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December 22, 2021

9 Stages of Ransomware & How AI Responds

Discover the 9 stages of ransomware attacks and how AI responds at each stage. Learn how you can protect your business from cyber threats.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Dan Fein
VP, Product
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22
Dec 2021

Ransomware gets its name by commandeering and holding assets ransom, extorting their owner for money in exchange for discretion and full cooperation in returning exfiltrated data and providing decryption keys to allow business to resume.

Average ransom demands are skyrocketing, rising to $5.3 million in 2021, a 518% increase from the previous year. But the cost of recovering from a ransomware attack typically far exceeds the ransom payments: the average downtime after a ransomware attack is 21 days; and 66% of ransomware victims report a significant loss of revenue following a successful attack.

In this series, we break down this huge topic step by step. Ransomware is a multi-stage problem, requiring a multi-stage solution that autonomously and effectively contains the attack at any stage. Read on to discover how Self-Learning AI and Autonomous Response stops ransomware in its tracks.

1. Initial intrusion (email)

Initial entry – the first stage of a ransomware attack – can be achieved through RDP brute-forcing (exposed Internet service), malicious websites and drive-by downloads, an insider threat with company credentials, system and software vulnerabilities, or any number of other attack vectors.

But the most common initial attack vector is email. An organization’s biggest security weakness is often their people – and attackers are good at finding ways of exploiting this. Well-researched, targeted, legitimate-looking emails are aimed at employees attempting to solicit a reaction: a click of a link, an opening of an attachment, or persuading them to divulge credentials or other sensitive information.

Gateways: Stops what has been seen before

Most conventional email tools rely on past indicators of attack to try and spot the next threat. If an email comes in from a blocklisted IP address or email domain, and uses known malware that has previously been seen in the wild, the attack may be blocked.

But the reality is, attackers know the majority of defenses take this historical approach, and so constantly update their attack infrastructure to bypass these tools. By buying new domains for a few pennies each, or creating bespoke malware with just small adaptions to the code, they can outpace and outsmart the legacy approach taken by a typical email gateway.

Real-world example: Supply chain phishing attack

By contrast, Darktrace’s evolving understanding of ‘normal’ for every email user in the organization enables it to detect subtle deviations that point to a threat – even if the sender or any malicious contents of the email are unknown to threat intelligence. This is what enabled the technology to stop an attack that recently targeted McLaren Racing, with emails sent to a dozen employees in the organization each containing a malicious link. This possible precursor to ransomware bypassed conventional email tools – largely because it was sent from a known supplier – however Darktrace recognized the account hijack and held the email back.

Figure 1: A snapshot of Darktrace’s Threat Visualizer surfacing the malicious email

Read the full case study

2. Initial intrusion (server-side)

With organizations rapidly expanding their Internet-facing perimeter, this increased attack surface has paved the way for a surge in brute-force and server-side attacks.

A number of vulnerabilities against such Internet-facing servers and systems have been disclosed this year, and for attackers, targeting and exploiting public-facing infrastructure is easier than ever – scanning the Internet for vulnerable systems is made simple with tools like Shodan or MassScan.

Attackers may also achieve initial intrusion via RDP brute-forcing or stolen credentials, with attackers often reusing legitimate credentials from previous data dumps. This has much higher precision and is less noisy than a classic brute-force attack.

A lot of ransomware attacks use RDP as an entry vector. This is part of a wider trend of ‘Living off the Land’: using legitimate off-the-shelf tools (abusing RDP, SMB1 protocol, or various command line tools WMI or Powershell) to blur detection and attribution by blending in with typical administrator activity. Ensuring that backups are isolated, configurations are hardened, and systems are patched is not enough – real-time detection of every anomalous action is needed.

Antivirus, firewalls and SIEMs

In cases of malware downloads, endpoint antivirus will detect these if, and only if, the malware has been seen and fingerprinted before. Firewalls typically require configuration on a per-organization basis, and often need to be modified based on the needs of the business. If the attack hits the firewall where a rule or signature does not match it, again, it will bypass the firewall.

SIEM and SOAR tools also look for known malware being downloaded, leverage pre-programmed rules and use pre-programmed responses. While these tools do look for patterns, these patterns are defined in advance, and this approach relies on a new attack to have sufficiently similar traits to attacks that have been seen before.

Real-world example: Dharma ransomware

Darktrace detected a targeted Dharma ransomware attack against a UK organization exploiting an open RDP connection through Internet-facing servers. The RDP server began receiving a large number of incoming connections from rare IP addresses on the Internet. It is highly likely that the RDP credential used in this attack had been compromised at a previous stage – either via common brute-force methods, credential stuffing attacks, or phishing. Indeed, a technique growing in popularity is to buy RDP credentials on marketplaces and skip to initial access.

Figure 2: The model breaches that fired over the course of this attack, including anomalous RDP activity

Unfortunately, in this case, without Autonomous Response installed, the Dharma ransomware attack continued until its final stages, where the security team were forced into the heavy-handed and disruptive action of pulling the plug on the RDP server midway through encryption.

Read the full case study

3. Establish foothold and C2

Whether through a successful phish, a brute-force attack, or some other method, the attacker is in. Now, they make contact with the breached device(s) and establish a foothold.

This stage allows attackers to control subsequent stages of the attack remotely. During these command and control (C2) communications, further malware may also pass from the attacker to the devices. This helps them to establish an even greater foothold within the organization and readies them for lateral movement.

Attackers can adapt malware functionality with an assortment of ready-made plug-ins, allowing them to lie low inside the business undetected. More modern and sophisticated ransomware is able to adapt by itself to the surrounding environment, and operate autonomously, blending in to regular activity even when cut off from its command and control server. These ‘self-sufficient’ ransomware strains pose a big problem for traditional defenses reliant on stopping threats solely on the grounds of its malicious external connections.

Viewing connections in isolation vs understanding the business

Conventional security tools like IDS and firewalls tend to look at connections in isolation rather than in the context of previous and potentially relevant connections, making command and control very difficult to spot.

IDS and firewalls may block ‘known-bad’ domains or use some geo-blocking, but this is where an attacker would likely leverage new infrastructure.

These tools also don’t tend to analyze for things like the periodicity, such as whether a connection is beaconing at a regular or irregular interval, or the age and rarity of the domain in the context of the environment.

With Darktrace’s evolving understanding of the digital enterprise, suspicious C2 connections and the downloads which follow them are spotted, even when conducted using regular programs or methods. The AI technology correlates multiple subtle signs of threat – a small subset of which includes anomalous connections to young and/or unusual endpoints, anomalous file downloads, incoming remote desktop, and unusual data uploads and downloads.

Once they are detected as a threat, Darktrace's Autonomous Response halts these connections and downloads, while allowing normal business activity to continue.

Real-world example: WastedLocker attack

When a WastedLocker ransomware attack hit a US agricultural organization, Darktrace immediately detected the initial unusual SSL C2 activity (based on a combination of destination rarity, JA3 unusualness and frequency analysis). Antigena (on this occasion configured in passive mode, and therefore not granted permission to take autonomous action) suggested instantly blocking the C2 traffic on port 443 and parallel internal scanning on port 135.

Figure 3: The Threat Visualizer reveals the action Antigena would have taken

When beaconing was later observed to bywce.payment.refinedwebs[.]com, this time over HTTP to /updateSoftwareVersion, Antigena escalated its response by blocking the further C2 channels.

Figure 4: Antigena escalates its response

Read the full case study

4. Lateral movement

Once an attacker has established a foothold within an organization, they begin to increase their knowledge of the wider digital estate and their presence within it. This is how they will find and access the files which they will ultimately attempt to exfiltrate and encrypt. It begins reconnaissance: scanning the network; building up a picture of its component devices; identifying the location of the most valuable assets.

Then the attacker begins moving laterally. They infect more devices and look to escalate their privileges – for instance, by obtaining admin credentials – thereby increasing their control over the environment. Once they have obtained authority and presence within the digital estate, they can progress to the final stages of the attack.

Modern ransomware has built-in functions that allow it to search automatically for stored passwords and spread through the network. More sophisticated strains are designed to build themselves differently in different environments, so the signature is constantly changing and it’s harder to detect.

Legacy tools: A blunt response to known threats

Because they rely upon static rules and signatures, legacy solutions struggle to prevent lateral movement and privilege escalation without also impeding essential business operations. Whilst in theory, an organization leveraging firewalls and NAC internally with proper network segmentation and a perfect configuration could prevent cross-network lateral movement, maintaining a perfect balance between protective and disruptive controls is near impossible.

Some organizations rely on Intrusion Prevent Systems (IPS) to deny network traffic when known threats are detected in packets, but as with previous stages, novel malware will evade detection, and this requires the database to be constantly updated. These solutions also sit at the ingress/egress points, limiting their network visibility. An Intrusion Detection System (IDS) may sit out-of-line, but doesn’t have response capabilities.

A self-learning approach

Darktrace’s AI learns ‘self’ for the organization, enabling it to detect suspicious activity indicative of lateral movement, regardless of whether the attacker uses new infrastructure or ‘lives off the land’. Potential unusual activity that Darktrace detects includes unusual scanning activity, unusual SMB, RDP, and SSH activity. Other models that fire at this stage include:

  • Suspicious Activity on High-Risk Device
  • Numeric EXE in SMB Write
  • New or Uncommon Service Control

Autonomous Response then takes targeted action to stop the threat at this stage, blocking anomalous connections, enforcing the infected device’s ‘pattern of life’, or enforcing the group ‘pattern of life’ – automatically clustering devices into peer groups and preventing a device from doing anything its peer group hasn’t done.

Where malicious behavior persists, and only if necessary, Darktrace will quarantine an infected device.

Real-world example: Unusual chain of RDP connections

At an organization in Singapore, one compromised server led to the creation of a botnet, which began moving laterally, predominantly by establishing chains of unusual RDP connections. The server then started making external SMB and RPC connections to rare endpoints on the Internet, in an attempt to find further vulnerable hosts.

Other lateral movement activities detected by Darktrace included the repeated failing attempts to access multiple internal devices over the SMB file-sharing protocol with a range of different usernames, implying brute-force network access attempts.

Figure 5: Darktrace’s Cyber AI Analyst reveals suspicious TCP scanning followed by a suspicious chain of administrative RDP connections

Read the full case study

5. Data exfiltration

In the past, ransomware was simply about encrypting an operating system and network files.

In a modern attack, as organizations insure against malicious encryption by becoming increasingly diligent with data backups, threat actors have moved towards ‘double extortion’, where they exfiltrate key data and destroy backups before the encryption takes place. Exfiltrated data is used to blackmail organizations, with attackers threatening to publish sensitive information online or sell it on to the organization’s competitors if they are not paid.

Modern ransomware variants also look for cloud file storage repositories such as Box, Dropbox, and others.

Many of these incidents aren’t public, because if IP is stolen, organizations are not always legally required to disclose it. However, in the case of customer data, organizations are obligated by law to disclose the incident and face the additional burden of compliance files – and we’ve seen these mount in recent years (Marriot, $23.8 million; British Airways, $26 million; Equifax, $575 million). There’s also the reputational blow associated with having to inform customers that a data breach has occurred.

Legacy tools: The same old story

For those that have been following, the narrative by now will sound familiar: to stop a ransomware attack at this stage, most defenses rely on either pre-programmed definitions of 'bad' or have rules constructed to combat different scenarios put organizations in a risky, never-ending game of cat and mouse.

A firewall and proxy might block connections based on pre-programmed policies based on specific endpoints or data volumes, but it’s likely an attacker will ‘live off the land’ and utilize a service that is generally allowed by the business.

The effectiveness of these tools will vary according to data volumes: they might be effective for ‘smash and grab’ attacks using known malware, and without employing any defense evasion techniques, but are unlikely to spot ‘low and slow’ exfiltration and novel or sophisticated strains.

On the other hand, because by nature it involves a break from expected behavior, even less conspicuous, low and slow data exfiltration is detected by Darktrace and stopped with Darktrace's Autonomos Response. No confidential files are lost, and attackers are unable to extort a ransom payment through blackmail.

Real-world example: Unusual chain of RDP connections

It becomes more difficult to find examples of Darktrace stopping ransomware at these later stages, as the threat is usually contained before it gets this far. This is the double-edged sword of effective security – early containment makes for bad storytelling! However, we can see the effects of a double extortion ransomware attack on an energy company in Canada. The organization had the Enterprise Immune System but no Antigena, and without anyone actively monitoring Darktrace’s AI detections, the attack was allowed to unfold.

The attacker managed to connect to an internal file server and download 1.95TB of data. The device was also seen downloading Rclone software – an open-source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000. As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.

Read the full details of the attack

It should be noted that the exact order of the stages 3–5 above is not set in stone, and varies according to attack. Sometimes data is exfiltrated and then there is further lateral movement, and additional C2 beaconing. This entire period is known as the ‘dwell time’. Sometimes it takes place over only a few days, other times attackers may persist for months, slowly gathering more intel and exfiltrating data in a ‘low and slow’ fashion so as to avoid detection from rule-based tools that are configured to flag any single data transfer over a certain threshold. Only through a holistic understanding of malicious activity over time can a technology spot this level of activity and allow the security team to remove the threat before it reaches the latter and most damaging stages of ransomware.

6. Data encryption

Using either symmetric encryption, asymmetric encryption, or a combination of the two, attackers attempt to render as much data unusable in the organization’s network as they can before the attack is detected.

As the attackers alone have access to the relevant decryption keys, they are now in total control of what happens to the organization’s data.

Pre-programmed response and disruption

There are many families of tools that claim to stop encryption in this manner, but each contain blind spots which enable a sophisticated attacker to evade detection at this crucial stage. Where they do take action, it is often highly disruptive, causing major shutdowns and preventing a business from continuing its usual operations.

Internal firewalls prevent clients from accessing servers, so once an attacker has penetrated to servers using any of the techniques outlined above, they have complete freedom to act as they want.

Similarly, antivirus tools look only for known malware. If the malware has not been detected until this point, it is highly unlikely the antivirus will act here.

Stopping encryption autonomously

Even if familiar tools and methods are used to conduct it, Autonomous Response can enforce the normal ‘pattern of life’ for devices attempting encryption, without using static rules or signatures. This action can be taken independently or via integrations with native security controls, maximizing the return on other security investments. With a targeted Autonomous Response, normal business operations can continue while encryption is prevented.

7. Ransom note

It is important to note that in the stages before encryption, this ransomware attack is not yet “ransomware”. Only at this stage does it gets its name.

A ransom note is deployed. The attackers request payment in return for a decryption key and threaten the release of sensitive exfiltrated data. The organization must decide whether to pay the ransom or lose their data, possibly to their competition or the public. The average demand made by ransomware threat actors rose in 2021 to $5.3 million, with meat processing company JBS paying out $11 million and DarkSide receiving over $90 million in Bitcoin payments following the Colonial Pipeline incident.

All of the stages up until this point represent a typical, traditional ransomware attack. But ransomware is shifting from indiscriminate encryption of devices to attackers targeting business disruption in general, using multiple techniques to hold their victims to ransom. Additional methods of extortion include not only data exfiltration, but corporate domain hijack, deletion or encryption of backups, attacks against systems close to industrial control systems, targeting company VIPs… the list goes on.

Sometimes, attackers will just skip straight from stage 2 to 6 and jump straight to extortion. Darktrace recently stopped an email attack which showed an attacker bypassing the hard work and attempting to jump straight to extortion in an email. The attacker claimed to have compromised the organization’s sensitive data, requesting payment in bitcoin for its same return. Whether or not the claims were true, this attack shows that encryption is not always necessary for extortion, and this type of harassment exists in multiple forms.

Figure 6: Darktrace holds back the offending email, protecting the recipient and organization from harm

As with the email example we explored in the first post of this series, Darktrace/Email was able to step in and stop this email where other email tools would have let it through, stopping this potentially costly extortion attempt.

Whether through encryption or some other kind of blackmail, the message is the same every time. Pay up, or else. At this stage, it’s too late to start thinking about any of the options described above that were available to the organization, that would have stopped the attack in its earliest stages. There is only one dilemma. “To pay or not to pay” – that is the question.

Often, people believe their payment troubles are over after the ransom payment stage, but unfortunately, it’s just beginning to scratch the surface…

8. Clean-up

Efforts are made to try to secure the vulnerabilities which allowed the attack to happen initially – the organization should be conscious that approximately 80% of ransomware victims will in fact be targeted again in the future.

Legacy tools largely fail to shed light on the vulnerabilities which allowed the initial breach. Like searching for a needle in an incomplete haystack, security teams will struggle to find useful information within the limited logs offered by firewalls and IDSs. Antivirus solutions may reveal some known malware but fail to spot novel attack vectors.

With Darktrace’s Cyber AI Analyst, organizations are given full visibility over every stage of the attack, across all coverage areas of their digital estate, taking the mystery out of ransomware attacks. They are also able to see the actions that would have been taken to halt the attack by Darktrace RESPOND.

9. Recovery

The organization begins attempts to return its digital environment to order. Even if it has paid for a decryption key, many files may remain encrypted or corrupted. Beyond the costs of the ransom payment, network shutdowns, business disruption, remediation efforts, and PR setbacks all incur hefty financial losses.

The victim organization may also suffer additional reputation costs, with 66% of victims reporting a significant loss of revenue following a ransomware attack, and 32% reporting losing C-level talent as a direct result from ransomware.

Conclusion

While the high-level stages described above are common in most ransomware attacks, the minute you start looking at the details, you realize every ransomware attack is different.

As many targeted ransomware attacks come through ransomware affiliates, the Tools, Techniques and Procedures (TTPs) displayed during intrusions vary widely, even when the same ransomware malware is used. This means that even comparing two different ransomware attacks using the same ransomware family, you are likely to encounter completely different TTPs. This makes it impossible to predict what tomorrow’s ransomware will look like.

This is the nail in the coffin for traditional tooling which is based on historic attack data. The above examples demonstrate that Self-Learning technology and Autonomous Response is the only solution that stops ransomware at every stage, across email and network.

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

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May 14, 2026

Chinese APT Campaign Targets Entities with Updated FDMTP Backdoor

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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

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About the author
Tara Gould
Malware Research Lead

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AI

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May 12, 2026

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

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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.

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About the author
Irving Bruckstein
CEO CyberAIgroup
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