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August 22, 2023

Darktrace’s Detection of Unattributed Ransomware

Leveraging anomaly-based detection, we successfully identified an ongoing ransomware attack on the network of a customer and the activity that preceded it.
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
Natalia Sánchez Rocafort
Cyber Security Analyst
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22
Aug 2023

In the current threat landscape, much of the conversation around ransomware focusses on high-profile strains and notorious threat groups. While organizations and their security teams are justified in these concerns, it is important not to underestimate the danger posed by smaller scale, unattributed ransomware attacks.

Unlike attributed ransomware strains, there are often no playbooks or lists of previously observed indicators of compromise (IoCs) that security teams can consult to help them shore up their cyber defenses. As such, anomaly detection is critical to ensure that emerging threats can be detected based on their abnormality on the network, rather than relying heavily on threat intelligence.

In mid-March 2023, a Darktrace customer requested analytical support from the Darktrace Security Operations Center (SOC) after they had been hit by a ransomware attack a few hours earlier. Darktrace was able to uncover a myriad of malicious activity that preceded the eventual ransomware deployment, ultimately assisting the customer to identify compromised devices and contain the ransomware attack.

Attack Overview

While there were a small number of endpoints that had been flagged as malicious by open-source intelligence (OSINT), Darktrace DETECT™ focused on the unusualness of the activity surrounding this emerging ransomware attack. This provided unparalleled visibility over this ransomware attack at every stage of the cyber kill chain, whilst also revealing the potential origins of the compromise which came months area.

Initial Compromise

Initial investigation revealed that several devices that Darktrace were observed performing suspicious activity had previously engaged in anomalous behavior several months before the ransomware event, indicating this could be a part of a repeated compromise or the result of initial access brokers.

Most notably, in late January 2023 there was a spike in unusual activity when some of the affected devices were observed performing activity indicative of network and device scanning.

Darktrace DETECT identified some of the devices establishing unusually high volumes of internal failed connections via TCP and UDP, and the SMB protocol. Various key ports, such as 135, 139, and 445, were also scanned.

Due to the number of affected devices, the exact initial attack vector is unclear; however, one likely scenario is associated with an internet-facing DNS server. Towards the end of January 2023, the server began to receive unusual TCP DNS requests from the rare external endpoint, 103.203.59[.]3, which had been flagged as potentially malicious by OSINT [4]. Based on a portion of the hostname of the device, dc01, we can assume that this server served as a gateway to the domain controller. If a domain controller is compromised, a malicious actor would gain access to usernames and passwords within a network allowing attackers to obtain administrative-level access to an organization’s digital estate.

Around the same time as the unusual TCP DNS requests, Darktrace DETECT observed the domain controller engaging in further suspicious activity. As demonstrated in Figure 1, Darktrace recognized that this server was not responding to common requests from multiple internal devices, as it would be expected to. Following this, the device was observed carrying out new or uncommon Windows Management Instrumentation (WMI) activity. WMI is typically used by network administrators to manage remote and local Windows systems [3].

Figure 1: Device event log depicting the possible Initial attack vector.


Had Darktrace RESPOND™ been enabled in autonomous response mode, it would have to blocked connections originating from the compromised internal devices as soon as they were detected, while also limiting affected devices to their pre-established patterns of file to prevent them from carrying out any further malicious activity.

Darktrace subsequently observed multiple devices establishing various chains of connections that are indicative of lateral movement activity, such as unusual internal RDP and WMI requests. While there may be devices within an organization that do regularly partake these types of connections, Darktrace recognized that this activity was extremely unusual for these devices.

Darktrace’s Self-Learning AI allows for a deep understanding of customer networks and the devices within them. It’s anomaly-based threat detection capability enables it to recognize subtle deviations in a device’s normal patterns of behavior, without depending on known IoCs or signatures and rules to guide it.

Figure 2: Observed chain of possible lateral movement.


Persistence

Darktrace DETECT observed several affected devices communicating with rare external endpoints that had also been flagged as potentially malicious by OSINT tools. Multiple devices were observed performing activity indicative of NTLM brute-forcing activity, as seen in the Figure 3 which highlights the event log of the aforementioned domain controller. Said domain controller continuously engaged in anomalous behavior throughout the course of the attack. The same device was seen using a potentially compromise credential, ‘cvd’, which was observed via an SMB login event.

Figure 3: Continued unusual external connectivity.


Affected devices, including the domain controller, continued to engage in consistent communication with the endpoints prior to the actual ransomware attack. Darktrace identified that some of these malicious endpoints had likely been generated by Domain Generation Algorithms (DGA), a classic tactic utilized by threat actors. Subsequent OSINT investigation revealed that one such domain had been associated with malware such as TrojanDownloader:Win32/Upatre!rfn [5].

All external engagements were observed by Darktrace DETECT and would have been actioned on by Darktrace RESPOND, had it been configured in autonomous response mode. It would have blocked any suspicious outgoing connections originating from the compromised devices, thus preventing additional external engagement from taking place. Darktrace RESPOND works in tandem with DETECT to autonomously take action against suspicious activity based on its unusualness, rather than relying on static lists of ‘known-bads’ or malicious IoCs.

Reconnaissance

On March 14, 2023, a few days before the ransomware attack, Darktrace observed multiple internal devices failing to establish connections in a manner that suggests SMB, RDP and network scanning. Among these devices once more was the domain controller, which was seen performing potential SMB brute-forcing, representing yet another example of malicious activity carried out by this device.

Lateral Movement

Immediately prior to the attack, many compromised devices were observed mobilizing to conduct an array of high-severity lateral movement activity. Darktrace detected one device using two administrative credentials, namely ‘Administrator’ and ‘administrator’, while it also observed a notable spike in the volume of successful SMB connections from the device around the same time.

At this point, Darktrace DETECT was observing the progression of this attack along the cyber kill chain. What had started as internal recognisance, had escalated to exploitation and ensuing command-and-control activity. Following an SMB brute-force attempt, Darktrace DETECT identified a successful DCSync attack.

A DCSync attack occurs when a malicious actor impersonates a domain controller in an effort to gather sensitive information, such as user credentials and passwords hashes, by replicating directory services [1]. In this case, a device sent various successful DRSGetNCChanges operation requests to the DRSUAPI endpoint.

Data Exfiltration

Around the same time, Darktrace detected the compromised server transferring a high volume of data to rare external endpoints associated with Bublup, a third-party project management application used to save and share files. Although the actors attempted to avoid the detection of security tools by using a legitimate file storage service, Darktrace understood that this activity represented a deviation in this device’s expected pattern of life.

In one instance, around 8 GB of data was transferred, and in another, over 4 GB, indicating threat actors were employing a tactic known as ‘low and slow’ exfiltration whereby data is exfiltrated in small quantities via multiple connections, in an effort to mask their suspicious activity. While this tactic may have evaded the detection of traditional security measures, Darktrace’s anomaly-based detection allowed it to recognize that these two incidents represented a wider exfiltration event, rather than viewing the transfers in isolation.

Impact

Finally, Darktrace began to observe a large amount of suspicious SMB activity on the affected devices, most of which was SMB file encryption. DETECT observed the file extension ‘uw9nmvw’ being appended to many files across various internal shares and devices. In addition to this, a potential ransom note, ‘RECOVER-uw9nmvw-FILES.txt’, was detected on the network shortly after the start of the attack.

Figure 4: Depiction of the high-volume of suspicious SMB activity, including file encryption.


Conclusion

Ultimately, this incident show cases how Darktrace was able to successfully identify an emerging ransomware attack using its unrivalled anomaly-based detection capabilities, without having to rely on any previously established threat intelligence. Not only was Darktrace DETECT able to identify the ransomware at multiple stages of the kill chain, but it was also able to uncover the anomalous activity that took place in the buildup to the attack itself.

As the attack progressed along the cyber kill chain, escalating in severity at every juncture, DETECT was able to provide full visibility over the events. Through the successful identification of compromised devices, anomalous administrative credentials usage and encrypted files, Darktrace was able to greatly assist the customer, ensuring they were well-equipped to contain the incident and begin their incident management process.

Darktrace would have been able to aid the customer even further had they enabled its autonomous response technology on their network. Darktrace RESPOND would have taken targeted, mitigative action as soon as suspicious activity was detected, preventing the malicious actors from achieving their goals.

Credit to: Natalia Sánchez Rocafort, Cyber Security Analyst, Patrick Anjos, Senior Cyber Analyst.

MITRE Tactics/Techniques Mapping

RECONNAISSANCE

Scanning IP Blocks  (T1595.001)

RECONNAISSANCE

Vulnerability Scanning  (T1595.002)

IMPACT

Service Stop  (T1489)

LATERAL MOVEMENT

Taint Shared Content (T1080)

IMPACT

Data Encrypted for Impact (T1486)

INITIAL ACCESS

Replication Through Removable Media (T1200)

DEFENSE EVASION

Rogue Domain Controller (T1207)

COMMAND AND CONTROL

Domain Generation Algorithms (T1568.002)

EXECUTION

Windows Management Instrumentation (T1047)

INITIAL ACCESS

Phishing (T1190)

EXFILTRATION

Exfiltration Over C2 Channel (T1041)

IoC Table

IoC ----------- TYPE ------------- DESCRIPTION + PROBABILITY

CVD --------- credentials -------- Possible compromised credential

.UW9NMVW - File extension ----- Possible appended file extension

RECOVER-UW9NMVW-FILES.TXT - Ransom note - Possible ransom note observed

84.32.188[.]186 - IP address ------ C2 Endpoint

AS.EXECSVCT[.]COM - Hostname - C2 Endpoint

ZX.EXECSVCT[.]COM - Hostname - C2 Endpoint

QW.EXECSVCT[.]COM - Hostname - C2 Endpoint

EXECSVCT[.]COM - Hostname ------ C2 Endpoint

15.197.130[.]221 --- IP address ------ C2 Endpoint

AS59642 UAB CHERRY SERVERS - ASN - Possible ASN associated with C2 Endpoints

108.156.28[.]43

108.156.28[.]22

52.84.93[.]26

52.217.131[.]241

54.231.193[.]89 - IP addresses - Possible IP addresses associated with data exfiltration

103.203.59[.]3 -IP address ---- Possible IP address associated with initial attack vector

References:

[1] https://blog.netwrix.com/2021/11/30/what-is-dcsync-an-introduction/

[2] https://www.easeus.com/computer-instruction/delete-system32.html#:~:text=System32%20is%20a%20folder%20on,DLL%20files%2C%20and%20EXE%20files.

[3] https://www.techtarget.com/searchwindowsserver/definition/Windows-Management-Instrumentation#:~:text=WMI%20provides%20users%20with%20information,operational%20environments%2C%20including%20remote%20systems.

[4] https://www.virustotal.com/gui/ip-address/103.203.59[.]3

[5] https://otx.alienvault.com/indicator/ip/15.197.130[.]221

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
Natalia Sánchez Rocafort
Cyber Security Analyst

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March 5, 2026

Inside Cloud Compromise: Investigating Attacker Activity with Darktrace / Forensic Acquisition & Investigation

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Investigating cloud attacks with Darktrace/ Forensic Acquisition & Investigation

Darktrace / Forensic Acquisition & Investigation™ is the industry’s first truly automated forensic solution purpose-built for the cloud. This blog will demonstrate how an investigation can be carried out against a compromised cloud server in minutes, rather than hours or days.

The compromised server investigated in this case originates from Darktrace’s Cloudypots system, a global honeypot network designed to observe adversary activity in real time across a wide range of cloud services. Whenever an attacker successfully compromises one of these honeypots, a forensic copy of the virtual server's disk is preserved for later analysis. Using Forensic Acquisition & Investigation, analysts can then investigate further and obtain detailed insights into the compromise including complete attacker timelines and root cause analysis.

Forensic Acquisition & Investigation supports importing artifacts from a variety of sources, including EC2 instances, ECS, S3 buckets, and more. The Cloudypots system produces a raw disk image whenever an attack is detected and stores it in an S3 bucket. This allows the image to be directly imported into Forensic Acquisition & Investigation using the S3 bucket import option.

As Forensic Acquisition & Investigation runs cloud-natively, no additional configuration is required to add a specific S3 bucket. Analysts can browse and acquire forensic assets from any bucket that the configured IAM role is permitted to access. Operators can also add additional IAM credentials, including those from other cloud providers, to extend access across multiple cloud accounts and environments.

Figure 1: Forensic Acquisition & Investigation import screen.

Forensic Acquisition & Investigation then retrieves a copy of the file and automatically begins running the analysis pipeline on the artifact. This pipeline performs a full forensic analysis of the disk and builds a timeline of the activity that took place on the compromised asset. By leveraging Forensic Acquisition & Investigation’s cloud-native analysis system, this process condenses hour of manual work into just minutes.

Successful import of a forensic artifact and initiation of the analysis pipeline.
Figure 2: Successful import of a forensic artifact and initiation of the analysis pipeline.

Once processing is complete, the preserved artifact is visible in the Evidence tab, along with a summary of key information obtained during analysis, such as the compromised asset’s hostname, operating system, cloud provider, and key event count.

The Evidence overview showing the acquired disk image.
Figure 3: The Evidence overview showing the acquired disk image.

Clicking on the “Key events” field in the listing opens the timeline view, automatically filtered to show system- generated alarms.

The timeline provides a chronological record of every event that occurred on the system, derived from multiple sources, including:

  • Parsed log files such as the systemd journal, audit logs, application specific logs, and others.
  • Parsed history files such as .bash_history, allowing executed commands to be shown on the timeline.
  • File-specific events, such as files being created, accessed, modified, or executables being run, etc.

This approach allows timestamped information and events from multiple sources to be aggregated and parsed into a single, concise view, greatly simplifying the data review process.

Alarms are created for specific timeline events that match either a built-in system rule, curated by Darktrace’s Threat Research team or an operator-defined rule  created at the project level. These alarms help quickly filter out noise and highlight on events of interest, such as the creation of a file containing known malware, access to sensitive files like Amazon Web Service (AWS) credentials, suspicious arguments or commands, and more.

 The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.
Figure 4: The timeline view filtered to alarm_severity: “1” OR alarm_severity: “3”, showing only events that matched an alarm rule.

In this case, several alarms were generated for suspicious Base64 arguments being passed to Selenium. Examining the event data, it appears the attacker spawned a Selenium Grid session with the following payload:

"request.payload": "[Capabilities {browserName: chrome, goog:chromeOptions: {args: [-cimport base64;exec(base64...], binary: /usr/bin/python3, extensions: []}, pageLoadStrategy: normal}]"

This is a common attack vector for Selenium Grid. The chromeOptions object is intended to specify arguments for how Google Chrome should be launched; however, in this case the attacker has abused the binary field to execute the Python3 binary instead of Chrome. Combined with the option to specify command-line arguments, the attacker can use Python3’s -c option to execute arbitrary Python code, in this instance, decoding and executing a Base64 payload.

Selenium’s logs truncate the Arguments field automatically, so an alternate method is required to retrieve the full payload. To do this, the search bar can be used to find all events that occurred around the same time as this flagged event.

Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].
Figure 5: Pivoting off the previous event by filtering the timeline to events within the same window using timestamp: [“2026-02-18T09:09:00Z” TO “2026-02-18T09:12:00Z”].

Scrolling through the search results, an entry from Java’s systemd journal can be identified. This log contains the full, unaltered payload. GCHQ’s CyberChef can then be used to decode the Base64 data into the attacker’s script, which will ultimately be executed.

Decoding the attacker’s payload in CyberChef.
Figure 6: Decoding the attacker’s payload in CyberChef.

In this instance, the malware was identified as a variant of a campaign that has been previously documented in depth by Darktrace.

Investigating Perfctl Malware

This campaign deploys a malware sample known as ‘perfctl to the compromised host. The script executed by the attacker downloads a Go binary named “promocioni.php” from 200[.]4.115.1. Its functionality is consistent with previously documented perfctl samples, with only minor changes such as updated filenames and a new command-and-control (C2) domain.

Perfctl is a stealthy malware that has several systems designed  to evade detection. The main binary is packed with UPX, with the header intentionally tampered with to prevent unpacking using regular tools. The binary also avoids executing any malicious code if it detects debugging or tracing activity, or if artifacts left by earlier stages are missing.

To further aid its evasive capabilities, perfctl features a usermode rootkit using an LD preload. This causes dynamically linked executables to load perfctl’s rootkit payload before other system modules, allowing it to override functions, such as intercepting calls to list files and hiding output from the returned list. Perfctl uses this to hide its own files, as well as other files like the ld.so.preload file, preventing users from identifying that a rootkit is present in the first place.

This also makes it difficult to dynamically analyze, as even analysts aware of the rootkit will struggle to get around it due to its aggressiveness in hiding its components. A useful trick is to use the busybox-static utilities, which are statically linked and therefore immune to LD preloading.

Perfctl will attempt to use sudo to escalate its permissions to root if the user it was executed as has the required privileges. Failing this, it will attempt to exploit the vulnerability CVE-2021-4034.

Ultimately, perfctl will attempt to establish a C2 link via Tor and spawn an XMRig miner to mine the Monero cryptocurrency. The traffic to the mining pool is encapsulated within Tor to limit network detection of the mining traffic.

Darktrace’s Cloudypots system has observed 1,959 infections of the perfctl campaign across its honeypot network in the past year, making it one of the most aggressive campaigns seen by Darktrace.

Key takeaways

This blog has shown how Darktrace / Forensic Acquisition & Investigation equips defenders in the face of a real-world attacker campaign. By using this solution, organizations can acquire forensic evidence and investigate intrusions across multiple cloud resources and providers, enabling defenders to see the full picture of an intrusion on day one. Forensic Acquisition & Investigation’s patented data-processing system takes advantage of the cloud’s scale to rapidly process large amounts of data, allowing triage to take minutes, not hours.

Darktrace / Forensic Acquisition & Investigation is available as Software-as-a-Service (SaaS) but can also be deployed on-premises as a virtual application or natively in the cloud, providing flexibility between convenience and data sovereignty to suit any use case.

Support for acquiring traditional compute instances like EC2, as well as more exotic and newly targeted platforms such as ECS and Lambda, ensures that attacks taking advantage of Living-off-the-Cloud (LOTC) strategies can be triaged quickly and easily as part of incident response. As attackers continue to develop new techniques, the ability to investigate how they use cloud services to persist and pivot throughout an environment is just as important to triage as a single compromised EC2 instance.

Credit to Nathaniel Bill (Malware Research Engineer)

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Nathaniel Bill
Malware Research Engineer

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March 2, 2026

What the Darktrace Annual Threat Report 2026 Means for Security Leaders

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The challenge for today’s CISOs

At the broadest level, the defining characteristic of cybersecurity in 2026 is the sheer pace of change shaping the environments we protect. Organizations are operating in ecosystems that are larger, more interconnected, and more automated than ever before – spanning cloud platforms, distributed identities, AI-driven systems, and continuous digital workflows.  

The velocity of this expansion has outstripped the slower, predictable patterns security teams once relied on. What used to be a stable backdrop is now a living, shifting landscape where technology, risk, and business operations evolve simultaneously. From this vantage point, the central challenge for security leaders isn’t reacting to individual threats, but maintaining strategic control and clarity as the entire environment accelerates around them.

Strategic takeaways from the Annual Threat Report

The Darktrace Annual Threat Report 2026 reinforces a reality every CISO feels: the center of gravity isn’t the perimeter, vulnerability management, or malware, but trust abused via identity. For example, our analysis found that nearly 70% of incidents in the Americas region begin with stolen or misused accounts, reflecting the global shift toward identity‑led intrusions.

Mass adoption of AI agents, cloud-native applications, and machine decision-making means CISOs now oversee systems that act on their own. This creates an entirely new responsibility: ensuring those systems remain safe, predictable, and aligned to business intent, even under adversarial pressure.

Attackers increasingly exploit trust boundaries, not firewalls – leveraging cloud entitlements, SaaS identity transitions, supply-chain connectivity, and automation frameworks. The rise of non-human identities intensifies this: credentials, tokens, and agent permissions now form the backbone of operational risk.

Boards are now evaluating CISOs on business continuity, operational recovery, and whether AI systems and cloud workloads can fail safely without cascading or causing catastrophic impact.

In this environment, detection accuracy, autonomous response, and blast radius minimization matter far more than traditional control coverage or policy checklists.

Every organization will face setbacks; resilience is measured by how quickly security teams can rise, respond, and resume momentum. In 2026, success will belong to those that adapt fastest.

Managing business security in the age of AI

CISO accountability in 2026 has expanded far beyond controls and tooling. Whether we asked for it or not, we now own outcomes tied to business resilience, AI trust, cloud assurance, and continuous availability. The role is less about certainty and more about recovering control in an environment that keeps accelerating.

Every major 2026 initiative – AI agents, third-party risk, cloud, or comms protection – connects to a single board-level question: Are we still in control as complexity and automation scale faster than humans?

Attackers are not just getting more sophisticated; they are becoming more automated. AI changes the economics of attack, lowering cost and increasing speed. That asymmetry is what CISOs are being measured against.

CISOs are no longer evaluated on tool coverage, but on the ability to assure outcomes – trust in AI adoption, resilience across cloud and identity, and being able to respond to unknown and unforeseen threats.

Boards are now explicitly asking whether we can defend against AI-driven threats. No one can predict every new behavior – survival depends on detecting malicious deviations from normal fast and responding autonomously.  

Agents introduce decision-making at machine speed. Governance, CI/CD scanning, posture management, red teaming, and runtime detection are no longer differentiators but the baseline.

Cloud security is no longer architectural, it is operational. Identity, control planes, and SaaS exposure now sit firmly with the CISO.

AI-speed threats already reshaping security in 2026

We’re already seeing clear examples of how quickly the threat landscape has shifted in 2026. Darktrace’s work on React2Shell exposed just how unforgiving the new tempo is: a honeypot stood up with an exposed React was hit in under two minutes. There was no recon phase, no gradual probing – just immediate, automated exploitation the moment the code appeared publicly. Exposure now equals compromise unless defenses can detect, interpret, and act at machine speed. Traditional operational rhythms simply don’t map to this reality.

We’re also facing the first wave of AI-authored malware, where LLMs generate code that mutates on demand. This removes the historic friction from the attacker side: no skill barrier, no time cost, no limit on iteration. Malware families can regenerate themselves, shift structure, and evade static controls without a human operator behind the keyboard. This forces CISOs to treat adversarial automation as a core operational risk and ensure that autonomous systems inside the business remain predictable under pressure.

The CVE-2026-1731 BeyondTrust exploitation wave reinforced the same pattern. The gap between disclosure and active, global exploitation compressed into hours. Automated scanning, automated payload deployment, coordinated exploitation campaigns, all spinning up faster than most organizations can push an emergency patch through change control. The vulnerability-to-exploit window has effectively collapsed, making runtime visibility, anomaly detection, and autonomous containment far more consequential than patching speed alone.

These cases aren’t edge scenarios; they represent the emerging norm. Complexity and automation have outpaced human-scale processes, and attackers are weaponizing that asymmetry.  

The real differentiator for CISOs in 2026 is less about knowing everything and more about knowing immediately when something shifts – and having systems that can respond at the same speed.

[related-resource]

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About the author
Mike Beck
Global CISO
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