January 30, 2023

Qakbot Resurgence in the Cyber Landscape

Stay informed on the evolving threat Qakbot. Protect yourself from the Qakbot resurgence! Learn more from our Darktrace AI Cybersecurity experts!

Written by

Nahisha Nobregas

SOC Analyst

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

Nahisha Nobregas

SOC Analyst

Jan 2023

In June 2022, Darktrace observed a surge in Qakbot infections across its client base. The detected Qakbot infections, which in some cases led to the delivery of secondary payloads such as Cobalt Strike and Dark VNC, were initiated through novel delivery methods birthed from Microsoft’s default blocking of XL4 and VBA macros in early 2022 [1]/[2]/[3]/[4] and from the public disclosure in May 2022 [5] of the critical Follina vulnerability (CVE-2022-30190) in Microsoft Support Diagnostic Tool (MSDT). Despite the changes made to Qakbot’s delivery methods, Qakbot infections still inevitably resulted in unusual patterns of network activity. In this blog, we will provide details of these network activities, along with Darktrace/Network’s coverage of them.

Qakbot Background

Qakbot emerged in 2007 as a banking trojan designed to steal sensitive data such as banking credentials. Since then, Qakbot has developed into a highly modular triple-threat powerhouse used to not only steal information, but to also drop malicious payloads and to serve as a backdoor. The malware is also versatile, with its delivery methods regularly changing in response to the changing threat landscape.

Threat actors deliver Qakbot through email-based delivery methods. In the first half of 2022, Microsoft started rolling out versions of Office which block XL4 and VBA macros by default. Prior to this change, Qakbot email campaigns typically consisted in the spreading of deceitful emails with Office attachments containing malicious macros. Opening these attachments and then enabling the macros within them would lead users’ devices to install Qakbot.

Actors who deliver Qakbot onto users’ devices may either sell their access to other actors, or they may leverage Qakbot’s capabilities to pursue their own objectives [6]. A common objective of actors that use Qakbot is to drop Cobalt Strike beacons onto infected systems. Actors will then leverage the interactive access provided by Cobalt Strike to conduct extensive reconnaissance and lateral movement activities in preparation for widespread ransomware deployment. Qakbot’s close ties to ransomware activity, along with its modularity and versatility, make the malware a significant threat to organisations’ digital environments.

Activity Details and Qakbot Delivery Methods

During the month of June, variationsof the following pattern of network activity were observed in several client networks:

1. User’s device contacts an email service such as outlook.office[.]com or mail.google[.]com

2. User’s device makes an HTTP GET request to 185.234.247[.]119 with an Office user-agent string and a ‘/123.RES' target URI. The request is responded to with an HTML file containing a exploit for the Follina vulnerability (CVE-2022-30190)

3. User’s device makes an HTTP GET request with a cURL User-Agent string and a target URI ending in ‘.dat’ to an unusual external endpoint. The request is responded to with a Qakbot DLL sample

4. User’s device contacts Qakbot Command and Control servers over ports such as 443, 995, 2222, and 32101

In some cases, only steps 1 and 4 were seen, and in other cases, only steps 1, 3, and 4 were seen. The different variations of the pattern correspond to different Qakbot delivery methods.

‍

‍*Figure 1: Geographic distribution of Darktrace clients affected by Qakbot*

Qakbot is known to be delivered via malicious email attachments [7]. The Qakbot infections observed across Darktrace’s client base during June were likely initiated through HTML smuggling — a method which consists in embedding malicious code into HTML attachments. Based on open-source reporting [8]-[14] and on observed patterns of network traffic, we assess with moderate to high confidence that the Qakbot infections observed across Darktrace’s client base during June 2022 were initiated via one of the following three methods:

User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a LNK file, which when opened, causes the user's device to make an external HTTP GET request with a cURL User-Agent string and a '.dat' target URI. If successful, the HTTP GET request is responded to with a Qakbot DLL.
User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a docx file, which when opened, causes the user's device to make an HTTP GET request to 185.234.247[.]119 with an Office user-agent string and a ‘/123.RES' target URI. If successful, the HTTP GET request is responded to with an HTML file containing a Follina exploit. The Follina exploit causes the user's device to make an external HTTP GET with a '.dat' target URI. If successful, the HTTP GET request is responded to with a Qakbot DL.
User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a Qakbot DLL and a LNK file, which when opened, causes the DLL to run.

The usage of these delivery methods illustrate how threat actors are adopting to a post-macro world [4], with their malware delivery techniques shifting from usage of macros-embedding Office documents to usage of container files, Windows Shortcut (LNK) files, and exploits for novel vulnerabilities.

The Qakbot infections observed across Darktrace’s client base did not only vary in terms of their delivery methods — they also differed in terms of their follow-up activities. In some cases, no follow-up activities were observed. In other cases, however, actors were seen leveraging Qakbot to exfiltrate data and to deliver follow-up payloads such as Cobalt Strike and Dark VNC. These follow-up activities were likely preparation for the deployment of ransomware. Darktrace’s early detection of Qakbot activity within client environments enabled security teams to take actions which likely prevented the deployment of ransomware.

Darktrace Coverage

Users’ interactions with malicious email attachments typically resulted in their devices making cURL HTTP GET requests with empty Host headers and target URIs ending in ‘.dat’ (such as as ‘/24736.dat’ and ‘/noFindThem.dat’) to rare, external endpoints. In cases where the Follina vulnerability is believed to have been exploited, users’ devices were seen making HTTP GET requests to 185.234.247[.]119 with a Microsoft Office User-Agent string before making cURL HTTP GET requests. The following Darktrace DETECT/Network models typically breached as a result of these HTTP activities:

Device / New User Agent
Anomalous Connection / New User Agent to IP Without Hostname
Device / New User Agent and New IP
Anomalous File / EXE from Rare External Location
Anomalous File / Numeric Exe Download

These DETECT models were able to capture the unusual usage of Office and cURL User-Agent strings on affected devices, as well as the downloads of the Qakbot DLL from rare external endpoints. These models look for unusual activity that falls outside a device’s usual pattern of behavior rather than for activity involving User-Agent strings, URIs, files, and external IPs which are known to be malicious.

When enabled, Darktrace RESPOND/Network autonomously intervened, taking actions such as ‘Enforce group pattern of life’ and ‘Block connections’ to quickly intercept connections to Qakbot infrastructure.

*Figure 2: This ‘New User Agent to IP Without Hostname’ model breach highlights an example of Darktrace’s detection of a device attempting to download a file containing a Follina exploit*

*Figure 3: This ‘New User Agent to IP Without Hostname’ model breach highlights an example of Darktrace’s detection of a device attempting to download Qakbot*

Figure 4: The Event Log for an infected device highlights the moment a connection to the endpoint outlook.office365[.]com was made. This was followed by an executable file transfer detection and use of a new User-Agent, curl/7.9.1

After installing Qakbot, users’ devices started making connections to Command and Control (C2) endpoints over ports such as 443, 22, 990, 995, 1194, 2222, 2078, 32101. Cobalt Strike and Dark VNC may have been delivered over some of these C2 connections, as evidenced by subsequent connections to endpoints associated with Cobalt Strike and Dark VNC. These C2 activities typically caused the following Darktrace DETECT/Network models to breach:

Anomalous Connection / Application Protocol on Uncommon Port
Anomalous Connection / Multiple Connections to New External TCP Port
Compromise / Suspicious Beaconing Behavior
Anomalous Connection / Multiple Failed Connections to Rare Endpoint
Compromise / Large Number of Suspicious Successful Connections
Compromise / Sustained SSL or HTTP Increase
Compromise / SSL or HTTP Beacon
Anomalous Connection / Rare External SSL Self-Signed
Anomalous Connection / Anomalous SSL without SNI to New External
Compromise / SSL Beaconing to Rare Destination
Compromise / Suspicious TLS Beaconing To Rare External
Compromise / Slow Beaconing Activity To External Rare

*Figure 5: This Device Event Log illustrates the Command and Control activity displayed by a Qakbot-infected device*

The Darktrace DETECT/Network models which detected these C2 activities do not look for devices making connections to known, malicious endpoints. Rather, they look for devices deviating from their ordinary patterns of activity, making connections to external endpoints which internal devices do not usually connect to, over ports which devices do not normally connect over.

In some cases, actors were seen exfiltrating data from Qakbot-infected systems and dropping Cobalt Strike in order to conduct extensive discovery. These exfiltration activities typically caused the following models to breach:

Anomalous Connection / Data Sent to Rare Domain
Unusual Activity / Enhanced Unusual External Data Transfer
Anomalous Connection / Uncommon 1 GiB Outbound
Anomalous Connection / Low and Slow Exfiltration to IP
Unusual Activity / Unusual External Data to New Endpoints

The reconnaissance and brute-force activities carried out by actors typically resulted in breaches of the following models:

Device / ICMP Address Scan
Device / Network Scan
Anomalous Connection / SMB Enumeration
Device / New or Uncommon WMI Activity
Unusual Activity / Possible RPC Recon Activity
Device / Possible SMB/NTLM Reconnaissance
Device / SMB Lateral Movement
Device / Increase in New RPC Services
Device / Spike in LDAP Activity
Device / Possible SMB/NTLM Brute Force
Device / SMB Session Brute Force (Non-Admin)
Device / SMB Session Brute Force (Admin)
Device / Anomalous NTLM Brute Force

Conclusion

June 2022 saw Qakbot swiftly mould itself in response to Microsoft's default blocking of macros and the public disclosure of the Follina vulnerability. The evolution of the threat landscape in the first half of 2022 caused Qakbot to undergo changes in its delivery methods, shifting from delivery via macros-based methods to delivery via HTML smuggling methods. The effectiveness of these novel delivery methods where highlighted in Darktrace's client base, where large volumes of Qakbot infections were seen during June 2022. Leveraging Self-Learning AI, Darktrace DETECT/Network was able to detect the unusual network behaviors which inevitably resulted from these novel Qakbot infections. Given that the actors behind these Qakbot infections were likely seeking to deploy ransomware, these detections, along with Darktrace RESPOND/Network’s autonomous interventions, ultimately helped to protect affected Darktrace clients from significant business disruption.

Appendices

List of IOCs

References

[1] https://techcommunity.microsoft.com/t5/excel-blog/excel-4-0-xlm-macros-now-restricted-by-default-for-customer/ba-p/3057905

[2] https://techcommunity.microsoft.com/t5/microsoft-365-blog/helping-users-stay-safe-blocking-internet-macros-by-default-in/ba-p/3071805

[3] https://learn.microsoft.com/en-us/deployoffice/security/internet-macros-blocked

[4] https://www.proofpoint.com/uk/blog/threat-insight/how-threat-actors-are-adapting-post-macro-world

[5] https://twitter.com/nao_sec/status/1530196847679401984

[6] https://www.microsoft.com/security/blog/2021/12/09/a-closer-look-at-qakbots-latest-building-blocks-and-how-to-knock-them-down/

[7] https://www.zscaler.com/blogs/security-research/rise-qakbot-attacks-traced-evolving-threat-techniques

[8] https://www.esentire.com/blog/resurgence-in-qakbot-malware-activity

[9] https://www.fortinet.com/blog/threat-research/new-variant-of-qakbot-spread-by-phishing-emails

[10] https://twitter.com/pr0xylife/status/1539320429281615872

[11] https://twitter.com/max_mal_/status/1534220832242819072

[12] https://twitter.com/1zrr4h/status/1534259727059787783?lang=en

[13] https://isc.sans.edu/diary/rss/28728

[14] https://www.fortiguard.com/threat-signal-report/4616/qakbot-delivered-through-cve-2022-30190-follina

Credit to: Hanah Darley, Cambridge Analyst Team Lead and Head of Threat Research and Sam Lister, Senior Cyber Analyst

執筆者

Nahisha Nobregas

SOC Analyst

Inside the SOC

Written by

Nahisha Nobregas

SOC Analyst

Malware Research Lead

Watch the NIS2 Webinar

Blog

Network

May 14, 2026

Chinese APT Campaign Targets Entities with Updated FDMTP Backdoor

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.

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.

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

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.

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.

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.

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.

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.

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”.

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.

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)

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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

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

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.

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

About the author

Irving Bruckstein

CEO CyberAIgroup

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唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ

デモを予約

Qakbot Resurgence in the Cyber Landscape

Qakbot Background

Activity Details and Qakbot Delivery Methods

Darktrace Coverage

Conclusion

Appendices

List of IOCs

References

Chinese APT Campaign Targets Entities with Updated FDMTP Backdoor

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

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More in this series

Chinese APT Campaign Targets Entities with Updated FDMTP Backdoor

When Trust Becomes the Attack Surface: Supply-Chain Attacks in an Era of Automation and Implicit Trust

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

Blog

Network

May 14, 2026

Chinese APT Campaign Targets Entities with Updated FDMTP Backdoor

Observed Campaign

What Darktrace Observed

Technical Analysis

Initial staging and execution

C2 Registration

Staged payload retrieval

ClickOnce and AppDomain hijacking

Core payload

Modular framework and plugins

Plugins observed

Conclusion

Appendices

Indicators of Compromise (IoCs)

MITRE ATT&CK Techniques

Darktrace Model Alerts

Blog

AI

May 12, 2026

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

Why higher education is a different cybersecurity battlefield

Discovering a force multiplier

Revealing the hidden threat other tools missed

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

Peace of mind isn’t about complacency

The next era: AI vs. AI

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