Blog
/
Network
/
January 31, 2024

How Darktrace Defeated SmokeLoader Malware

Read how Darktrace's AI identified and neutralized SmokeLoader malware. Gain insights into their proactive approach to cybersecurity.
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
Patrick Anjos
Senior Cyber Analyst
Default blog image
31
Jan 2024

What is Loader Malware?

Loader malware is a type of malicious software designed primarily to infiltrate a system and then download and execute additional malicious payloads.

In recent years, loader malware has emerged as a significant threat for organizations worldwide. This trend is expected to continue given the widespread availability of many loader strains within the Malware-as-a-Service (MaaS) marketplace. The MaaS marketplace contains a wide variety of innovative strains which are both affordable, with toolkits ranging from USD 400 to USD 1,650 [1], and continuously improving, aiming to avoid traditional detection mechanisms.

SmokeLoader is one such example of a MaaS strain that has been observed in the wild since 2011 and continues to pose a significant threat to organizations and their security teams.

How does SmokeLoader Malware work?

SmokeLoader’s ability to drop an array of different malware strains onto infected systems, from backdoors, ransomware, cryptominers, password stealers, point-of-sale malware and banking trojans, means its a highly versatile loader that has remained consistently popular among threat actors.

In addition to its versatility, it also exhibits advanced evasion strategies that make it difficult for traditional security solutions to detect and remove, and it is easily distributed via methods like spam emails or malicious file downloads.

Between July and August 2023, Darktrace observed an increasing trend in SmokeLoader compromises across its customer base. The anomaly-based threat detection capabilities of Darktrace, coupled with the autonomous response technology, identified and contained the SmokeLoader infections in their initial stages, preventing attackers from causing further disruption by deploying other malicious software or ransomware.

SmokeLoader Malware Attack Details

PROPagate Injection Technique

SmokeLoader utilizes the PROPagate code injection technique, a less common method that inserts malicious code into existing processes in order to appear legitimate and bypass traditional signature-based security measures [2] [3]. In the case of SmokeLoader, this technique exploits the Windows SetWindowsSubclass function, which is typically used to add or change the behavior of Windows Operation System. By manipulating this function, SmokeLoader can inject its code into other running processes, such as the Internet Explorer. This not only helps to disguise  the malware's activity but also allows attackers to leverage the permissions and capabilities of the infected process.

Obfuscation Methods

SmokeLoader is known to employ several obfuscation techniques to evade the detection and analysis of security teams. The techniques include scrambling portable executable files, encrypting its malicious code, obfuscating API functions and packing, and are intended to make the malware’s code appear harmless or unremarkable to antivirus software. This allows attackers to slip past defenses and execute their malicious activities while remaining undetected.

Infection Vector and Communication

SmokeLoader typically spreads via phishing emails that employ social engineering tactics to convince users to unknowingly download malicious payloads and execute the malware. Once installed on target networks, SmokeLoader acts as a backdoor, allowing attackers to control infected systems and download further malicious payloads from command-and-control (C2) servers. SmokeLoader uses fast flux, a DNS technique utilized by botets whereby IP addresses associated with C2 domains are rapidly changed, making it difficult to trace the source of the attack. This technique also boosts the resilience of attack, as taking down one or two malicious IP addresses will not significantly impact the botnet's operation.

Continuous Evolution

As with many MaaS strains, SmokeLoader is continuously evolving, with its developers regularly adding new features and techniques to increase its effectiveness and evasiveness. This includes new obfuscation methods, injection techniques, and communication protocols. This constant evolution makes SmokeLoader a significant threat and underscores the importance of advanced threat detection and response capabilities solution.

Darktrace’s Coverage of SmokeLoader Attack

Between July and August 2023, Darktrace detected one particular SmokeLoader infection at multiple stages of its kill chain on a customer network. This detection was made possible by Darktrace DETECT’s anomaly-based approach and Self-Learning AI that allows it to identify subtle deviations in device behavior.

One of the key components of this process is the classification of endpoint rarity and determining whether an endpoint is new or unusual for any given network. This classification is applied to various aspects of observed endpoints, such as domains, IP addresses, or hostnames within the network. It thereby plays a vital role in identifying SmokeLoader activity, such as the initial infection vector or C2 communication, which typically involve a device contacting a malicious endpoint associated with SmokeLoader.

The First Signs of Infection SmokeLoader Infection

Beginning in July 2023, Darktrace observed a surge in suspicious activities that were assessed with moderate to high confidence to be associated with SmokeLoader malware.

For example on July 30, a device was observed making a successful HTTPS request to humman[.]art, a domain that had never been seen on the network, and therefore classified as 100% rare by DETECT. During this connection, the device in question received a total of 6.0 KiB of data from the unusual endpoint. Open-source intelligence (OSINT) sources reported with high confidence that this domain was associated with the SmokeLoader C2 botnet.

The device was then detected making an HTTP request to another 100% rare external IP, namely 85.208.139[.]35, using a new user agent. This request contained the URI ‘/DefenUpdate.exe’, suggesting a possible download of an executable (.exe) file. This was corroborated by the total amount of data received in this connection, 4.3 MB. Both the file name and its size suggest that the offending device may have downloaded additional malicious tooling from the SmokeLoader C2 endpoint, such as a trojan or information stealer, as reported on OSINT platforms [4].

Figure 1: Device event log showing the moment when a device made its first connection to a SmokeLoader associated domain, and the use of a new user agent. A few seconds later, the DETECT model “Anomalous Connection / New User Agent to IP Without Hostname” breached.

The observed new user agent, “Mozilla/5.0 (Windows NT 10.0; Win64; x64; Trident/7.0; rv:11.0) like Gecko” was identified as suspicious by Darktrace leading to the “Anomalous Connection / New User Agent to IP Without Hostname” DETECT model breach.

As this specific user agent was associated with the Internet Explorer browser running on Windows 10, it may not have appeared suspicious to traditional security tools. However, Darktrace’s anomaly-based detection allows it to identify and mitigate emerging threats, even those that utilize sophisticated evasion techniques.

This is particularly noteworthy in this case because, as discussed earlier, SmokeLoader is known to inject its malicious code into legitimate processes, like Internet Explorer.

Figure 2: Darktrace detecting the affected device leveraging a new user agent and establishing an anomalous HTTP connection with an external IP, which was 100% rare to the network.

C2 Communication

Darktrace continued to observe the device making repeated connections to the humman[.]art endpoint. Over the next few days. On August 7, the device was observed making unusual POST requests to the endpoint using port 80, breaching the ‘Anomalous Connection / Multiple HTTP POSTs to Rare Hostname’ DETECT model. These observed POST requests were observed over a period of around 10 days and consisted of a pattern of 8 requests, each with a ten-minute interval.

Figure 3: Model Breach Event Log highlighting the Darktrace DETECT model breach ‘Anomalous Connection / Multiple HTTP POSTs to Rare Hostname’.

Upon investigating the details of this activity identified by Darktrace DETECT, a particular pattern was observed in these requests: they used the same user-agent, “Mozilla/5.0 (Windows NT 10.0; Win64; x64; Trident/7.0; rv:11.0) like Gecko”, which was previously detected in the initial breach.

Additionally, they the requests had a constantly changing  eferrer header, possibly using randomly generated domain names for each request. Further examination of the packet capture (PCAP) from these requests revealed that the payload in these POST requests contained an RC4 encrypted string, strongly indicating SmokeLoader C2 activity.

Figure4: Advanced Search results display an unusual pattern in the requests made by the device to the hostname humman[.]art. This pattern shows a constant change in the referrer header for each request, indicating anomalous behavior.
Figure 5: The PCAP shows the payload seen in these POST requests contained an RC4 encrypted string strongly indicating SmokeLoader C2 activity.  

Unfortunately in this case, Darktrace RESPOND was not active on the network meaning that the attack was able to progress through its kill chain. Despite this, the timely alerts and detailed incident insights provided by Darktrace DETECT allowed the customer’s security team to begin their remediation process, implementing blocks on their firewall, thus preventing the SmokeLoader malware from continuing its communication with C2 infrastructure.

Darktrace RESPOND Halting Potential Threats from the Initial Stages of Detection

With Darktrace RESPOND, organizations can move beyond threat detection to proactive defense against emerging threats. RESPOND is designed to halt threats as soon as they are identified by DETECT, preventing them from escalating into full-blown compromises. This is achieved through advanced machine learning and Self-Learning AI that is able to understand  the normal ‘pattern of life’ of customer networks, allowing for swift and accurate threat detection and response.

One pertinent example was seen on July 6, when Darktrace detected a separate SmokeLoader case on a customer network with RESPOND enabled in autonomous response mode. Darktrace DETECT initially identified a string of anomalous activity associated with the download of suspicious executable files, triggering the ‘Anomalous File / Multiple EXE from Rare External Locations’ model to breach.

The device was observed downloading an executable file (‘6523.exe’ and ‘/g.exe’) via HTTP over port 80. These downloads originated from endpoints that had never been seen within the customer’s environment, namely ‘hugersi[.]com’ and ‘45.66.230[.]164’, both of which had strongly been linked to SmokeLoader by OSINT sources, likely indicating the initial infection stage of the attack [5].

Figure 6: This figure illustrates Darktrace DETECT observing a device downloading multiple .exe files from rare endpoints and the associated model breach, ‘Anomalous File / Multiple EXE from Rare External Locations’.

Around the same time, Darktrace also observed the same device downloading an unusual file with a numeric file name. Threat actors often employ this tactic in order to avoid using file name patterns that could easily be recognized and blocked by traditional security measures; by frequently changing file names, malicious executables are more likely to remain undetected.

Figure 7: Graph showing the unusually high number of executable files downloaded by the device during the initial infection stage of the attack. The orange and red circles represent the number of model breaches that the device made during the observed activity related to SmokeLoader infection.
Figure 8: This figure illustrates the moment when Darktrace DETECT identified a suspicious download with a numeric file name.

With Darktrace RESPOND active and enabled in autonomous response mode, the SmokeLoader infection was thwarted in the first instance. RESPOND took swift autonomous action by blocking connections to the suspicious endpoints identified by DETECT, blocking all outgoing traffic, and enforcing a pre-established “pattern of life” on offending devices. By enforcing a patten of life on a device, Darktrace RESPOND ensures that it cannot deviate from its ‘normal’ activity to carry out potentially malicious activity, while allowing the device to continue expected business operations.

Figure 9:  A total of 8 RESPOND actions were applied, including blocking connections to suspicious endpoints and domains associated with SmokeLoader.

In addition to the autonomous mitigative actions taken by RESPOND, this customer also received a Proactive Threat Notification (PTN) informing them of potentially malicious activity on their network. This prompted the Darktrace Security Operations Center (SOC) to investigate and document the incident, allowing the customer’s security team to shift their focus to remediating and removing the threat of SmokeLoader.

Conclusion

Ultimately, Darktrace showcased its ability to detect and contain versatile and evasive strains of loader malware, like SmokeLoader. Despite its adeptness at bypassing conventional security tools by frequently changing its C2 infrastructure, utilizing existing processes to infect malicious code, and obfuscating malicious file and domain names, Darktrace’s anomaly-based approach allowed it to recognize such activity as deviations from expected network behavior, regardless of their apparent legitimacy.

Considering SmokeLoader’s wide array of functions, including C2 communication that could be used to facilitate additional attacks like exfiltration, or even the deployment of information-stealers or ransomware, Darktrace proved to be crucial in safeguarding customer networks. By identifying and mitigating SmokeLoader at the earliest possible stage, Darktrace effectively prevented the compromises from escalating into more damaging and disruptive compromises.

With the threat of loader malware expected to continue growing alongside the boom of the MaaS industry, it is paramount for organizations to adopt proactive security solutions, like Darktrace DETECT+RESPOND, that are able to make intelligent decisions to identify and neutralize sophisticated attacks.

Credit to Patrick Anjos, Senior Cyber Analyst, Justin Torres, Cyber Analyst

Appendices

Darktrace DETECT Model Detections

- Anomalous Connection / New User Agent to IP Without Hostname

- Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

- Anomalous File / Multiple EXE from Rare External Locations

- Anomalous File / Numeric File Download

List of IOCs (IOC / Type / Description + Confidence)

- 85.208.139[.]35 / IP / SmokeLoader C2 Endpoint

- 185.174.137[.]109 / IP / SmokeLoader C2 Endpoint

- 45.66.230[.]164 / IP / SmokeLoader C2 Endpoint

- 91.215.85[.]147 / IP / SmokeLoader C2 Endpoint

- tolilolihul[.]net / Hostname / SmokeLoader C2 Endpoint

- bulimu55t[.]net / Hostname / SmokeLoader C2 Endpoint

- potunulit[.]org / Hostname / SmokeLoader C2 Endpoint

- hugersi[.]com / Hostname / SmokeLoader C2 Endpoint

- human[.]art / Hostname / SmokeLoader C2 Endpoint

- 371b0d5c867c2f33ae270faa14946c77f4b0953 / SHA1 / SmokeLoader Executable

References:

[1] https://bazaar.abuse.ch/sample/d7c395ab2b6ef69210221337ea292e204b0f73fef8840b6e64ab88595eda45b3/#intel

[2] https://malpedia.caad.fkie.fraunhofer.de/details/win.smokeloader

[3] https://www.darkreading.com/cyber-risk/breaking-down-the-propagate-code-injection-attack

[4] https://n1ght-w0lf.github.io/malware%20analysis/smokeloader/

[5] https://therecord.media/surge-in-smokeloader-malware-attacks-targeting-ukrainian-financial-gov-orgs

MITRE ATT&CK Mapping

Model: Anomalous Connection / New User Agent to IP Without Hostname

ID: T1071.001

Sub technique: T1071

Tactic: COMMAND AND CONTROL

Technique Name: Web Protocols

Model: Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

ID: T1185

Sub technique: -

Tactic: COLLECTION

Technique Name: Man in the Browser

ID: T1071.001

Sub technique: T1071

Tactic: COMMAND AND CONTROL

Technique Name: Web Protocols

Model: Anomalous File / Multiple EXE from Rare External Locations

ID: T1189

Sub technique: -

Tactic: INITIAL ACCESS

Technique Name: Drive-by Compromise

ID: T1588.001

Sub technique: - T1588

Tactic: RESOURCE DEVELOPMENT

Technique Name: Malware

Model: Anomalous File / Numeric File Download

ID: T1189

Sub technique: -

Tactic: INITIAL ACCESS

Technique Name: Drive-by Compromise

ID: T1588.001

Sub technique: - T1588

Tactic: RESOURCE DEVELOPMENT

Technique Name: Malware

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
Patrick Anjos
Senior Cyber Analyst

More in this series

No items found.

Blog

/

Network

/

July 27, 2026

Hiding in Plain Sight: Uncovering a Multi-Stage Ransomware Attack Through Behavioral Detection

ransomware attackDefault blog imageDefault blog image

Why ransomware has changed

Ransomware attacks have continued to increase globally, with 698 incidents reported in May 2026, representing a 48% rise compared to 472 incidents in May 2025 [1]. At the same time, the ransomware landscape is evolving. Several major ransomware groups, including LockBit [2], have been disrupted by successful joint law enforcement operations, resulting in a shift away from a small number of dominant actors towards a more fragmented and distributed ecosystem. This is increasingly composed of smaller groups who play a specialized role in the attack, such as initial access brokers, affiliates or developers.

As a result, ransomware tactics, techniques, and procedures (TTPs) are becoming more diverse and less predictable. On top of this, adversaries are leveraging native tools and legitimate penetration testing frameworks to evade detection. Anomaly-based detection is therefore critical to identify pre-ransomware activity, rather than relying on signatures associated with a handful of well-known ransomware groups.

As these attacks often unfold over several days, there is a critical window for defenders to act. In this context, behavioral-based detection plays a vital role in identifying suspicious pre-ransomware activity, and enabling early intervention before encryption or exfiltration occurs.

Inside a modern ransomware intrusion

In early 2026, Darktrace detected activity within a customer’s environment related to a multi-stage ransomware intrusion from the initial compromise. This activity does not appear to be attributable to a specific ransomware group, and no known ransomware payload was observed until the final stage.

The attack aligns with a broader industry trend in which compromised virtual private network (VPN) credentials are used as an entry point, followed by rapid internal reconnaissance and lateral movement using legitimate administrative tools. This growing preference for native tools and legitimate frameworks in cyber-attacks illustrates that it is increasingly unreliable to depend solely on traditional indicators of compromise such as known malware signatures or exploit detection.

The intrusion also involved the use of Sliver, an open-source adversary emulation framework, which is increasingly observed in real-world attacks. Originally designed for penetration testing and red teaming, Sliver has gained traction among threat actors as a stealthier alternative to more heavily signatured frameworks such as Cobalt Strike. As a legitimate framework, its use further complicates detection for security tools that rely on known malicious signatures.

Darktrace’s detection of a ransomware event in a customer’s environment

The initial compromise appears to have occurred via compromised credentials used over the VPN shortly before, or at the onset of the first indicators of suspicious activity. While it remains unclear as to how or when the threat actors gained access to these credentials, the use of initial access brokers (IABs) is a common feature of modern ransomware operations. This suggests that access to the environment may have been established several days or weeks beforehand.

The intrusion unfolded over three days, presenting multiple opportunities for early detection and intervention before ransomware deployment. The attack progressed through a compressed but structured sequence: initial access and reconnaissance were completed within hours, followed by privilege escalation and lateral movement the next day, and culminating in data exfiltration and encryption shortly thereafter. Throughout each stage, distinct behavioral anomalies emerged across the network providing clear indicators of malicious activity well before the ransomware was deployed.

While Darktrace’s Autonomous Response capability was enabled within the customer’s environment, it was not fully configured across the impacted devices, allowing the attack to progress to ransomware deployment. Had Autonomous Response been fully deployed across the affected systems, it could have taken targeted action against the earliest stages of malicious activity, potentially disrupting the intrusion before it escalated.

Figure 1: Timeline of the attack progression.

Day 1: Reconnaissance and privilege escalation

The threat actor gained access via compromised VPN credentials and initiated internal reconnaissance. Darktrace detected anomalous scanning behavior, including unusual port scanning activity and widespread network enumeration.

Specifically, Darktrace detected a high volume of east-west scanning activity across a broad range of ports, with TCP connections targeting ports 21, 80, 445, 4899 and 8080. Associated URIs suggested the use of Nmap, a widely used penetration testing tool. This highlights how attackers often leverage legitimate penetration testing tools for malicious reconnaissance, enabling them to blend into normal network activity and evade traditional signature-based detection methods.

Figure 2: Darktrace's detection of a sharp increase in anomalous internal connections, triggering multiple high-severity model alerts associated with reconnaissance activity.

Several devices were observed using administrative credentials to carry out privileged actions in a manner that was highly anomalous for the environment. This activity was accompanied by behavior consistent with SMB authentication scanning, suggesting efforts to identify and access additional systems. As the activity intensified, an increasing number of devices became involved, signalling lateral movement and further spread across the network.

Darktrace also identified privilege escalation through active directory (AD) replication abuse, specifically via the drsuapi::DRSGetNCChanges function. This technique allows an attacker with sufficient privileges to request directory replication data from a domain controller (DC), enabling them to extract credentials, including password hashes, without directly interacting with user accounts. Commonly associated with ‘DCSync’ attacks, this technique is frequently used to obtain highly privileged credentials and enable further escalation within an environment.

Figure 3: Darktrace’s detection of anomalous AD replication activity indicative of privilege escalation.

This activity was seen alongside the use of the now obsolete SMBv1, repeated NTLM authentication attempts using multiple variations of ‘Administrator’ credentials, reverse DNS scanning, and large-scale network scanning. Darktrace observed widespread use of SMBv1 across the customer’s environment, exposing a significant security weakness. As a legacy protocol with well-documented weaknesses, SMBv1 can be exploited to facilitate lateral movement, allowing the attackers to expand their access following initial compromise.

Day 3: Lateral Movement, Command & Control, and Exfiltration

Two days later, the attacker escalated privileges and expanded their foothold using living-off-the-land (LOTL) techniques such as PSExec, WMI, and RDP. Concurrently, Darktrace identified command-and-control (C2)-style communications consistent with the Sliver framework, alongside rare outbound connections to cloud infrastructure indicating potential data exfiltration. The volume and severity of observed activity increased as attack behavior intensified.

The device was observed conducting extensive lateral movement, leveraging LOTL techniques to evade detection. Activity included WMI execution (e.g. ExecQuery), DCE-RPC activity, SMB sessions and file writes, most of which were successful, as well as the deployment of PSEXESVC.exe via ADMIN$ shares and prolonged RDP sessions. Darktrace identified this behavior as highly anomalous for the environment. Such activity is commonly associated with the transfer of attacker tooling, remote command execution, and the establishment of persistent access across compromised systems.  

Figure 4: Darktrace’s detection of a spike in RPC binding events indicative of potential lateral movement.

On the same day, Darktrace detected C2-style SSL communications originating from multiple internal devices to rare external endpoints. These connections exhibited anomalous characteristics, including invalid SSL certificates and repeated connection patterns resembling beaconing. Analysis of the observed JA3 fingerprint further linked the activity to Sliver, the adversary simulation framework referenced earlier, as the hash has previously been associated with Sliver-related infrastructure [3]. The use of this framework reflects a broader trend of attackers repurposing legitimate offensive security tools for stealthy C2 communications. Connections to 137[.]220[.]59[.]55 (ASN AS20473 AS-VULTR) indicated that the communications were likely routed via a virtual private server (VPS) hosted by Vultr. Attackers often utilize VPS infrastructure from legitimate cloud providers like Vultr to obscure their true origin, blend into benign traffic, and evade IP-based detection mechanisms [4].

Figure 5: Darktrace’s Cyber AI Analyst detection of two linked unusual connections to Vultr infrastructure.

Darktrace also observed a device initiating SSL connections to safedata.s3[.]wasabisys[.]com, an endpoint associated with Wasabi cloud storage. Darktrace recognized that neither the destination nor the associated IP address had previously been observed within the environment.  More than 200 MB of data was subsequently uploaded externally to endpoints sharing the same JA3 client hash, indicating a sustained transfer session and potential data exfiltration to third-party storage. The apparent exfiltration prior to encryption is consistent with a double-extortion ransomware strategy.

Figure 6: Darktrace’s Cyber AI Analyst detection of more than 30 rare outbound connections to a Wasabi cloud storage endpoint, indicative of potential data exfiltration

Day 4: Encryption

The attack culminated in ransomware deployment, marking the transition from suspicious network activity to a business-impacting incident. Using SMB-based propagation, the threat actor encrypted thousands of files across the network, affecting multiple systems and disrupting normal operations. Throughout the encryption event, the legacy SMBv1 protocol was used extensively across multiple internal systems, resulting in a significant increase in newly encrypted files.

Figure 7: Darktrace’s detection of abnormal spikes in SMB activity and associated model alerts, indicative of ransomware encryption and propagation.

Darktrace’s Cyber AI Analyst automatically investigated and correlated the encryption activity and related events into a single incident narrative, providing the customer with real-time visibility into the attack while significantly reducing investigation time.

Figure 8: Darktrace’s Cyber AI Analyst’s investigation into the encryption activity. AI Analyst incident detailing example encryption activity in real time. Related events are automatically correlated and summarized into a clear narrative, reducing investigation time.

Defender action recommendations

What Could Have Stopped the Attack Earlier?

Although the attack ultimately resulted in ransomware deployment, there were multiple opportunities to detect, contain, and disrupt the intrusion before encryption occurred. The following actions could have significantly reduced the overall impact:

Detect and investigate indicators of reconnaissance and lateral movement

  • Unusual scanning
  • Active Directory replication anomalies consistent with DCSync activity
  • Anomalous use of native tools and processes indicative of LOTL attacks
  • Unusual use of common reconnaissance tools such as Nmap and NetScan

Contain compromised credentials and affected devices

  • Disable and reset compromised VPN credentials
  • Isolate devices performing anomalous scanning and lateral movement activity

Block suspicious external communications and data exfiltration

  • Use anomaly-based detection to detect and block repeated outbound connections to rare external infrastructure
  • Prevent data exfiltration to unauthorized cloud storage services such as Wasabi

Conclusion

The incident highlights the importance of anomaly-based detection, particularly against attacks that primarily use native or legitimate tools to evade traditional security measures. Darktrace identified suspicious activity from the first day of the compromise, providing multiple opportunities to disrupt the intrusion before it progressed to lateral movement and data exfiltration.

In this instance, detection was not the limiting factor; response time was. Prompt investigation and containment of devices exhibiting anomalous behavior could have prevented lateral movement, data exfiltration, and ultimately ransomware deployment.

As adversaries increasingly prioritize stealth over custom malware, relying instead on legitimate tools, valid credentials, and trusted infrastructure, traditional signature-based detection becomes less effective. Identifying subtle behavioral deviations early remains critical to disrupting attacks before they escalate into full-scale ransomware incidents.

Credit to Alexandra Evzona (Cyber Analyst), Priya Thapa (Senior Cyber Analyst)
Edited by Ryan Traill (Content Manager)

Appendices

References

[1] https://industrialcyber.co/ransomware/check-point-reports-ransomware-attacks-jump-48-year-over-year-despite-decline-in-overall-cyberattack-activity/

[2] https://www.europol.europa.eu/media-press/newsroom/news/law-enforcement-disrupt-worlds-biggest-ransomware-operation

[3] https://fieldeffect.com/blog/field-effect-mitigates-not-so-simplehelp-exploits-enabling-deployment-of-backdoors

[4] https://www.darktrace.com/blog/from-vps-to-phishing-how-darktrace-uncovered-saas-hijacks-through-virtual-infrastructure-abuse

Indicators of Compromise (IoCs)

IP Addresses

  • 137[.]220[.]59[.]55 – Potential C2 infrastructure (Vultr AS20473)
  • 38[.]27[.]106[.]123 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]128 – Wasabi cloud storage endpoint associated with potential data exfiltration
  • 38[.]27[.]106[.]117 – Wasabi cloud storage endpoint associated with potential data exfiltration

Domains

  • safedata[.]s3[.]wasabisys[.]com – Potential data exfiltration endpoint
  • *.wasabisys[.]com – Associated Wasabi cloud storage infrastructure

JA3 Fingerprint

  • d6828e30ab66774a91a96ae93be4ae4c – Associated with the Sliver adversary emulation framework

Files

  • Delete[.]me – File observed during reconnaissance activity, commonly associated with NetScan

Darktrace Model Coverage

·       Anomalous Connection / Active Remote Desktop Tunnel

·       Anomalous Connection / Anomalous Remote Registry Service Control

·       Anomalous Connection / Multiple Failed Windows UDP

·       Anomalous Connection / New or Uncommon Service Control

·       Anomalous Connection / New or Uncommon Service Enumeration

·       Anomalous Connection / New User Agent to IP Without Hostname

·       Anomalous Connection / SMB Enumeration

·       Anomalous Connection / Suspicious Activity On High Risk Device

·       Anomalous Connection / Suspicious Read Write Ratio

·       Anomalous Connection / Sustained MIME Type Conversion

·       Anomalous Connection / Uncommon 1 GiB Outbound

·       Anomalous Connection / Unusual Admin RDP Session

·       Anomalous Connection / Unusual Admin SMB Session

·       Anomalous Connection / Unusual SMB Version 1 Connectivity

·       Anomalous File / EXE from Rare External Location

·       Anomalous File / Internal / Additional Extension Appended to SMB File

·       Anomalous Server Activity / Outgoing from Server

·       Compromise / Beaconing Activity To External Rare

·       Compromise / Ransomware / Possible Ransom Note Read

·       Compromise / Ransomware / Ransom or Offensive Words Written to SMB

·       Compromise / Ransomware / Suspicious SMB Activity

·       Device / Anonymous NTLM Logins

·       Device / Attack and Recon Tools

·       Device / Initial Attack Chain Activity

·       Device / Large Number of Model Alerts

·       Device / Long Agent Connection to New Endpoint

·       Device / Multiple Lateral Movement Model Alerts

·       Device / Network Scan

·       Device / New or Uncommon SMB Named Pipe

·       Device / New or Unusual Remote Command Execution

·       Device / New User Agent

·       Device / Possible RPC Lateral Movement

·       Device / Possible SMB/NTLM Brute Force

·       Device / RDP Scan

·       Device / SMB Lateral Movement

·       Device / SMB Session Brute Force (Non-Admin)

·       Device / SMB Version 1 Access Failures

·       Device / Suspicious File Delete Activity

·       Device / Suspicious Network Scan Activity

·       Device / Unusual SMB Error Detected

·       Device / Unusual SMB To Critical Resource

·       Device / Unusual Winreg Operation

·       Unusual Activity / Multiple Failed Internal Connections

·       Unusual Activity / Possible RPC Recon Activity

·       Unusual Activity / Sustained Anomalous SMB Activity

·       Unusual Activity / Unusual External Data to New Endpoint

·       Unusual Activity / Unusual External Data Transfer

·       Unusual Activity / Unusual File Storage Data Transfer

·       Unusual Activity / Unusual Large Internal Transfer

·       User / New Admin Credentials on Client

·       User / NTLM Login from Unauthenticated Device

Autonomous Response Model Alerts

·       Antigena / Network / External Threat / Antigena Ransomware Block

·       Antigena / Network / External Threat / Antigena Suspicious File Block

·       Antigena / Network / Insider Threat / Antigena Active Threat SMB Write Block

·       Antigena / Network / Insider Threat / Antigena Internal Anomalous File Activity

·       Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

·       Antigena / Network / Insider Threat / Antigena Network Scan Block

·       Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block

·       Antigena / Network / Manual / Quarantine Device

·       Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·       Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

·       Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block 100

·       Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

MITRE ATT&CK MAPPING

Command and control - Protocol Tunneling - T1572

Command and control – Web Protocols – T1071.001

Credential Access- Exploitation for Credential Access- T1212

Credential Access- Password Guessing- T1110

Discovery- File and Directory Discovery- T1083

Discovery- Network Service Discovery- T1046

Discovery- Network Share Discovery- T1135

Discovery- Remote System Discovery- T1018

Exfiltration- Exfiltration Over C2 Channel- T1041

Exfiltration- Exfiltration to Cloud Storage- T1567

Impact- Data Encrypted for Impact- T1486

Impact- Data Encrypted for Impact- T1486

Impact- Service Stop - T1489

Initial Access- Public-Facing Application- T1190

Lateral Movement- Exploitation of Remote Services- T1210

Lateral Movement- Remote Desktop Protocol- T1021

Lateral Movement- SMB/Windows Admin Shares- T1021

Lateral Movement- Taint Shared Content- T1080

Persistence- Modify Registry- T1112

Privilege Escalation- Exploitation for Privilege Escalation- T0890

Privilege Escalation- Valid Accounts- T1078

Reconnaissance- Scanning IP Blocks- T1595

Reconnaissance- Vulnerability Scanning- T1595

Resource Development- Malware- T1588

Stealth- File Deletion- T1070

Continue reading
About the author
Alexandra Evzona
Cyber Analyst

Blog

/

Email

/

July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

Default blog imageDefault blog image

Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

[related-resource]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email
Your data. Our AI.
Elevate your network security with Darktrace AI