appendices

References:

1. https://www.infosecurity-magazine.com/news/dragonforce-goup-ms-coop-harrods/

2. https://www.picussecurity.com/resource/blog/dragonforce-ransomware-attacks-retail-giants

3. https://blog.checkpoint.com/security/dragonforce-ransomware-redefining-hybrid-extortion-in-2025/

4. https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/proton66-part-1-mass-scanning-and-exploit-campaigns/

5. https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/proton66-part-2-compromised-wordpress-pages-and-malware-campaigns/

6. https://www.broadcom.com/support/security-center/protection-bulletin/proton66-infrastructure-tied-to-expanding-malware-campaigns-and-c2-operations

7. https://www.virustotal.com/gui/ip-address/45.135.232.229

8. https://spur.us/context/45.135.232.229

9. https://www.group-ib.com/blog/dragonforce-ransomware/

IoC - Type - Description + Confidence

·      45.135.232[.]229 - Endpoint Associated with Data Exfiltration

·      .readme.txt – Ransom Note File Extension

·      .df_win – File Encryption Extension Observed

MITRE ATT&CK Mapping

DragonForce TTPs vs Darktrace Models

Initial Access:

·      Anomalous Connection::Callback on Web Facing Device

Command and Control:

·      Compromise::SSL or HTTP Beacon

·      Compromise::Beacon to Young Endpoint

·      Compromise::Beaconing on Uncommon Port

·      Compromise::Suspicious SSL Activity

·      Anomalous Connection::Devices Beaconing to New Rare IP

·      Compromise::Suspicious HTTP and Anomalous Activity

·      DNS Tunnel with TXT Records

Tooling:

·      Anomalous File::EXE from Rare External Location

·      Anomalous File::Masqueraded File Transfer

·      Anomalous File::Numeric File Download

·      Anomalous File::Script from Rare External Location

·      Anomalous File::Uncommon Microsoft File then Exe

·      Anomalous File::Zip or Gzip from Rare External Location

·      Anomalous File::Uncommon Microsoft File then Exe

·      Anomalous File::Internet Facing System File Download

Reconnaissance:

·      Device::Suspicious SMB Query

·      Device::ICMP Address Scan

·      Anomalous Connection::SMB Enumeration

·      Device::Possible SMB/NTLM Reconnaissance

·      Anomalous Connection::Possible Share Enumeration Activity

·      Device::Possible Active Directory Enumeration

·      Anomalous Connection::Large Volume of LDAP Download

·      Device::Suspicious LDAP Search Operation

Lateral Movement:

·      User::Suspicious Admin SMB Session

·      Anomalous Connection::Unusual Internal Remote Desktop

·      Anomalous Connection::Unusual Long Remote Desktop Session

·      Anomalous Connection::Unusual Admin RDP Session

·      User::New Admin Credentials on Client

·      User::New Admin Credentials on Server

·      Multiple Device Correlations::Spreading New Admin Credentials

·      Anomalous Connection::Powershell to Rare External

·      Device::New PowerShell User Agent

·      Anomalous Active Directory Web Services

·      Compromise::Unusual SVCCTL Activity

Evasion:

·      Unusual Activity::Anomalous SMB Delete Volume

·      Persistence

·      Device::Anomalous ITaskScheduler Activity

·      Device::AT Service Scheduled Task

·      Actions on Objectives

·      Compromise::Ransomware::Suspicious SMB Activity (EM)

·      Anomalous Connection::Sustained MIME Type Conversion

·      Compromise::Ransomware::SMB Reads then Writes with Additional Extensions

·      Compromise::Ransomware::Possible Ransom Note Write

·      Data Sent to Rare Domain

·      Uncommon 1 GiB Outbound

·      Enhanced Unusual External Data Transfer

Darktrace Cyber AI Analyst Coverage/Investigation Events:

·      Web Application Vulnerability Scanning of Multiple Devices

·      Port Scanning

·      Large Volume of SMB Login Failures

·      Unusual RDP Connections

·      Widespread Web Application Vulnerability Scanning

·      Unusual SSH Connections

·      Unusual Repeated Connections

·      Possible Application Layer Reconnaissance Activity

·      Unusual Administrative Connections

·      Suspicious Remote WMI Activity

·      Extensive Unusual Administrative Connections

·      Suspicious Directory Replication Service Activity

·      Scanning of Multiple Devices

·      Unusual External Data Transfer

·      SMB Write of Suspicious File

·      Suspicious Remote Service Control Activity

·      Access of Probable Unencrypted Password Files

·      Internal Download and External Upload

·      Possible Encryption of Files over SMB

·      SMB Writes of Suspicious Files to Multiple Devices

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content.

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Justin Torres
Cyber Analyst

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November 5, 2025

WSUS Exploited: Darktrace’s Analysis of Post-Exploitation Activities Related to CVE-2025-59287

WSUS Exploited: Darktrace’s Analysis of Post-Exploitation Activities Related to CVE-2025-59287Default blog imageDefault blog image

Introduction

On October 14, 2025, Microsoft disclosed a new critical vulnerability affecting the Windows Server Update Service (WSUS), CVE-2025-59287.  Exploitation of the vulnerability could allow an unauthenticated attacker to remotely execute code [1][6].

WSUS allows for centralized distribution of Microsoft product updates [3]; a server running WSUS is likely to have significant privileges within a network making it a valuable target for threat actors. While WSUS servers are not necessarily expected to be open to the internet, open-source intelligence (OSINT) has reported  thousands of publicly exposed instances that may be vulnerable to exploitation [2].

Microsoft’s initial ‘Patch Tuesday’ update for this vulnerability did not fully mitigate the risk, and so an out-of-band update followed on October 23 [4][5] . Widespread exploitation of this vulnerability started to be observed shortly after the security update [6], prompting CISA to add CVE-2025-59287 to its Known Exploited Vulnerability Catalog (KEV) on October 24 [7].

Attack Overview

The Darktrace Threat Research team have recently identified multiple potential cases of CVE-2025-59287 exploitation, with two detailed here. While the likely initial access method is consistent across the cases, the follow-up activities differed, demonstrating the variety in which such a CVE can be exploited to fulfil each attacker’s specific goals.

The first signs of suspicious activity across both customers were detected by Darktrace on October 24, the same day this vulnerability was added to CISA’s KEV. Both cases discussed here involve customers based in the United States.

Case Study 1

The first case, involving a customer in the Information and Communication sector, began with an internet-facing device making an outbound connection to the hostname webhook[.]site. Observed network traffic indicates the device was a WSUS server.

OSINT has reported abuse of the workers[.]dev service in exploitation of CVE-2025-59287, where enumerated network information gathered through running a script on the compromised device was exfiltrated using this service [8].

In this case, the majority of connectivity seen to webhook[.]site involved a PowerShell user agent; however, cURL user agents were also seen with some connections taking the form of HTTP POSTs. This connectivity appears to align closely with OSINT reports of CVE-2025-59287 post-exploitation behaviour [8][9].

Connections to webhook[.]site continued until October 26. A single URI was seen consistently until October 25, after which the connections used a second URI with a similar format.

Later on October 26, an escalation in command-and-control (C2) communication appears to have occurred, with the device starting to make repeated connections to two rare workers[.]dev subdomains (royal-boat-bf05.qgtxtebl.workers[.]dev & chat.hcqhajfv.workers[.]dev), consistent with C2 beaconing. While workers[.]dev is associated with the legitimate Cloudflare Workers service, the service is commonly abused by malicious actors for C2 infrastructure. The unusual connections to both webhook[.]site and workers[.]dev triggered multiple alerts in Darktrace, including high-fidelity Enhanced Monitoring alerts and Autonomous Response actions.

Infrastructure insight

Hosted on royal-boat-bf05.qgtxtebl.workers[.]dev is a Microsoft Installer file (MSI) named v3.msi.

Screenshot of v3.msi content.
Figure 1: Screenshot of v3.msi content.

Contained in the MSI file is two Cabinet files named “Sample.cab” and “part2.cab”. After extracting the contents of the cab files, a file named “Config” and a binary named “ServiceEXE”. ServiceEXE is the legitimate DFIR tool Velociraptor, and “Config” contains the configuration details, which include chat.hcqhajfv.workers[.]dev as the server_url, suggesting that Velociraptor is being used as a tunnel to the C2. Additionally, the configuration points to version 0.73.4, a version of Velociraptor that is vulnerable to CVE-2025-6264, a privilege escalation vulnerability.

 Screenshot of Config file.
Figure 2: Screenshot of Config file.

Velociraptor, a legitimate security tool maintained by Rapid7, has been used recently in malicious campaigns. A vulnerable version of tool has been used by threat actors for command execution and endpoint takeover, while other campaigns have used Velociraptor to create a tunnel to the C2, similar to what was observed in this case [10] .

The workers[.]dev communication continued into the early hours of October 27. The most recent suspicious behavior observed on the device involved an outbound connection to a new IP for the network - 185.69.24[.]18/singapure - potentially indicating payload retrieval.

The payload retrieved from “/singapure” is a UPX packed Windows binary. After unpacking the binary, it is an open-source Golang stealer named “Skuld Stealer”. Skuld Stealer has the capabilities to steal crypto wallets, files, system information, browser data and tokens. Additionally, it contains anti-debugging and anti-VM logic, along with a UAC bypass [11].

A timeline outlining suspicious activity on the device alerted by Darktrace.
Figure 3: A timeline outlining suspicious activity on the device alerted by Darktrace.

Case Study 2

The second case involved a customer within the Education sector. The affected device was also internet-facing, with network traffic indicating it was a WSUS server

Suspicious activity in this case once again began on October 24, notably only a few seconds after initial signs of compromise were observed in the first case. Initial anomalous behaviour also closely aligned, with outbound PowerShell connections to webhook[.]site, and then later connections, including HTTP POSTs, to the same endpoint with a cURL user agent.

While Darktrace did not observe any anomalous network activity on the device after October 24, the customer’s security integration resulted in an additional alert on October 27 for malicious activity, suggesting that the compromise may have continued locally.

By leveraging Darktrace’s security integrations, customers can investigate activity across different sources in a seamless manner, gaining additional insight and context to an attack.

A timeline outlining suspicious activity on the device alerted by Darktrace.
Figure 4: A timeline outlining suspicious activity on the device alerted by Darktrace.

Conclusion

Exploitation of a CVE can lead to a wide range of outcomes. In some cases, it may be limited to just a single device with a focused objective, such as exfiltration of sensitive data. In others, it could lead to lateral movement and a full network compromise, including ransomware deployment. As the threat of internet-facing exploitation continues to grow, security teams must be prepared to defend against such a possibility, regardless of the attack type or scale.

By focussing on detection of anomalous behaviour rather than relying on signatures associated with a specific CVE exploit, Darktrace is able to alert on post-exploitation activity regardless of the kind of behaviour seen. In addition, leveraging security integrations provides further context on activities beyond the visibility of Darktrace / NETWORKTM, enabling defenders to investigate and respond to attacks more effectively.

With adversaries weaponizing even trusted incident response tools, maintaining broad visibility and rapid response capabilities becomes critical to mitigating post-exploitation risk.

Credit to Emma Foulger (Global Threat Research Operations Lead), Tara Gould (Threat Research Lead), Eugene Chua (Principal Cyber Analyst & Analyst Team Lead), Nathaniel Jones (VP, Security & AI Strategy, Field CISO),

Edited by Ryan Traill (Analyst Content Lead)

Appendices

References

1.        https://nvd.nist.gov/vuln/detail/CVE-2025-59287

2.    https://www.bleepingcomputer.com/news/security/hackers-now-exploiting-critical-windows-server-wsus-flaw-in-attacks/

3.    https://learn.microsoft.com/en-us/windows-server/administration/windows-server-update-services/get-started/windows-server-update-services-wsus

4.    https://www.cisa.gov/news-events/alerts/2025/10/24/microsoft-releases-out-band-security-update-mitigate-windows-server-update-service-vulnerability-cve

5.    https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-59287

6.    https://thehackernews.com/2025/10/microsoft-issues-emergency-patch-for.html

7.    https://www.cisa.gov/known-exploited-vulnerabilities-catalog

8.    https://www.huntress.com/blog/exploitation-of-windows-server-update-services-remote-code-execution-vulnerability

9.    https://unit42.paloaltonetworks.com/microsoft-cve-2025-59287/

10. https://blog.talosintelligence.com/velociraptor-leveraged-in-ransomware-attacks/

11. https://github.com/hackirby/skuld

Darktrace Model Detections

·       Device / New PowerShell User Agent

·       Anomalous Connection / Powershell to Rare External

·       Compromise / Possible Tunnelling to Bin Services

·       Compromise / High Priority Tunnelling to Bin Services

·       Anomalous Server Activity / New User Agent from Internet Facing System

·       Device / New User Agent

·       Device / Internet Facing Device with High Priority Alert

·       Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

·       Anomalous Server Activity / Rare External from Server

·       Compromise / Agent Beacon (Long Period)

·       Device / Large Number of Model Alerts

·       Compromise / Agent Beacon (Medium Period)

·       Device / Long Agent Connection to New Endpoint

·       Compromise / Slow Beaconing Activity To External Rare

·       Security Integration / Low Severity Integration Detection

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

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

·       Antigena / Network / External Threat / Antigena Suspicious Activity Block

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

List of Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

o   royal-boat-bf05.qgtxtebl.workers[.]dev – Hostname – Likely C2 Infrastructure

o   royal-boat-bf05.qgtxtebl.workers[.]dev/v3.msi - URI – Likely payload

o   chat.hcqhajfv.workers[.]dev – Hostname – Possible C2 Infrastructure

o   185.69.24[.]18 – IP address – Possible C2 Infrastructure

o   185.69.24[.]18/bin.msi - URI – Likely payload

o   185.69.24[.]18/singapure - URI – Likely payload

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About the author
Emma Foulger
Global Threat Research Operations Lead
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