Blog
/
Email
/
February 24, 2025

Detecting and Containing Account Takeover with Darktrace

Account takeovers are rising with SaaS adoption. Learn how Darktrace detects deviations in user behavior and autonomously stops threats before they escalate.
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
Min Kim
Cyber Security Analyst
women on laptop in officeDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
24
Feb 2025

Thanks to its accessibility from anywhere with an internet connection and a web browser, Software-as-a-Service (SaaS) platforms have become nearly universal across organizations worldwide. However, with this growing popularity comes greater responsibility. Increased attention attracts a larger audience, including those who may seek to exploit these widely used services. One crucial factor to be vigilant about in the SaaS landscape is safeguarding internal credentials. Minimal protection on accounts can lead to SaaS hijacking, which could allow further escalations within the network.

How does SaaS account takeover work?

SaaS hijacking occurs when a malicious actor takes control of a user’s active session with a SaaS application. Attackers can achieve this through various methods, including employees using company credentials on compromised or spoofed external websites, brute-force attacks, social engineering, and exploiting outdated software or applications.

After the hijack, attackers may escalate their actions by changing email rules and using internal addresses for additional social engineering attacks. The larger goal of these actions is often to steal internal data, damage reputations, and disrupt operations.

Account takeover protection

It has become essential to have security tools capable of outsmarting potential malicious actors. Traditional tools that rely on rules and signatures may not be able to identify new events, such as logins or activities from a rare endpoint, unless they come from a known malicious source.

Darktrace relies on analysis of user and network behavior, tailored to each customer, allowing it to identify anomalous events that the user typically does not engage in. In this way, unusual SaaS activities can be detected, and unwanted actions can be halted to allow time for remediation before further escalations.

The following cases, drawn from the global customer base, illustrate how Darktrace detects potential SaaS hijack attempts and further escalations, and applies appropriate actions when necessary.

Case 1: Unusual login after a phishing email

A customer in the US received a suspicious email that seemed to be from the legitimate file storage service, Dropbox. However, Darktrace identified that the reply-to email address, hremployeepyaroll@mail[.]com, was masquerading as one associated with the customer’s Human Resources (HR) department.

Further inspection of this sender address revealed that the attacker had intentionally misspelled ‘payroll’ to trick recipients into believing it was legitimate

Furthermore, the subject of the email indicated that the attackers were attempting a social engineering attack by sharing a file related to pay raises and benefits to capture the recipients' attention and increase the likelihood of their targets engaging with the email and its attachment.

Figure 1: Subject of the phishing email.
Figure 1: Subject of the phishing email.

Unknowingly, the recipient, who believed the email to be a legitimate HR communication, acted on it, allowing malicious attackers to gain access to the account. Following this, the recipient’s account was observed logging in from a rare location using multi-factor authentication (MFA) while also being active from another more commonly observed location, indicating that the SaaS account had been compromised.

Darktrace’s Autonomous Response action triggered by an anomalous email received by an internal user, followed by a failed login attempt from a rare external source.
Figure 2: Darktrace’s Autonomous Response action triggered by an anomalous email received by an internal user, followed by a failed login attempt from a rare external source.

Darktrace subsequently observed the SaaS actor creating new inbox rules on the account. These rules were intended to mark as read and move any emails mentioning the file storage company, whether in the subject or body, to the ‘Conversation History’ folder. This was likely an attempt by the threat actor to hide any outgoing phishing emails or related correspondence from the legitimate account user, as the ‘Conversation History’ folder typically goes unread by most users.

Typically, Darktrace / EMAIL would have instantly placed the phishing email in the junk folder before they reached user’s inbox, while also locking the links identified in the suspicious email, preventing them from being accessed. Due to specific configurations within the customer’s deployment, this did not happen, and the email remained accessible to the user.

Case 2: Login using unusual credentials followed by password change

In the latter half of 2024, Darktrace detected an unusual use of credentials when a SaaS actor attempted to sign into a customer’s Microsoft 365 application from an unfamiliar IP address in the US. Darktrace recognized that since the customer was located within the Europe, Middle East, and Africa (EMEA) region, a login from the US was unexpected and suspicious. Around the same time, the legitimate account owner logged into the customer’s SaaS environment from another location – this time from a South African IP, which was commonly seen within the environment and used by other internal SaaS accounts.

Darktrace understood that this activity was highly suspicious and unlikely to be legitimate, given one of the IPs was known and expected, while the other had never been seen before in the environment, and the simultaneous logins from two distant locations were geographically impossible.

Model alert in Darktrace / IDENTITY: Detecting a login from a different source while the user is already active from another source.
Figure 3: Model alert in Darktrace / IDENTITY: Detecting a login from a different source while the user is already active from another source.

Darktrace detected several unusual login attempts, including a successful login from an uncommon US source. Subsequently, Darktrace / NETWORK identified the device associated with this user making external connections to rare endpoints, some of which were only two weeks old. As this customer had integrated Darktrace with Microsoft Defender, the Darktrace detection was enriched by Defender, adding the additional context that the user had likely been compromised in an Adversary-in-the-Middle (AiTM) phishing attack. AiTM phishing attacks occur when a malicious attacker intercepts communications between a user and a legitimate authentication service, potentially leading to account hijacking. These attacks are harder to identify as they can bypass security measures like MFA.

Following this, Darktrace observed the attacker using the now compromised credentials to access password management and change the account's password. Such behavior is common in account takeover incidents, as attackers seek to maintain persistence within the SaaS environment.

While Darktrace’s Autonomous Response was not fully configured on the customer’s SaaS environment, they were subscribed to the Managed Threat Detection service offered by Darktrace’s Security Operations Center (SOC). This 24/7 service ensures that Darktrace’s analysts monitor and investigate emerging suspicious activity, informing customers in real-time. As such, the customer received notification of the compromise and were able to quickly take action to prevent further escalation.

Case 3: Unusual logins, new email rules and outbound spam

Recently, Darktrace has observed a trend in SaaS compromises involving unusual logins, followed by the creation of new email rules, and then outbound spam or phishing campaigns being launched from these accounts.

In October, Darktrace identified a SaaS user receiving an email with the subject line "Re: COMPANY NAME Request for Documents" from an unknown sender using a freemail  account. As freemail addresses require very little personal information to create, threat actors can easily create multiple accounts for malicious purposes while retaining their anonymity.

Within the identified email, Darktrace found file storage links that were likely intended to divert recipients to fraudulent or malicious websites upon interaction. A few minutes after the email was received, the recipient was seen logging in from three different sources located in the US, UK, and the Philippines, all around a similar time. As the customer was based in the Philippines, a login from there was expected and not unusual. However, Darktrace understood that the logins from the UK and US were highly unusual, and no other SaaS accounts had connected from these locations within the same week.

After successfully logging in from the UK, the actor was observed updating a mailbox rule, renaming it to ‘.’ and changing its parameters to move any inbound emails to the deleted items folder and mark them as read.

Figure 4: The updated email rule intended to move any inbound emails to the deleted items folder.

Malicious actors often use ambiguous names like punctuation marks, repetitive letters, and unreadable words to name resources, disguising their rules to avoid detection by legitimate users or administrators. Similarly, attackers have been known to adjust existing rule parameters rather than creating new rules to keep their footprints untracked. In this case, the rule was updated to override an existing email rule and delete all incoming emails. This ensured that any inbound emails, including responses to potential phishing emails sent by the account, would be deleted, allowing the attacker to remain undetected.

Over the next two days, additional login attempts, both successful and failed, were observed from locations in the UK and the Philippines. Darktrace noted multiple logins from the Philippines where the legitimate user was attempting to access their account using a password that had recently expired or been changed, indicating that the attacker had altered the user’s original password as well.

Following this chain of events, over 500 emails titled “Reminder For Document Signed Agreement.10/28/2024” were sent from the SaaS actor’s account to external recipients, all belonging to a different organization within the Philippines.

These emails contained rare attachments with a ‘.htm’ extension, which included programming language that could initiate harmful processes on devices. While inherently not malicious, if used inappropriately, these files could perform unwanted actions such as code execution, malware downloads, redirects to malicious webpages, or phishing upon opening.

Outbound spam seen from the hijacked SaaS account containing a ‘.htm’ attachment.
Figure 5: Outbound spam seen from the hijacked SaaS account containing a ‘.htm’ attachment.

As this customer did not have Autonomous Response enabled for Darktrace / IDENTITY, the unusual activity went unattended, and the compromise was able to escalate to the point of a spam email campaign being launched from the account.

In a similar example on a customer network in EMEA, Darktrace detected unusual logins and the creation of new email rules from a foreign location through a SaaS account. However, in this instance, Autonomous Response was enabled and automatically disabled the compromised account, preventing further malicious activity and giving the customer valuable time to implement their own remediation measures.

Conclusion

Whether it is an unexpected login or an unusual sequence of events – such as a login followed by a phishing email being sent – unauthorized or unexpected activities can pose a significant risk to an organization’s SaaS environment. The threat becomes even greater when these activities escalate to account hijacking, with the compromised account potentially providing attackers access to sensitive corporate data. Organizations, therefore, must have robust SaaS security measures in place to prevent data theft, ensure compliance and maintain continuity and trust.

The Darktrace suite of products is well placed to detect and contain SaaS hijack attempts at multiple stages of an attack. Darktrace / EMAIL identifies initial phishing emails that attackers use to gain access to customer SaaS environments, while Darktrace / IDENTITY detects anomalous SaaS behavior on user accounts which could indicate they have been taken over by a malicious actor.

By identifying these threats in a timely manner and taking proactive mitigative measures, such as logging or disabling compromised accounts, Darktrace prevents escalation and ensures customers have sufficient time to response effectively.

Credit to Min Kim (Cyber Analyst) and Ryan Traill (Analyst Content Lead)

[related-resource]

Appendices

Darktrace Model Detections Case 1

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and New Email Rule

SaaS / Compliance / Anomalous New Email Rule

SaaS / Unusual Activity / Multiple Unusual SaaS Activities

SaaS / Access / Unusual External Source for SaaS Credential Us

SaaS / Compromise / Login From Rare Endpoint While User is Active

SaaS / Email Nexus / Unusual Login Location Following Link to File Storage

Antigena / SaaS / Antigena Email Rule Block (Autonomous Response)

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (Autonomous Response)

Antigena / SaaS / Antigena Enhanced Monitoring from SaaS User Block (Autonomous Response)

List of Indicators of Compromise (IoCs)

176.105.224[.]132 – IP address – Unusual SaaS Activity Source

hremployeepyaroll@mail[.]com – Email address – Reply-to email address

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Outlook Rules – PERSISTENCE – T1137

Cloud Service Dashboard – DISCOVERY – T1538

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Steal Web Session Cookie – CREDENTIAL ACCESS – T1539

Darktrace Model Detections Case 2

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and Account Update

Security Integration / High Severity Integration Detection

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Compromise / Login From Rare Endpoint While User Is Active

SaaS / Compromise / Login from Rare High Risk Endpoint

SaaS / Access / M365 High Risk Level Login

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (Autonomous Response)

Antigena / SaaS / Antigena Enhanced Monitoring from SaaS user Block (Autonomous Response)

List of IoCs

74.207.252[.]129 – IP Address – Suspicious SaaS Activity Source

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Cloud Service Dashboard – DISCOVERY – T1538

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Steal Web Session Cookie – CREDENTIAL ACCESS – T1539

Darktrace Model Detections Case 3

SaaS / Compromise / Unusual Login and Outbound Email Spam

SaaS / Compromise / New Email Rule and Unusual Email Activity

SaaS / Compromise / Unusual Login and New Email Rule

SaaS / Email Nexus / Unusual Login Location Following Sender Spoof

SaaS / Email Nexus / Unusual Login Location Following Link to File Storage

SaaS / Email Nexus / Possible Outbound Email Spam

SaaS / Unusual Activity / Multiple Unusual SaaS Activities

SaaS / Email Nexus / Suspicious Internal Exchange Activity

SaaS / Compliance / Anomalous New Email Rule

List of IoCs

95.142.116[.]1 – IP Address – Suspicious SaaS Activity Source

154.12.242[.]58 – IP Address – Unusual Source

MITRE ATT&CK Mapping

Cloud Accounts – DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS – T1078

Compromise Accounts – RESOURCE DEVELOPMENT – T1586

Email Accounts – RESOURCE DEVELOPMENT – T1585

Phishing – INITIAL ACCESS – T1566

Outlook Rules – PERSISTENCE – T1137

Internal Spear phishing – LATERAL MOVEMENT - T1534

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2025.

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
Min Kim
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

/

AI

/

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