Blog
/
Network
/
October 26, 2022

Strategies to Prolong Quantum Ransomware Attacks

Learn more about how Darktrace combats Quantum Ransomware changing strategy for cyberattacks. Explore the power of AI-driven network cyber security!
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
Nicole Wong
Cyber Security Analyst
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
26
Oct 2022

Within science and engineering, the word ‘quantum’ may spark associations with speed and capability, referencing a superior computer that can perform tasks a classical computer cannot. In cyber security, some may recognize ‘quantum’ in relation to cryptography or, more recently, as the name of a new ransomware group, which achieved network-wide encryption a mere four hours after an initial infection.   

Although this group now has a reputation for carrying out fast and efficient attacks, speed is not their only tactic. In August 2022, Darktrace detected a Quantum Ransomware incident where attackers remained in the victim’s network for almost a month after the initial signs of infection, before detonating ransomware. This was a stark difference to previously reported attacks, demonstrating that as motives change, so do threat actors’ strategies. 

The Quantum Group

Quantum was first identified in August 2021 as the latest of several rebrands of MountLocker ransomware [1]. As part of this rebrand, the extension ‘.quantum’ is appended to filenames that are encrypted and the associated ransom notes are named ‘README_TO_DECRYPT.html’ [2].  

From April 2022, media coverage of this group has increased following a DFIR report detailing an attack that progressed from initial access to domain-wide ransomware within four hours [3]. To put this into perspective, the global median dwell time for ransomware in 2020 and 2021 is 5 days [4]. In the case of Quantum, threat actors gained direct keyboard access to devices merely 2 hours after initial infection. The ransomware was staged on the domain controller around an hour and a half later, and executed 12 minutes after that.   

Quantum’s behaviour bears similarities to other groups, possibly due to their history and recruitment. Several members of the disbanded Conti ransomware group are reported to have joined the Quantum and BumbleBee operations. Security researchers have also identified similarities in the payloads and C2 infrastructure used by these groups [5 & 6].  Notably, these are the IcedID initial payload and Cobalt Strike C2 beacon used in this attack. Darktrace has also observed and prevented IcedID and Cobalt Strike activity from BumbleBee across several customer environments.

The Attack

From 11th July 2022, a device suspected to be patient zero made repeated DNS queries for external hosts that appear to be associated with IcedID C2 traffic [7 & 8]. In several reported cases [9 & 10], this banking trojan is delivered through a phishing email containing a malicious attachment that loads an IcedID DLL. As Darktrace was not deployed in the prospect’s email environment, there was no visibility of the initial access vector, however an example of a phishing campaign containing this payload is presented below. It is also possible that the device was already infected prior to joining the network. 

Figure 1- An example phishing email used to distribute IcedID. If configured, Darktrace/Email would be able to detect that the email was sent from an anomalous sender, was part of a fake reply chain, and had a suspicious attachment containing compressed content of unusual mime type [11].    

 

Figure 2- The DNS queries to endpoints associated with IcedID C2 servers, taken from the infected device’s event log.  Additional DNS queries made to other IcedID C2 servers are in the list of IOCs in the appendices.  The repeated DNS queries are indicative of beaconing.


It was not until 22nd July that activity was seen which indicated the attack had progressed to the next stage of the kill chain. This contrasts the previously seen attacks where the progression to Cobalt Strike C2 beaconing and reconnaissance and lateral movement occurred within 2 hours of the initial infection [12 & 13]. In this case, patient zero initiated numerous unusual connections to other internal devices using a compromised account, connections that were indicative of reconnaissance using built-in Windows utilities:

·      DNS queries for hostnames in the network

·      SMB writes to IPC$ shares of those hostnames queried, binding to the srvsvc named pipe to enumerate things such as SMB shares and services on a device, client access permissions on network shares and users logged in to a remote session

·      DCE-RPC connections to the endpoint mapper service, which enables identification of the ports assigned to a particular RPC service

These connections were initiated using an existing credential on the device and just like the dwelling time, differed from previously reported Quantum group attacks where discovery actions were spawned and performed automatically by the IcedID process [14]. Figure 3 depicts how Darktrace detected that this activity deviated from the device’s normal behaviour.  

Figure 3- This figure displays the spike in active internal connections initiated by patient zero. The coloured dots represent the Darktrace models that were breached, detecting this unusual reconnaissance and lateral movement activity.

Four days later, on the 26th of July, patient zero performed SMB writes of DLL and MSI executables to the C$ shares of internal devices including domain controllers, using a privileged credential not previously seen on the patient zero device. The deviation from normal behaviour that this represents is also displayed in Figure 3. Throughout this activity, patient zero made DNS queries for the external Cobalt Strike C2 server shown in Figure 4. Cobalt Strike has often been seen as a secondary payload delivered via IcedID, due to IcedID’s ability to evade detection and deploy large scale campaigns [15]. It is likely that reconnaissance and lateral movement was performed under instructions received by the Cobalt Strike C2 server.   

Figure 4- This figure is taken from Darktrace’s Advanced Search interface, showing a DNS query for a Cobalt Strike C2 server occurring during SMB writes of .dll files and DCE-RPC requests to the epmapper service, demonstrating reconnaissance and lateral movement.


The SMB writes to domain controllers and usage of a new account suggests that by this stage, the attacker had achieved domain dominance. The attacker also appeared to have had hands-on access to the network via a console; the repetition of the paths ‘programdata\v1.dll’ and ‘ProgramData\v1.dll’, in lower and title case respectively, suggests they were entered manually.  

These DLL files likely contained a copy of the malware that injects into legitimate processes such as winlogon, to perform commands that call out to C2 servers [16]. Shortly after the file transfers, the affected domain controllers were also seen beaconing to external endpoints (‘sezijiru[.]com’ and ‘gedabuyisi[.]com’) that OSINT tools have associated with these DLL files [17 & 18]. Moreover, these SSL connections were made using a default client fingerprint for Cobalt Strike [19], which is consistent with the initial delivery method. To illustrate the beaconing nature of these connections, Figure 5 displays the 4.3 million daily SSL connections to one of the C2 servers during the attack. The 100,000 most recent connections were initiated by 11 unique source IP addresses alone.

Figure 5- The Advanced Search interface, querying for external SSL connections from devices in the network to an external host that appears to be a Cobalt Strike C2 server. 4.3 million connections were made over 8 days, even after the ransomware was eventually detonated on 2022-08-03.


Shortly after the writes, the attack progressed to the penultimate stage. The next day, on the 27th of July, the attackers moved to achieve their first objective: data exfiltration. Data exfiltration is not always performed by the Quantum ransomware gang. Researchers have noted discrepancies between claims of data theft made in their ransom notes versus the lack of data seen leaving the network, although this may have been missed due to covert exfiltration via a Cobalt Strike beacon [20]. 

In contrast, this attack displayed several gigabytes of data leaving internal devices including servers that had previously beaconed to Cobalt Strike C2 servers. This data was transferred overtly via FTP, however the attacker still attempted to conceal the activity using ephemeral ports (FTP in EPSV mode). FTP is an effective method for attackers to exfiltrate large files as it is easy to use, organizations often neglect to monitor outbound usage, and it can be shipped through ports that will not be blocked by traditional firewalls [21].   

Figure 6 displays an example of the FTP data transfer to attacker-controlled infrastructure, in which the destination share appears structured to identify the organization that the data was stolen from, suggesting there may be other victim organizations’ data stored. This suggests that data exfiltration was an intended outcome of this attack. 

Figure 6- This figure is from Darktrace’s Advanced Search interface, displaying some of the data transferred from an internal device to the attacker’s FTP server.

 
Data was continuously exfiltrated until a week later when the final stage of the attack was achieved and Quantum ransomware was detonated. Darktrace detected the following unusual SMB activity initiated from the attacker-created account that is a hallmark for ransomware (see Figure 7 for example log):

·      Symmetric SMB Read to Write ratio, indicative of active encryption

·      Sustained MIME type conversion of files, with the extension ‘.quantum’ appended to filenames

·      SMB writes of a ransom note ‘README_TO_DECRYPT.html’ (see Figure 8 for an example note)

Figure 7- The Model Breach Event Log for a device that had files encrypted by Quantum ransomware, showing the reads and writes of files with ‘.quantum’ appended to encrypted files, and an HTML ransom note left where the files were encrypted.

 

Figure 8- An example of the ransom note left by the Quantum gang, this one is taken from open-sources [22].


The example in Figure 8 mentions that the attacker also possessed large volumes of victim data.  It is likely that the gigabytes of data exfiltrated over FTP were leveraged as blackmail to further extort the victim organization for payment.  

Darktrace Coverage

 

Figure 9- Timeline of Quantum ransomware incident


If Darktrace/Email was deployed in the prospect’s environment, the initial payload (if delivered through a phishing email) could have been detected and held from the recipient’s inbox. Although DETECT identified anomalous network behaviour at each stage of the attack, since the incident occurred during a trial phase where Darktrace could only detect but not respond, the attack was able to progress through the kill chain. If RESPOND/Network had been configured in the targeted environment, the unusual connections observed during the initial access, C2, reconnaissance and lateral movement stages of the attack could have been blocked. This would have prevented the attackers from delivering the later stage payloads and eventual ransomware into the target network.

It is often thought that a properly implemented backup strategy is sufficient defense against ransomware [23], however as discussed in a previous Darktrace blog, the increasing frequency of double extortion attacks in a world where ‘data is the new oil’ demonstrates that backups alone are not a mitigation for the risk of a ransomware attack [24]. Equally, the lack of preventive defenses in the target’s environment enabled the attacker’s riskier decision to dwell in the network for longer and allowed them to optimize their potential reward. 

Recent crackdowns from law enforcement on ransomware groups have shifted these groups’ approaches to aim for a balance between low risk and significant financial rewards [25]. However, given the Quantum gang only have a 5% market share in Q2 2022, compared to the 13.2% held by LockBit and 16.9% held by BlackCat [26], a riskier strategy may be favourable, as a longer dwell time and double extortion outcome offers a ‘belt and braces’ approach to maximizing the rewards from carrying out this attack. Alternatively, the gaps in-between the attack stages may imply that more than one player was involved in this attack, although this group has not been reported to operate a franchise model before [27]. Whether assisted by others or driving for a risk approach, it is clear that Quantum (like other actors) are continuing to adapt to ensure their financial success. They will continue to be successful until organizations dedicate themselves to ensuring that the proper data protection and network security measures are in place. 

Conclusion 

Ransomware has evolved over time and groups have merged and rebranded. However, this incident of Quantum ransomware demonstrates that regardless of the capability to execute a full attack within hours, prolonging an attack to optimize potential reward by leveraging double extortion tactics is sometimes still the preferred action. The pattern of network activity mirrors the techniques used in other Quantum attacks, however this incident lacked the continuous progression of the group’s attacks reported recently and may represent a change of motives during the process. Knowing that attacker motives can change reinforces the need for organizations to invest in preventative controls- an organization may already be too far down the line if it is executing its backup contingency plans. Darktrace DETECT/Network had visibility over both the early network-based indicators of compromise and the escalation to the later stages of this attack. Had Darktrace also been allowed to respond, this case of Quantum ransomware would also have had a very short dwell time, but a far better outcome for the victim.

Thanks to Steve Robinson for his contributions to this blog.

Appendices

References

[1] https://community.ibm.com/community/user/security/blogs/tristan-reed/2022/07/13/ibm-security-reaqta-vs-quantum-locker-ransomware

 

[2] https://www.bleepingcomputer.com/news/security/quantum-ransomware-seen-deployed-in-rapid-network-attacks/

 

[3], [12], [14], [16], [20] https://thedfirreport.com/2022/04/25/quantum-ransomware/

 

[4] https://www.mandiant.com/sites/default/files/2022-04/M-Trends%202022%20Executive%20Summary.pdf

 

[5] https://cyware.com/news/over-650-healthcare-organizations-affected-by-the-quantum-ransomware-attack-d0e776bb/

 

[6] https://www.kroll.com/en/insights/publications/cyber/bumblebee-loader-linked-conti-used-in-quantum-locker-attacks

 

[7] https://github.com/pan-unit42/tweets/blob/master/2022-06-28-IOCs-for-TA578-IcedID-Cobalt-Strike-and-DarkVNC.txt 

 

[8] https://github.com/stamparm/maltrail/blob/master/trails/static/malware/icedid.txt

 

[9], [15] https://www.cynet.com/blog/shelob-moonlight-spinning-a-larger-web-from-icedid-to-conti-a-trojan-and-ransomware-collaboration/

 

[10] https://www.microsoft.com/security/blog/2021/04/09/investigating-a-unique-form-of-email-delivery-for-icedid-malware/

 

[11] https://twitter.com/0xToxin/status/1564289244084011014

 

[13], [27] https://cybernews.com/security/quantum-ransomware-gang-fast-and-furious/

 

[17] https://www.virustotal.com/gui/domain/gedabuyisi.com/relations

 

[18] https://www.virustotal.com/gui/domain/sezijiru.com/relations.

 

[19] https://github.com/ByteSecLabs/ja3-ja3s-combo/blob/master/master-list.txt 

 

[21] https://www.darkreading.com/perimeter/ftp-hacking-on-the-rise

 

[22] https://www.pcrisk.com/removal-guides/23352-quantum-ransomware

 

[23] https://www.cohesity.com/resource-assets/tip-sheet/5-ways-ransomware-renders-backup-useless-tip-sheet-en.pdf

 

[24] https://www.forbes.com/sites/nishatalagala/2022/03/02/data-as-the-new-oil-is-not-enough-four-principles-for-avoiding-data-fires/ 

 

[25] https://www.bleepingcomputer.com/news/security/access-to-hacked-corporate-networks-still-strong-but-sales-fall/

 

[26] https://www.bleepingcomputer.com/news/security/ransom-payments-fall-as-fewer-victims-choose-to-pay-hackers/ 

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
Nicole Wong
Cyber Security Analyst

More in this series

No items found.

Blog

/

Network

/

May 14, 2026

Chinese APT Campaign Targets Entities with Updated FDMTP Backdoor

Default blog imageDefault blog image

Darktrace have identified activity consistent with Chinese-nexus operations, a Twill Typhoon-linked campaign targeting customer environments, primarily within the Asia-Pacific & Japan (APJ) region

Beginning in late September 2025, multiple affected hosts were observed making requests to domains impersonating content delivery networks (CDNs), including infrastructure masquerading as Yahoo- and Apple-affiliated services. Across these cases, Darktrace identified a consistent behavioral execution pattern: the retrieval of legitimate binaries alongside malicious Dynamic Link Libraries (DLLs), enabling sideloading and execution of a modular .NET-based Remote Access Trojan (RAT) framework.

The activity aligns with patterns described in Darktrace’s previous Chinese-nexus operations report, Crimson Echo. In this case, observed modular intrusion chains built on legitimate software, and staged payload delivery. Threat actors retrieve legitimate binaries alongside configuration files and malicious DLLs to enable sideloading of a .NET-based RAT.

Observed Campaign

Across cases, the same ordered sequence appears: retrieval of a legitimate executable, (2) retrieval of a matching .config file, (3) retrieval of the malicious

DLL, (4) repeated DLL downloads over time, and (5) command-and-control (C2) communication. The .config file retrieves a malicious binary, while the legitimate binary provides a legitimate process to run it in.

Darktrace assesses with moderate confidence that this activity aligns with publicly reported Twill Typhoon tradecraft. The observed use of FDMTP, DLL sideloading, and overlapping infrastructure is consistent with previously observed operations, though not unique to a single actor. While initial access was not directly observed, previous Twill Typhoon campaigns have typically involved spear-phishing.

What Darktrace Observed

Since late September 2025, Darktrace has observed multiple customer environments making HTTP GET requests to infrastructure presenting as “CDN” endpoints for well-known platforms (including Yahoo and Apple lookalikes). Across cases, the affected hosts retrieved legitimate executables, then matching .config files (same base filename), then DLLs intended for sideloading. The sequencing of a legitimate binary + configuration + DLL  has been previously observed in campaigns linked to China-nexus threat actors.

In several cases, affected hosts also issued outbound requests to a /GetCluster endpoint, including the protocol=Dotnet-Tcpdmtp parameter. This activity was repeatedly followed by retrieval of DLL content that was subsequently used for search-order hijacking within legitimate processes.

In the September–October 2025 cases, Darktrace alerting commonly surfaced early-stage registration and C2 setup behaviors, followed by retrieval of a DLL (e.g., Client.dll) from the same external host, sometimes repeatedly over multiple days, consistent with establishing and maintaining the execution chain.

In April 2026, a finance-sector endpoint initiated a series of GET requests to yahoo-cdn[.]it[.]com, first fetching legitimate binaries (including vshost.exe and dfsvc.exe), then repeatedly retrieving associated configuration and DLL components (including dfsvc.exe.config and dnscfg.dll) over an 11-day window. The use of both Visual Studio hosting and OneClick (dfsvc.exe) paths are used to ensure the malware can run in the targeted environment.

Technical Analysis

Initial staging and execution

While the initial access method is unknown, Darktrace security researchers identified multiple archives containing the malware.

A representative example includes a ZIP archive (“test.zip”) containing:

  • A legitimate executable: biz_render.exe (Sogou Pinyin IME)
  • A malicious DLL: browser_host.dll

Contained within the zip archive named “test.zip” is the legitimate binary “biz_render.exe”, a popular Chinese Input Method Editor (IME) Sogou Pinyin.

Alongside the legitimate binary is a malicious DLL named “browser_host.dll”. As the legitimate binary loads a legitimate DLL named “browser_host.dll” via LoadLibraryExW, the malicious DLL has been named the same to sideload the malicious DLL into biz_render.exe. By supplying a malicious DLL with an identical name, the actor hijacks execution flow, enabling the payload to execute within a trusted process.

Figure 1: Biz_render.exe loading browser_host.dll.

The legitimate binary invokes the function GetBrowserManagerInstance from the sideloaded “browser_host.dll”, which then performs XOR-based decryption of embedded strings (key 0x90) to resolve and dynamically load mscoree.dll.

The DLL uses the Windows Common Language Runtime (CLR) to execute managed .NET code inside the process rather than relying solely on native binaries. During execution, the loader loads a payload directly into memory as .NET assemblies, enabling an in-memory execution.

C2 Registration

A GET request is made to:

GET /GetCluster?protocol=DotNet-TcpDmtp&tag={0}&uid={1}

with the custom header:

Verify_Token: Dmtp

This returns Base64-encoded and gzip-compressed IP addresses used for subsequent communication.

Figure 2: Decoded IPs.

Staged payload retrieval

Subsequent activity includes retrieval of multiple components from yahoo-cdn.it[.]com. The following GET requests are made:

/dfsvc.exe

/dnscfg.dll

/dfsvc.exe.config

/vhost.exe

/Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll

/config.etl

ClickOnce and AppDomain hijacking

Dfsvc.exe is the legitimate Windows ClickOnce Engine, part of the .NET framework used for updating ClickOnce Applications. Accompanying dfsvc.exe is a legitimate dfsvc.exe.config file that is used to store configuration data for the application. However, in this instance the malware has replaced the legitimate dfsvc.exe.config with the one retrieved from the server in: C:\Windows\Microsoft.NET\Framework64\v4.0.30319.

Additionally, vhost.exe the legitimate Visual Studio hosting process is retrieved from the server, along with “Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll” and “config.etl”. The DLL is used to decrypt the AES encrypted payload in config.etl and load it. The encrypted payload is dnscfg.dll, which can be loaded into vshost instead of dfsvc, and may be used if the environment does not support .NET.

Figure 3: ClickOnce configuration.

The malicious configuration disables logging, forces the application to load dnscfg.dll from the remote server, and uses a custom AppDomainManager to ensure the DLL is executed during initialization of dfsvc.exe. To ensure persistence, a scheduled task is added for %APPDATA%\Local\Microsoft\WindowsApps\dfsvc.exe.

Core payload

The DLL dnscfg.dll is a .NET binary named Client.TcpDmtp.dll. The payload is a heavily obfuscated backdoor that generates its logic at runtime and communicates with the command and control (C2) over custom TCP, DMTP (Duplex Message Transport Protocol) and appears to be an updated version of FDMTP to version 3.2.5.1

Figure 4: InitializeNewDomain.

The payload:

  • Uses cluster-based resolution (GetHostFromCluster)
  • Implements token validation
  • Enters a persistent execution loop (LoopMessage)
  • Supports structured remote tasking over DMTP

Once connected, the malware enters a persistent loop (LoopMessage), enabling it to receive commands from the remote server.

Figure 5: DMTP Connect function.

Rather than referencing values directly, they are retrieved through containers that are resolved at runtime. String values are stored in an encrypted byte array (_0) and decrypted by a custom XOR-based string decryption routine (dcsoft). The lower 16 bits of the provided key are XORed with 0xA61D (42525) to derive the initial XOR key, while subsequent bits define the string length and offset into the encrypted byte array. Each character is reconstructed from two encrypted bytes and XORed with the incrementing key value, producing the plaintext string used by the payload.

Figure 6: Decrypted strings.

Embedded in the resources section are multiple compressed binaries, the majority of which are library files. The only exceptions are client.core.dll and client.dmtpframe.dll.

Figure 7: Resources.

Modular framework and plugins

The payload embeds multiple compressed libraries, notably:

  • client.core.dll
  • client.dmtpframe.dll

Client.core.dll is a core library used for system profiling, C2 communication and plugin execution. The implant has the functionality to retrieve information including antivirus products, domain name, HWID, CLR version, administrator status, hardware details, network details, operating system, and user.

Figure 8: Client.Core.Info functions.

Additionally, the component is responsible for loading plugins, with support for both binary and JSON-based plugin execution. This allows plugins to receive commands and parameters in different formats depending on the task being performed.

The framework handles details such as plugin hashes, method names, task identifiers, caller tracking, and argument processing, allowing plugins to be executed consistently within the environment. In addition to execution management, the library also provides plugins with access to common runtime functionality such as logging, communication, and process handling.

Figure 9: Client.core functions.

client.dmtpframe.dll handles:

  • DMTP communication
  • Heartbeats and reconnection
  • Plugin persistence via registry:

HKCU\Software\Microsoft\IME\{id}

Client.dmtpframe.dll is built on the TouchSocket DMTP networking library and continues to manage the remote plugins. The DLL implements remote communication features including heartbeat maintenance, reconnection handling, RPC-style messaging, SSL support, and token-based verification. The DLL also has the ability to add plugins to the registry under HKCU/Software/Microsoft/IME/{id} for persistence.

Plugins observed

While the full set of plugins remains unknown, researchers were able to identify four plugins, including:

  • Persist.WpTask.dll - used to create, remove and trigger scheduled Windows tasks remotely.
  • Persist.registry.dll - used to manage registry persistence with the ability to create, and delete registry values, along with hidden persistence keys.
  • Persist.extra.dll - used to load and persist the main framework.
  • Assist.dll - used to remotely retrieve files or commands, as well as manipulate system processes.
Figure 10: Plugins stored in IME registry.
Figure 11: Obfuscated script in plugin resources.

Persist.extra.dll is a module that is used to load a script “setup.log” to load and persist the main framework. Stored within the resources section of the binary is an obfuscated script that creates a .NET COM object that is added to the registry key HKCU\Software\Classes\TypeLib\ {9E175B61-F52A-11D8-B9A5-505054503030} \1.0\1\Win64 for persistence. After deobfuscating this script, another DLL is revealed named “WindowsBase.dll”.

Figure 12: Registry entry for script.

The binary checks in with icloud-cdn[.]net every five minutes, retrieves a version string, downloads an encrypted payload named checksum.bin, saves it locally as C:\ProgramData\USOShared\Logs\checksum.etl, decrypts it with AES using the hardcoded key POt_L[Bsh0=+@0a., and loads the decrypted assembly directly from memory via Assembly.Load(byte[]). The version.txt file acts as an update marker so it only re-downloads when the remote version changes, while the mutex prevents duplicate instances.

Figure 13: USOShared/Logs.

Checksum.etl is decrypted with AES and loaded into memory, loading another .NET DLL named “Client.dll”. This binary is the same as “dnscfg.dll” mentioned at the start and allows the threat actors to update the main framework based on the version.

Conclusion

Across cases, Darktrace consistently observed the following sequence:

  • Retrieval of legitimate executables
  • Retrieval of DLLs for sideloading
  • C2 registration via /GetCluster

This approach is consistent with broader China-nexus tradecraft. As outlined in Darktrace’s Crimson Echo report, the stable feature of this activity is behavioral. Infrastructure rotates and payloads can change, but the execution model persists. For defenders, the implication is straightforward: detection anchored to individual indicators will degrade quickly. Detection anchored to a behavioral sequence offer a far more durable approach.

Credit to Tara Gould (Malware Research Lead), Adam Potter (Senior Cyber Analyst), Emma Foulger (Global Threat Research Operations Lead), Nathaniel Jones (VP, Security & AI Strategy)

Edited by Ryan Traill (Content Manager)


Appendices

A detailed list of detection models and triggered indicators is provided alongside IoCs.

Indicators of Compromise (IoCs)

Test.zip - fc3959ebd35286a82c662dc81ca658cb

Dnscfg.dll - b2c8f1402d336963478f4c5bc36c961a

Client.TcpDmtp.dll - c52b4a16d93a44376f0407f1c06e0b

Browser_host.dll - c17f39d25def01d5c87615388925f45a

Client.DmtpFrame.dll - 482cc72e01dfa54f30efe4fefde5422d

Persist.Extra - 162F69FE29EB7DE12B684E979A446131

Persist.Registry - 067FBAD4D6905D6E13FDC19964C1EA52

Assist - 2CD781AB63A00CE5302ED844CFBECC27

Persist.WpTask - DF3437C88866C060B00468055E6FA146

Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll - c650a624455c5222906b60aac7e57d48

www.icloud-cdn[.]net

www.yahoo-cdn.it[.]com

154.223.58[.]142[AP8] [EF9]

MITRE ATT&CK Techniques

T1106 – Native API

T1053.005 - Scheduled Task

T1546.16 - Component Object Model Hijacking

T1547.001 - Registry Run Keys

T1511.001 - Dynamic Link Library Injection

T1622 – Debugger Evasion

T1140 – Deobfuscate/Decode Files or Information

T1574.001 - Hijack Execution Flow: DLL

T1620 – Reflective Code Loading

T1082 – System Information Discovery

T1007 – System Service Discovery

T1030 – System Owner/User Discovery

T1071.001 - Web Protocols

T1027.007 - Dynamic API Resolution

T1095 – Non-Application Layer Protocol

Darktrace Model Alerts

·      Compromise / Beaconing Activity To External Rare

·      Compromise / HTTP Beaconing to Rare Destination

·      Anomalous File / Script from Rare External Location

·      Compromise / Sustained SSL or HTTP Increase

·      Compromise / Agent Beacon to New Endpoint

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Compromise / Quick and Regular Windows HTTP Beaconing

·      Compromise / High Volume of Connections with Beacon Score

·      Anomalous File / Anomalous Octet Stream (No User Agent)

·      Compromise / Repeating Connections Over 4 Days

·      Device / Large Number of Model Alerts

·      Anomalous Connection / Multiple Connections to New External TCP Port

·      Compromise / Large Number of Suspicious Failed Connections

·      Anomalous Connection / Multiple Failed Connections to Rare Endpoint

·      Device / Increased External Connectivity

Continue reading
About the author
Tara Gould
Malware Research Lead

Blog

/

/

May 12, 2026

Resilience at the Speed of AI: Defending the Modern Campus with Darktrace

Default blog imageDefault blog image

Why higher education is a different cybersecurity battlefield

After four decades in IT, now serving as both CIO and CISO, I’ve learned one simple truth: cybersecurity is never “done.” It’s a constant game of cat and mouse. Criminals evolve. Technologies advance. Regulations expand. But in higher education, the challenge is uniquely complex.

Unlike a bank or a military installation, we can’t lock down networks to a narrow set of approved applications. Higher education environments are open by design. Students collaborate globally, faculty conduct cutting-edge research, and administrators manage critical operations, all of which require seamless access to the internet, global networks, cloud platforms, and connected systems.

Combine that openness with expanding regulatory mandates and tight budgets, and the balancing act becomes clear.

Threat actors don’t operate under the same constraints. Often well-funded and sponsored by nation-states with significant resources, they’re increasingly organized, strategic, and innovative.

That sophistication shows up in the tactics we face every day, from social engineering and ransomware to AI-driven impersonation attacks. We’re dealing with massive volumes of data, countless signals, and a very small window between detection and damage.

No human team, no matter how talented or how numerous, can manually sift through that noise at the speed required.

Discovering a force multiplier

Nothing in cybersecurity is 100% foolproof. I never “set it and forget it.” But for institutions balancing rising threats and finite resources, the Darktrace ActiveAI Security Platform™ offers something incredibly valuable: peace of mind through speed and scale.

It closes the gap between detection and response in a way humans can’t possibly match. At the speed of light, it can quarantine, investigate, and contain anomalous activity.

I’ve purchased and deployed Darktrace three separate times at three different institutions because I’ve seen firsthand what it can do and what it enables teams like mine to achieve.

I first encountered Darktrace while serving as CIO for a large multi-campus college system. What caught my attention was Darktrace's Self-Learning AI, and its ability to learn what "normal" looked like across our network. Instead of relying solely on static signatures or rigid rules, Darktrace built a behavioral baseline unique to our environment and alerted us in real time when something simply didn’t look right.

In higher education, where strict lockdowns aren’t realistic, that behavioral model made all the difference. We deployed it across five campuses, and the impact was immediate. Operating 24/7, Darktrace surfaced threats in ways our team couldn’t replicate manually.

Over time, the Darktrace platform evolved alongside the changing threat landscape, expanding into intrusion prevention, cloud visibility, and email security. At subsequent institutions, including Washington College, Darktrace was one of my first strategic investments.

Revealing the hidden threat other tools missed

One of the most surprising investigations of my career involved a data leak. Leadership suspected sensitive information from high-level meetings was being exposed, but our traditional tools couldn’t provide any answers.

Using Darktrace’s deep network visibility, down to packet-level data, we traced unusual connections to our CCTV camera system, which had been configured with a manufacturer’s default password. A small group of employees had hacked into the CCTV cameras, accessed audio-enabled recordings from boardroom meetings, and stored copies locally.

No other tool in our environment could have surfaced those connections the way Darktrace did. It was a clear example of why using AI to deeply understand how your organization, systems, and tools normally behave, matters: threats and risks don’t always look the way we expect.

Elevating a D-rating into a A-level security program

When I arrived at my last CISO role, the institution had recently experienced a significant ransomware attack. Attackers located  data  which informed their setting  ransom demands to an amount they knew would likely result in payment. It was a sobering example of how calculated and strategic modern cybercriminals have become.

Third-party cyber ratings reflected that reality, with a  D rating.

To raise the bar, we implemented a comprehensive security program and integrated layered defenses; -deploying state of the art tools and methods-  across the environment, with Darktrace at its core.

After a 90-day learning period to establish our behavioral baseline, we transitioned the platform into fully autonomous mode. In a single 30-day span, Darktrace conducted more than 2,500 investigations and autonomously resolved 92% of all false positives.

For a small team, that’s transformative. Instead of drowning in alerts, my staff focused on less than  200 meaningful cases that warranted human review.

Today, we maintain a perfect A rating from third-party assessors and have remained cybersafe.

Peace of mind isn’t about complacency

The effect of Darktrace as a force multiplier has a real human impact.

With the time reclaimed through automation, we expanded community education programs and implemented simulated phishing exercises. Through sustained training and awareness efforts, we reduced social engineering susceptibility from nearly 45% to under 5%.

On a personal level, Darktrace allows me to sleep better at night and take time off knowing we have intelligent systems monitoring and responding around the clock. For any CIO or CISO carrying institutional risk on their shoulders, that matters.

The next era: AI vs. AI

A new chapter in cybersecurity is unfolding as adversaries leverage AI to enhance scale, speed, and believability. Phishing campaigns are more personalized, impersonation attempts are more precise, and deepfake video technology, including live video, is disturbingly authentic. At the same time, organizations are rapidly adopting AI across their own environments —from GenAI assistants to embedded tools to autonomous agents. These systems don’t operate within fixed rules. They act across email, cloud, SaaS, and identity systems, often with broad permissions, and their behavior can evolve over time in ways that are difficult to predict or control.

That creates a new kind of security challenge. It’s not just about defending against AI-powered threats but understanding and governing how AI behaves within your environment, including what it can access, how it acts, and where risk begins to emerge.

From my perspective, this is a natural next step for Darktrace.

Darktrace brings a level of maturity and behavioral understanding uniquely suited to the complexity of AI environments. Self-Learning AI learns the normal patterns of each business to interpret context, uncover subtle intent, and detect meaningful deviations without relying on predefined rules or signatures. Extending into securing AI by bringing real-time visibility and control to GenAI assistants, AI agents, development environments and Shadow AI, feels like the logical evolution of what Darktrace already does so well.

Just as importantly, Darktrace is already built for dynamic, cross-domain environments where risk doesn’t sit in a single tool or control plane. In higher education, activity already spans multiple systems and, with AI, that interconnection only accelerates.

Having deployed Darktrace multiple times, I have confidence it’s uniquely positioned to lead in this space and help organizations adopt AI with greater visibility and control.

---

Since authoring this blog, Irving Bruckstein has transitioned to the role of Chief Executive Officer of the Cyberaigroup.

Continue reading
About the author
Irving Bruckstein
CEO CyberAIgroup
Your data. Our AI.
Elevate your network security with Darktrace AI