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
Max Heinemeyer
Global Field CISO
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26
Apr 2020
Overview
The mind of an experienced and dedicated cyber-criminal works like that of an entrepreneur: the relentless pursuit of profit guides every move they make. At each step of the journey towards their objective, the same questions are asked: how can I minimize my time and resources? How can I mitigate against risk? What measures can I take which will return the best results?
Incorporating this ‘enterprise’ model into the cyber-criminal framework uncovers why attackers are turning to new technology in an attempt to maximize efficiency, and why a report from Forrester earlier this year revealed that 88% of security leaders now consider the nefarious use of AI in cyber activity to be inevitable. Over half of the responders to that same survey foresee AI attacks manifesting themselves to the public in the next twelve months – or think they are already occurring.
AI has already achieved breakthroughs in fields such as healthcare, facial recognition, voice assistance and many others. In the current cat-and-mouse game of cyber security, defenders have started to accept that augmenting their defenses with AI is necessary, with over 3,500 organizations using machine learning to protect their digital environments. But we have to be ready for the moment attackers themselves use open-source AI technology available today to supercharge their attacks.
Enhancing the attack life cycle
To a cyber-criminal ring, the benefits of leveraging AI in their attacks are at least four-fold:
It gives them an understanding of context
It helps to scale up operations
It makes attribution and detection harder
It ultimately increases their profitability
To best demonstrate how each of these factors surface themselves, we can break down the life cycle of a typical data exfiltration attempt, telling the story of how AI can augment the attacker during the campaign at every stage of the attack.
ReconnaissanceCAPTCHA breakerIntrusionShellphish and SNAP_RC2 establishmentFirstOrder and unsupervised clustering algorithmPrivilege escalationCeWL and neural networkLateral movementMITRE CALDERAMission accomplishedYahoo NSFW
Figure 1: The ‘AI toolbox’ attackers use to augment their attacks
Stage 1: Reconnaissance
In seeking to garner trust and make inroads into an organization, automated chatbots would first interact with employees via social media, leveraging profile pictures of non-existent people created by AI instead of re-using actual human photos. Once the chatbots have gained the trust of the victims at the target organization, the human attackers can gain valuable intelligence about its employees, while CAPTCHA-breakers are used for automated reconnaissance on the organization’s public-facing web pages.
Forrester estimates that AI-enabled ‘deep fakes’ will cost businesses a quarter of a billion dollars in losses in 2020.
Stage 2: Intrusion
This intelligence would then be used to craft convincing spear phishing attacks, whilst an adapted version of SNAP_R can be leveraged to create realistic tweets at scale – targeting several key employees. The tweets either trick the user into downloading malicious documents, or contain links to servers which facilitate exploit-kit attacks.
An autonomous vulnerability fuzzing engine based on Shellphish would be constantly crawling the victim’s perimeter – internet-facing servers and websites – and trying to find new vulnerabilities for an initial foothold.
Stage 3: Command and control
A popular hacking framework, Empire, allows attackers to ‘blend in’ with regular business operations, restricting command and control traffic to periods of peak activity. An agent using some form of automated decision-making engine for lateral movement might not even require command and control traffic to move laterally. Eliminating the need for command and control traffic drastically reduces the detection surface of existing malware.
Stage 4: Privilege escalation
At this stage, a password crawler like CeWL could collect target-specific keywords from internal websites and feed those keywords into a pre-trained neural network, essentially creating hundreds of realistic permutations of contextualized passwords at machine-speed. These can be automatically entered in period bursts so as to not alert the security team or trigger resets.
Stage 5: Lateral movement
Moving laterally and harvesting accounts and credentials involves identifying the optimal paths to accomplish the mission and minimize intrusion time. Parts of the attack planning can be accelerated by concepts such as from the CALDERA framework using automated planning AI methods. This would greatly reduce the time required to reach the final destination.
Stage 6: Data exfiltration
It is in this final stage where the role of offensive AI is most apparent. Instead of running a costly post-intrusion analysis operation and sifting through gigabytes of data, the attackers can leverage a neural network that pre-selects only relevant material for exfiltration. This neural network is pre-trained and therefore has a basic understanding of what valuable material constitutes and flags those for immediate exfiltration. The neural network could be based on something like Yahoo’s open-source project for content recognition.
Conclusion
Today’s attacks still require several humans behind the keyboard making guesses about the sorts of methods that will be most effective in their target network – it’s this human element that often allows defenders to neutralize attacks.
Offensive AI will make detecting and responding to attacks far more difficult. Open-source research and projects exist today which can be leveraged to augment every phase of the attack lifecycle. This means that the speed, scale, and contextualization of attacks will exponentially increase. Traditional security controls are already struggling to detect attacks that have never been seen before in the wild – be it malware without known signatures, new command and control domains, or individualized spear phishing emails. There is no chance that traditional tools will be able to cope with future attacks as this becomes the norm and easier to realize than ever before.
To stay ahead of this next wave of attacks, AI is becoming a necessary part of the defender’s stack, as no matter how well-trained or how well-staffed, humans alone will no longer be able to keep up. Hundreds of organizations are already using Autonomous Response to fight back against new strains of ransomware, insider threats, previously unknown techniques, tools and procedures, and many other threats. Cyber AI technology allows human responders to take stock and strategize from behind the front line. A new age in cyber defense is just beginning, and the effect of AI on this battleground is already proving fundamental.
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.
Tracking a Dragon: Investigating a DragonForce-affiliated ransomware attack with Darktrace
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.
Figure 1: Darktrace’s detection of administrator credentials being used in Kerberos login events 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
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.
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.
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.
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.
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.
WSUS Exploited: Darktrace’s Analysis of Post-Exploitation Activities Related to CVE-2025-59287
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.
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.
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].
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.
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),
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
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