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February 20, 2020

Lessons Learned from a Sodinokibi Ransomware Attack

Gain insights into a targeted Sodinokibi ransomware attack and learn how to better prepare your organization for potential 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
Max Heinemeyer
Global Field CISO
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20
Feb 2020

Introduction

Last week, Darktrace detected a targeted Sodinokibi ransomware attack during a 4-week trial with a mid-sized company.

This blog post will go through every stage of the attack lifecycle and detail the attacker’s techniques, tools and procedures used, and how Darktrace detected the attack.

The Sodinokibi group is an innovative threat-actor that is sometimes referred to as a ‘double-threat’, due to their ability to run targeted attacks using ransomware while simultaneously exfiltrating their victim’s data. This enables them to threaten to make the victim’s data publicly available if the ransom is not paid.

While Darktrace’s AI was able to identify the attack in real time as it was emerging, unfortunately the security team didn’t have eyes on the technology and was unable to action the alerts — nor was Antigena set in active mode, which would have slowed down and contained the threat instantaneously.

Timeline

The timeline below provides a rough overview of the major attack phases. Most of the attack took place over the course of a week, with the majority of activity distributed over the last three days.

Technical analysis

Darktrace detected two main devices being hit by the attack: an internet-facing RDP server (‘RDP server’) and a Domain Controller (‘DC’), that also acts as a SMB file server.

In previous attacks, Sodinokibi has used host-level encryption for ransomware activity where the encryption takes place on the compromised host itself — in contrast to network-level encryption where the bulk of the ransomware activity takes place over network protocols such as SMB.

Initial compromise

Over several days, the victim’s external-facing RDP server was receiving successful RDP connections from a rare external IP address located in Ukraine.

Shortly before the initial reconnaissance started, Darktrace saw another RDP connection coming into the RDP server with the same RDP account as seen before. This connection lasted for almost an hour.

It is highly likely that the RDP credential used in this attack had been compromised prior to the attack, either via common brute-force methods, credential stuffing attacks, or phishing.

Thanks to Darktrace’s Deep-Packet Inspection, we can clearly see the connection and all related information.

Suspicious RDP connection information:

Time: 2020-02-10 16:57:06 UTC
Source: 46.150.70[.]86 (Ukraine)
Destination: 192.168.X.X
Destination Port: 64347
Protocol: RDP
Cookie: [REDACTED]
Duration: 00h41m40s
Data out: 8.44 MB
Data in: 1.86 MB

Darktrace detects incoming RDP connections from IP addresses that usually do not connect to the organization.

Attack tools download

Approximately 45 minutes after the suspicious RDP connection from Ukraine, the RDP server connected to the popular file sharing platform, Megaupload, and downloaded close to 300MB from there.

Darktrace’s AI recognized that neither this server, nor its automatically detected peer group, nor, in fact, anyone else on the network commonly utilized Megaupload — and therefore instantly detected this as anomalous behavior, and flagged it as unusual.

As well as the full hostname and actual IP used for the download, Megaupload is 100% rare for this organization.

Later on, we will see over 40GB being uploaded to Megaupload. This initial download of 300MB however is likely additional tooling and C2 implants downloaded by the threat-actor into the victim’s environment.

Internal reconnaissance

Only 3 minutes after the download from Megaupload onto the RDP server, Darktrace alerted on the RDP server doing an anomalous network scan:

The RDP server scanned 9 other internal devices on the same subnet on 7 unique ports: 21, 80, 139, 445, 3389, 4899, 8080
 . Anybody with some offensive security know-how will recognize most of these ports as default ports one would scan for in a Windows environment for lateral movement. Since this RDP server does not usually conduct network scans, Darktrace again identified this activity as highly anomalous.

Later on, we see the threat-actor do more network scanning. They become bolder and use more generic scans — one of them showing that they are using Nmap with a default user agent:

Additional Command and Control traffic

While the initial Command and Control traffic was most likely using predominantly RDP, the threat-actor now wanted to establish more persistence and create more resilient channels for C2.

Shortly after concluding the initial network scans (ca. 19:17 on 10th February 2020), the RDP server starts communicating with unusual external services that are unique and unusual for the victim’s environment.

Communications to Reddcoin

Again, nobody else is using Reddcoin on the network. The combination of application protocol and external port is extremely unusual for the network as well.

The communications also went to the Reddcoin API, indicating the installation of a software agent rather than manual communications. This was detected as Reddcoin was not only rare for the network, but also ‘young’ — i.e. this particular external destination had never been seen to be contacted before on the network until 25 minutes before.

Communications to the Reddcoin API

Communications to Exceptionless[.]io

As we can see, the communications to exceptionalness[.]io were done in a beaconing manner, using a Let’s Encrypt certificate, being rare for the network and using an unusual JA3 client hash. All of this indicates the presence of new software on the device, shortly after the threat-actor downloaded their 300MB of tooling.

While most of the above network activity started directly after the threat-actor dropped their tooling on the RDP server, the exact purpose of interfacing with Reddcoin and Exceptionless is unclear. The attacker seems to favor off-the-shelf tooling (Megaupload, Nmap, …) so they might use these services for C2 or telemetry-gathering purposes.

This concluded most of the activity on February 10.

More Command and Control traffic

Why would an attacker do this? Surely using all this C2 at the same time is much noisier than just using 1 or 2 channels?

Another significant burst of activity was observed on February 12 and 13.

The RDP server started making a lot of highly anomalous and rare connections to external destinations. It is inconclusive if all of the below services, IPs, and domains were used for C2 purposes only, but they are linked with high-confidence to the attacker’s activities:

  • HTTP beaconing to vkmuz[.]net
  • Significant amount of Tor usage
  • RDP connections to 198-0-244-153-static.hfc.comcastbusiness[.]net over non-standard RDP port 29348
  • RDP connections to 92.119.160[.]60 using an administrative account (geo-located in Russia)
  • Continued connections to Megaupload
  • Continued SSL beaconing to Exceptionless[.]io
  • Continued connections to api.reddcoin[.]com
  • SSL beaconing to freevpn[.]zone
  • HTTP beaconing to 31.41.116[.]201 to /index.php using a new User Agent
  • Unusual SSL connections to aj1713[.]online
  • Connections to Pastebin
  • SSL beaconing to www.itjx3no[.]com using an unusual JA3 client hash
  • SSL beaconing to safe-proxy[.]com
  • SSL connection to westchange[.]top without prior DNS hostname lookups (likely machine-driven)

What is significant here is the diversity in (potential) C2 channels: Tor, RDP going to dynamic ISP addresses, VPN solutions and possibly custom / customized off-the-shelf implants (the DGA-looking domains and HTTP to IP addresses to /index.php).

Why would an attacker do this? Surely using all this C2 at the same time is much noisier than just using 1 or 2 channels?

One answer might be that the attacker cared much more about short-term resilience than about stealth. As the overall attack in the network took less than 7 days, with a majority of the activity taking place over 2.5 days, this makes sense. Another possibility might be that various individuals were involved in parallel during this attack — maybe one attacker prefers the comfort of RDP sessions for hacking while another is more skilled and uses a particular post-exploitation framework.

The overall modus operandi in this financially-motivated attack is much more smash-and-grab than in the stealthy, espionage-related incidents observed in Advanced Persistent Threat campaigns (APT).

Data exfiltration

The DC uploaded around 40GB of data to Megaupload over the course of 24 hours.

While all of the above activity was seen on the RDP server (acting as the initial beach-head), the following data exfiltration activity was observed on a Domain Controller (DC) on the same subnet as the RDP server.

The DC uploaded around 40GB of data to Megaupload over the course of 24 hours.

Darktrace detected this data exfiltration while it was in progress — never did the DC (or any similar devices) upload similar amounts of data to the internet. Neither did any client nor server in the victim’s environment use Megaupload:

Ransom notes

Finally, Darktrace observed unusual files being accessed on internal SMB shares on February 13. These files appear to be ransom notes — they follow a similar, randomly-generated naming convention as other victims of the Sodinokibi group have reported:

413x0h8l-readme.txt
4omxa93-readme.txt

Conclusion and observations

The threat-actor seems to be using mostly off-the-shelf tooling which makes attribution harder — while also making detection more difficult.

This attack is representative of many of the current ransomware attacks: financially motivated, fast-acting, and targeted.

The threat-actor seems to be using mostly off-the-shelf tooling (RDP, Nmap, Mega, VPN solutions) which makes attribution harder — while also making detection more difficult. Using this kind of tooling often allows to blend in with regular admin activity — only once anomaly detection is used can this kind of activity be detected.

How can you spot the one anomalous outbound RDP connection amongst the thousands of regular RDP connections leaving your environment? How do you know when the use of Megaupload is malicious — compared to your users’ normal use of it? This is where the power of Darktrace’s self-learning AI comes into play.

Darktrace detected every stage of the visible attack lifecycle without using any threat intelligence or any static signatures.

The graphics below show an overview of detections on both compromised devices. The compromised devices were the highest-scoring assets for the network — even a level 1 analyst with limited previous exposure to Darktrace could detect such an in-progress attack in real time.

RDP Server

Some of the detections on the RDP server include:

  • Compliance / File Storage / Mega — using Megaupload in an unusual way
  • Device / Network Scan — detecting unusual network scans
  • Anomalous Connection / Application Protocol on Uncommon Port — detecting the use of protocols on unusual ports
  • Device / New Failed External Connections — detecting unusual failing C2
  • Compromise / Unusual Connections to Let’s Encrypt — detecting potential C2 over SSL using Let’s Encrypt
  • Compromise / Beacon to Young Endpoint — detecting C2 to new external endpoints for the network
  • Device / Attack and Recon Tools — detecting known offensive security tools like Nmap
  • Compromise / Tor Usage — detecting unusual Tor usage
  • Compromise / SSL Beaconing to Rare Destination — detecting generic SSL C2
  • Compromise / HTTP Beaconing to Rare Destination — detecting generic HTTP C2
  • Device / Long Agent Connection to New Endpoint — detecting unusual services on a device
  • Anomalous Connection / Outbound RDP to Unusual Port — detecting unusual RDP C2

DC

Some of the detections on the DC include:

  • Anomalous Activity / Anomalous External Activity from Critical Device — detecting unusual behaviour on dcs
  • Compliance / File storage / Mega — using Megaupload in an unusual way
  • Anomalous Connection / Data Sent to New External Device — data exfiltration to unusual locations
  • Anomalous Connection / Uncommon 1GB Outbound — large amounts of data leaving to unusual destinations
  • Anomalous Server Activity / Outgoing from Server — likely C2 to unusual endpoint on the internet

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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
Max Heinemeyer
Global Field CISO

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October 22, 2026

Vulnerability Discovery to Active Attacks: AI in Cyber Operations

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The disclosure of CVE-2026-109618 and CVE-2026-109619 placed immediate pressure on organizations running PHP. Both vulnerabilities can allow unauthenticated remote code execution, including in products and appliances where PHP is embedded, and updates depend on third-party vendors [1].

Early guidance focused on patching internet-facing systems and monitoring for behavior associated with successful exploitation, including unexpected command execution, Webroot changes and outbound connections from PHP servers.

Darktrace observed widespread scanning across monitored environments involving hundreds of distinct source IP addresses that public reporting has associated with activity targeting the PHP vulnerabilities. The volume of scanning was significantly greater than the evidence available for successful exploitation. For analysts, that distinction is important. Scanning demonstrates interest in exposed infrastructure; establishing compromise requires evidence of subsequent behavior on the targeted system, including command execution, file modification or unexpected outbound communication.

While Darktrace has not observed direct evidence of CVE-2026-109618 or CVE-2026-109619 exploitation, on October 21st we observed a six-month high in detections consistent with zero-day exploitation activity, representing a 64% increase compared to historic data. This increase has not been confirmed as related to the PHP vulnerabilities. Darktrace’s behavioral approach is designed to detect anomalous activity without relying solely on rules written for a specific exploit. Comparable increases were observed around the Log4j disclosure and other globally significant vulnerability disclosures.

AI-assisted vulnerability research

The PHP disclosure arrives as AI systems have become more capable at vulnerability research [2]. OpenAI launched Daybreak in June 2026, combining GPT-5.5-Cyber, Codex Security and partnerships with security researchers and software maintainers. OpenAI says its models have been applied to discovering and generating patches for vulnerabilities in browsers, network infrastructure, FreeBSD and the Linux kernel. Darktrace joined the OpenAI Daybreak Cyber Partner Program in June to explore how OpenAI's cyber capabilities could be integrated within Darktrace products and services [3][9].

OpenAI reported in August that Codex Security Cloud had analyzed more than 30 million commits across more than 30,000 codebases. According to OpenAI, Cloudflare uses models accessed through Daybreak to investigate customer-authorized code and combines findings with live traffic and security signals to assess whether affected routes are active or receiving suspicious traffic, with proposed mitigations and patches remaining subject to customer review [4][5].

Faster discovery still leaves defenders with the work of locating affected software, testing fixes and completing remediation. The PHP case shows that gap clearly, particularly when vulnerable runtimes are embedded inside other products.

Enterprise agents as an attack surface

Darktrace Signal Labs has also examined how a trusted enterprise agent could be abused after an attacker has already obtained access to an environment. In a controlled simulation, researchers demonstrated that, in that post-compromise scenario, the Claude platform’s memory stores feature could be abused to establish a live C2 channel between an external operator and internal Claude Code agents. The external operator was able to dynamically assign network discovery, credential access, and data exfiltration tasks to the agents via this C2 channel. Detection of the agents’ activities relied on the correlation of process creation and network traffic activity from agent harness processes, alongside behavioral analysis to identify deviations from expected behavior. Darktrace has not claimed it observed this memory-store command-and-control technique in operational attacks [6].

AI-assisted attacks in Japan

Evidence from Japan indicates AI being used for vulnerability scanning and attacks outside controlled research. On 21 October, JPCERT/CC published findings from an investigation into recent unauthorized access incidents affecting organizations in Japan. JPCERT/CC states that it confirmed attackers had used AI to scan for known vulnerabilities and conduct attacks through APIs. According to JPCERT/CC, its examination of attack infrastructure found evidence of Android application analysis, API discovery and testing for weaknesses including server-side request forgery, and the infrastructure also targeted services intended for users outside Japan [7].

JPCERT/CC does not identify ARTEX by name in that publication. The named ARTEX attribution comes from separate investigations, including Microsoft's analysis of Storm-0940.

AI in Offensive Operations

Microsoft describes ARTEX as an open-source, multi-agent penetration-testing system capable of automating activity with limited human interaction. Microsoft reported that Storm-0940, which it assesses to be a China state-aligned actor active since 2021, used ARTEX against hundreds of organizations, and Microsoft attributed the ConnectWise supply-chain compromise [8].

Microsoft observed ARTEX accelerating scanning, reconnaissance, password spraying and OAuth application abuse. Several ARTEX-driven operations reached privileged cloud access less than three hours after initial targeting. Following the ConnectWise compromise, Storm-0940 attempted to authenticate to more than 2,250 tenants at rates of up to 2,900 attempts per minute [8].

Darktrace Threat Research found activity in its own telemetry involving infrastructure later associated with the campaign. On 13 October, Darktrace recorded failed authentication activity involving 144.172.64[.]128, one of the addresses Microsoft later identified as performing sign-ins and Microsoft Graph activity in ConnectWise customer environments. Darktrace analysis identified 65 failed authentication attempts in around 20 minutes.

Figure 1: Darktrace Advanced Search showing failed authentication events involving 144.172.64[.]128 on 13 October 2026, (customer-identifying information redacted).

The telemetry establishes repeated failed authentication from infrastructure later associated with the campaign. It does not establish ARTEX execution or successful compromise. At the time of observation, Microsoft's later attribution was unavailable. Analysts therefore had the behavior itself: repeated failed authentication from the same source over a short period, together with any related identity, application or network activity inside the environment.

Microsoft documented a wider password-spray campaign against 327 accounts across eight organizations between 10 and 14 October. It recorded more than 151,000 failed logons, including more than 111,000 on 11 October, but no successful authentication, token issuance, Graph call or directory change from that campaign. Microsoft also reported that most Storm-0940 targeting was unsuccessful [8].

Where the actor progressed beyond authentication, Microsoft observed service-principal sign-ins followed by Graph and directory operations. Activity included reconnaissance, application credential changes and attempts to modify privileges. Microsoft recommends correlating service-principal sign-ins with Graph activity, directory changes and resource access [8].

Behavior before Attribution

The PHP and ARTEX cases begin with different information. PHP defenders knew which software required urgent remediation but initially had limited information about exploitation. Process execution, file modification and outbound communication could still provide evidence of compromise.

The PHP and ARTEX observations also illustrate the difference between volume and impact. Darktrace observed broad scanning around the PHP disclosure, while the ARTEX-associated infrastructure produced concentrated authentication activity. Neither observation independently demonstrates compromise and in each case examining the behavior that follows the initial interaction is the investigative clue.

The Darktrace ARTEX-related observation began with authentication behavior. Microsoft publicly attributed the source infrastructure eight days later. That attribution added context, but it did not change what had originally been recorded in the environment.

AI is now present at several points in this cycle. Daybreak applies models to vulnerability discovery and remediation. Darktrace Signal Labs demonstrated how a trusted enterprise agent could be co-opted in a controlled environment. JPCERT/CC confirmed AI-assisted vulnerability scanning and API attacks in recent incidents. Microsoft documented ARTEX being used by Storm-0940 in active operations [3][6][7][8].

Microsoft's findings also show why scale and impact must be assessed separately. One ARTEX-associated campaign produced more than 151,000 failed logons without successful access, while other operations reached privileged cloud access in less than three hours. Security teams have to distinguish high-volume attempts from activity that progresses through authentication, privilege changes and resource access.

The evidence required to assess these cases remains familiar: authentication records, process execution, application and directory changes, file activity and network communication. AI can increase the rate at which those behaviors occur. Defenders still need to establish their sequence and impact.

Credit to Emma Foulger, Sam Lister, Dylan Evans, Moishe Tajtelbaum

References

[1] NCSC-NL, php2shell vulnerabilities (CVE-2026-109618, CVE-2026-109619), 21 October 2026. https://github.com/NCSC-NL/php2shell

[2] Cyber Security Agency of Singapore, https://www.csa.gov.sg/alerts-and-advisories/alerts/al-2026-151/

[3] OpenAI, Daybreak: Tools for securing every organization in the world, 22 June 2026. https://openai.com/index/daybreak-securing-the-world/

[4] OpenAI Developers, Scaling cyber defenders with Daybreak, 21 August 2026. https://developers.openai.com/blog/scaling-cyber-defenders-with-daybreak

[5] OpenAI, Cloudflare links vulnerability discovery to defense with OpenAI, 21 October 2026. https://openai.com/index/cloudflare-cyber/

[6] Darktrace Signal Labs, Shared Memories as a Covert C2 Channel, draft research supplied for review.

[7] JPCERT/CC, Recent AI-Assisted Cyber Attacks in Japan: Investigating Attack Infrastructure and Techniques, 21 October 2026. https://blogs.jpcert.or.jp/en/2026/10/web_api_attacks.html

[8] Microsoft Security Research and Microsoft Threat Intelligence, AI-driven, large-scale nation-state campaign using ARTEX enables supply chain compromise, 21 October 2026. https://www.microsoft.com/en-us/security/blog/2026/10/21/storm-0940-uses-artex-agentic-ai-connectwise-supply-chain-compromise/

[9] Darktrace, Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations, 22 June 2026. https://www.darktrace.com/blog/advancing-the-use-of-frontier-ai-in-cybersecurity-darktrace-joins-the-openai-daybreak-cyber-partner-program-to-explore-defensive-ai-integrations

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October 21, 2026

Beyond the Fridge: Exposed Industrial Control Systems

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Introduction

In October 2026, hundreds of supermarkets across France were reportedly affected by a cyberattack targeting their refrigeration systems. According to public reports, the attackers gained access to internet-connected controllers and modified their settings, forcing refrigeration units into high and extremely low temperatures and causing significant disruption. Total losses have been reported to reach several million euros.

The incident drew attention to the security of industrial refrigeration systems, particularly older devices with exposed management interfaces and known vulnerabilities.

The exact details of how the attackers gained access have not been confirmed, however it is speculated that the public-facing web server could be accessed with the username “admin” and password being the current date. Researchers from Claroty’s Team82 identified a vulnerability in Danfoss AK-SM 8xxA Series in 2025, “CVE-2025-41450", allowing for administrative access using a time-based “code-of-the-day" for authentication [1].

Default credentials, outdated firmware and publicly accessible administrative interfaces have been an issue for years. And yet these systems continue to appear in internet scans and require little sophistication to gain admin access. A simple search of internet-connected devices shows a range of exposed devices from Programmable Logic Controllers (PLCs) and remote gateways, to, yes, more fridges.

Darktrace malware researchers identified hundreds of IoT and OT devices publicly exposed on the internet, many of which are configured with no authentication, weak credentials, or default credentials. As shown in Figure 1, an exposed Copeland XWEB web interface, used to manage Dixell equipment, including refrigerators, can provide the ability to remotely control connected devices.

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Figure 1: Copeland XWEB interface used to control systems including refrigerators and air conditioning.

However, it’s not only fridges; web management interfaces for industrial controls are also publicly accessible, often protected only by weak credentials. Figure 2 shows an example of a publicly exposed WAGO interface. The WAGO management interface is a configuration and administration panel for controllers. Through it, a logged-in user can view device details and diagnostics, manage user accounts and passwords, update firmware, back up or restore the full configuration, set the clock, and manage SD card storage. While it typically doesn’t manage switch outputs or read live process values, someone with access could still disrupt operation by changing network or Modbus settings, loading bad firmware or configurations, or by resetting passwords and enabling insecure services.

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Figure 2: WAGO Management interface.

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The issue extends beyond management interfaces. During our research, we identified publicly accessible web interfaces belonging to PLCs, including Rockwell Automation Allen-Bradley devices. In each case, the risk arises from how the devices have been deployed and configured, not from a flaw in the vendors’ products.

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Figure 3: Web interface for Allen-Bradley CompactLogix controller.

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Figure 3 shows the web interface of an Allen-Bradley CompactLogix controller. Unlike the previous examples, this interface provides a view into the controller's operation, including running tasks, network connections, memory usage and diagnostic information. Rockwell Automation's CompactLogix controllers are commonly used to automate industrial processes, from manufacturing and packaging lines to water treatment and material handling systems. Depending on their configuration, these controllers can operate motors, pumps, valves and other physical equipment.

Although in this example the interface shown does not provide direct control over the PLC's logic, it exposes information about the model, firmware, network layout, and connections, which is useful reconnaissance for a bigger attack.

The concern is not necessarily that every exposed interface allows an attacker to immediately manipulate industrial processes. However, these devices are accessible from the public internet in the first place, providing information about systems that may be responsible for controlling physical equipment.

The attacks on refrigeration systems in France highlight the consequences of poorly secured industrial devices. As demonstrated throughout this research, publicly accessible management interfaces are not limited to refrigeration controllers but extend to PLCs and other systems used to manage industrial equipment.

Finding these devices requires little effort. Internet scanners make it possible to identify publicly exposed interfaces quickly, and in some cases these interfaces rely on publicly documented default credentials or have no authentication at all. As attackers increasingly use AI tools, such attacks may be identified and carried out faster and at a greater scale.

The consequences of this access depend on the device and the functionality exposed. While some interfaces provide little more than diagnostic information, others allow users to modify configurations, disrupt communications, or change settings that affect physical equipment. In industrial environments, this can result in operational downtime, damaged equipment and financial losses.

Weak passwords, default credentials and exposed administrative interfaces are not new security issues. Yet their continued presence on internet-connected industrial systems demonstrates how easily preventable weaknesses can leave physical operations vulnerable to attack.

Exposed devices often come from third-party suppliers and contractors, who frequently install refrigeration controllers, gateways and PLCs without the organization ever being aware they are connected to the internet. Asset visibility remains an important foundation, but organizations should assume that some unknown assets and services will exist. Effective security therefore also depends on continuous monitoring, anomaly detection, network segmentation and rapid response, so that risky exposure and unusual activity can be identified and addressed before it becomes disruptive.

Darktrace helps security teams gain visibility across IT and OT and respond to risks: https://www.darktrace.com/products/operational-technology.

Copeland, Dixell, WAGO, Rockwell Automation, Allen-Bradley and CompactLogix are trademarks of their respective owners. Their use here is for identification only and does not imply any affiliation with or endorsement by those owners.

References

1.        https://claroty.com/team82/research/freeze-the-controller-defrost-the-food-uncovering-vulnerabilities-in-danfoss-refrigeration-controllers

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About the author
Tara Gould
Malware Research Lead
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