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July 11, 2023

Detecting and Responding to Vendor Email Compromises (VEC)

Learn how Darktrace detected and responded to a March 2023 Vendor Email Compromise (VEC) attacks on customer in the energy industry. Read more here!
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
Tiana Kelly
Senior Cyber Analyst & Team Lead
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11
Jul 2023

Threat Trends: Email Landscape

As organizations and security teams around the world continue to improve their cyber hygiene and strengthen the defenses of their digital environments, threat actors are being forced to adapt and employ more advanced, sophisticated attack methods to achieve their goals.

Vendor Email Compromise (VEC) is one such elaborate and sophisticated type of Business Email Compromise (BEC) attack which exploits pre-existing trusted business relationships to impersonate vendors, with the goal of launching a targeted attack on the vendor’s customers [1].  

In March 2023, Darktrace/Email™ detected an example of a VEC attack on the network of a customer in the energy sector. Darktrace’s Self-Learning AI worked to successfully neutralize the VEC attack before it was able to take hold, by blocking the malicious emails so that they did not reach the inboxes of the intended recipients.

Business Email Compromise (BEC)

BEC is the practice of using deceitful emails to trick an organization into transferring funds or divulging sensitive information to a malicious actor. BEC attacks can have devastating financial consequences for organizations, with the FBI reporting a total of USD 2.7 billion in losses from BEC attacks in 2022 [2].  Along with ransomware attacks, BEC attacks are one of the greatest cyber threats facing organizations.

Vendor Email Compromise (VEC)

VEC represents a “new milestone in the evolution of BEC attacks” having taken BEC attacks “to a whole new level of sophistication” [3]. Traditional BEC attacks involve the impersonation of an upper or middle-management employee by a cybercriminal, who attempts to trick a senior executive or employee with access to the company’s finances into transferring funds [4]. Thus, they are crafted to target a specific individual within an organization.

On the other hand, VEC attack campaigns take this attack style even further as they tend to require a greater understanding of existing vendor-customer business relationships. A cyber-criminal gains access to a legitimate vendor account, the process of which may take months to design and fully implement, and uses the account to spread malicious emails to the vendor’s customers. VEC attacks are complex and difficult to detect, however they share some common features [1,3]:

1. Reconnaissance on the vendor and their customer base – the threat actor conducts in-depth research in an attempt to be as convincing as possible in their impersonation efforts. This process may take weeks or months to complete.

2. Credential stealing through phishing campaigns – the threat actor tricks the vendor’s employees into revealing confidential data or corporate credentials in order to gain access to one of the email accounts belonging to the vendor.

3. Account takeover - once the attacker has gained access to one of the vendor’s email accounts, they will create mailbox rules which forward emails meeting certain conditions (such as having ‘Invoice’ in their subject line) to the threat actor’s inbox. This is typically a lengthy process and requires the malicious actors to harvest as much sensitive information as they need in order to successfully masquerade as vendor employees.

4. Deceitful emails are sent to the vendor’s customers – the attacker crafts and sends a highly sophisticated and difficult to detect email campaign to targeted individuals amongst the vendor’s customers. These emails, which may be embedded into existing email threads, will typically contain instructions on how to wire money to the bank account of an attacker.

There have been many high-profile cases of BEC attacks over the years, one of the most famous being the vendor-impersonating BEC attacks carried out between 2013 and 2015 [5]. This BEC campaign resulted in victim companies transferring a total of USD 120 million to bank accounts under the attacker’s control. As the threat of BEC, and in particular VEC, attacks continue to rise, so too does the importance of being able to detect and respond to them.

Observed VEC Attack  

In March 2023, Darktrace/Email observed a VEC attack on an energy company. Email communication between this customer and one of their third-party vendors was common and took place as part of expected business activity, earning previous emails tags such as “Known Domain Relationship”, “Known Correspondent”, and “Established Domain Relationship”. These tags identify the sender relationship as trusted, causing Darktrace’s AI to typically attribute an anomaly score of 0% to emails from this third-party sender.

Just fifty minutes after the above legitimate email was observed, a group of suspicious emails were sent from the same domain, indicating that the trusted third-party had been compromised. Darktrace’s AI picked up on the peculiarity of these emails straight away, detecting elements of the mails which were out of character compared to the sender’s usual pattern of life, and as a result attributing these emails a 100% anomaly score despite the trusted relationship between the customer and sender domain. These suspicious emails were part of a targeted phishing attack, sent to high value individuals such as the company’s CTO and various company directors.  

Figure 1: Darktrace/Email's interface highlighting tags indicating the trusted relationship between the third-party domain and the customer.

Using methods outside of Darktrace’s visibility, a malicious actor managed to hijack the corporate account of a senior employee of this vendor company. The actor abused this email account to send deceitful emails to multiple employees at the energy company, including senior executives.

Figure 2: This screenshot shows Darktrace/Email’s assessment of emails from the vendor account pre-compromise and post-compromise.

Each of the emails sent by the attacker contained a link to a malicious file hosted inside a SharePoint repository associated with a university that had no association with the energy company. The malicious actor therefore appears to have leveraged a previously hijacked SharePoint repository to host their payload.

Cyber-criminals frequently use legitimate file storage domains to host malicious payloads as traditional gateways often fail to defend against them using reputation checks. The SharePoint file which the attacker sought to distribute to employees of the energy company likely provided wire transfer or bank account update instructions. If the attacker had succeeded in delivering these emails to these employees’ mailboxes, then the employees may have been tricked into performing actions resulting in the transfer of funds to a malicious actor. However, the attacker’s attempts to deliver these emails were thwarted by Darktrace/Email.

Darktrace Coverage

Despite the malicious actor sending their deceitful emails from a trusted vendor account, a range of anomalies were detected by Darktrace’s AI, causing the malicious emails to be given a 100% anomaly score and thus held from their recipients’ mailboxes. Such abnormalities, which represented a deviation in normal behavior, included:

  • The presence of an unexpected, out of character file storage link (known to be used for hosting malicious content)
  • The geographical source of the email
  • The anomalous linguistic structure and content of the email body, which earned the emails a high inducement score
Figure 3: Darktrace/Email’s overview of one of the malicious VEC emails it observed.

Darktrace has a series of models designed to trigger when anomalous features, such as those described above, are detected. The emails which made up this particular VEC attack breached a number of notable Darktrace/Email models. The presence of the suspicious link in the emails caused multiple link-related models to breach, which in turn elicited Darktrace RESPOND™ to perform its ‘double lock link’ action – an action which ensures that a user who has clicked on it cannot follow it to its original source. Models which breached due to the suspicious SharePoint link include:

Link / Link To File Storage

  • Link / Low Link Association
  • Link / New Unknown Link
  • Link / Outlook Hijack
  • Link / Relative Sender Anomaly + New Unknown Link
  • Link / Unknown Storage Service
  • Link / Visually Prominent Link Unexpected for Sender
  • Unusual / Unusual Login Location + Unknown Link

The out-of-character and suspicious linguistic aspects of the emails caused the following Darktrace/Email models to breach:

  • High Anomaly Sender
  • Proximity / Phishing
  • Proximity / Phishing and New Activity
  • Unusual / Inducement Shift High
  • Unusual / Undisclosed Recipients
  • Unusual / Unusual Login Location
  • Unusual / Off Topic

Due to the combination of suspicious features that were detected, tags such as ‘Phishing Link’ and ‘Out of Character’ were also added to these emails by Darktrace/Email. Darktrace’s coverage of these emails’ anomalous features ultimately led Darktrace RESPOND to perform its most severe inhibitive action, ‘hold message’. Applying this action stopped the emails from entering their recipients’ mailboxes. By detecting deviations from the sender’s normal email behavior, Darktrace/Email was able to completely neutralize the emails, and prevent them from potentially leading to significant financial harm.

Conclusion

Despite bypassing the customer’s other security measures, Darktrace/Email successfully identified and held these malicious emails, blocking them from reaching the inboxes of the intended recipients and thus preventing a successful targeted VEC attack. The elaborate and sophisticated nature of VEC attacks makes them particularly perilous to customers, and they can be hard to detect due to their exploitation of trusted relationships, and in this case, their use of legitimate services to host malicious files.

Darktrace’s anomaly-based approach to threat detection means it is uniquely placed to identify deviations in common email behavior, while its autonomous response capabilities allow it to take preventative action against emerging threats without latency.

Credits to: Sam Lister, Senior Analyst, for his contributions to this blog.

Appendices

MITRE ATT&CK Mapping

Tactic - Techniques

Resource Development

  • T1586.002 – Compromise Accounts: Email Accounts
  • T1584.006 – Compromise Infrastructure: Web Services
  • T1608.005 – Stage Capabilities: Link Target

Initial Access

  • T1195 – Supply Chain Compromise
  • T1566.002 – Phishing : Spearphishing Link

References

[1] https://www.cloudflare.com/en-gb/learning/email-security/what-is-vendor-email-compromise/

[2] https://www.ic3.gov/Media/PDF/AnnualReport/2022_IC3Report.pdf

[3] https://heimdalsecurity.com/blog/vendor-email-compromise-vec/

[4] https://www.ncsc.gov.uk/files/Business-email-compromise-infographic.pdf  

[5] https://www.justice.gov/usao-sdny/pr/lithuanian-man-sentenced-5-years-prison-theft-over-120-million-fraudulent-business

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
Tiana Kelly
Senior Cyber Analyst & Team Lead

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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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October 20, 2026

Why Kubernetes Security Needs A Behavioral Approach

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Kubernetes has become a foundational technology for modern cloud-native infrastructure. It can orchestrate everything from customer-facing applications to data pipelines and, increasingly, AI workloads. This is due to the highly scalable nature of Kubernetes, making it cost efficient and reliable.

Gartner projects that by 2028, 95% of new AI deployments will run on Kubernetes, up from less than 30% today1. However, the same qualities that make Kubernetes powerful also make it uniquely challenging to secure, leaving many organizations with limited visibility into their clusters. This lack of visibility increases risk - it’s difficult to secure what you can’t see.

However, this risk is often looked at as purely a posture management challenge. While Cloud Security Posture Management (CSPM) solutions can tell you how a cluster should be configured, they don’t tell you what it is doing in real time. This requires runtime behavioral monitoring of clusters to detect active threats.

Yet existing approaches to securing Kubernetes by most Cloud Detection and Response (CDR) and Network Detection and Response (NDR) vendors often have limited coverage or require a third-party integration to forward traffic and metadata. This means that capabilities can be limited and detections can lack the context of the wider environment, which slows down triage, investigation and response.

Why Kubernetes is hard to secure

Traditional security tooling was built for persistent, long-term infrastructure such as servers and virtual machines with fixed identities that last months or years. Some elements of Kubernetes environments can have a much shorter lifespan, breaking many assumptions those tools rely on:

  1. Increased complexity
    1. Kubernetes environments can be complex in comparison to traditional infrastructure, with many different elements to consider such as nodes, clusters, containers, pods and the workloads themselves.
  2. Ephemeral by design
    1. Kubernetes pods can spin up and terminate in seconds. A container that was compromised may no longer exist by the time a deeper investigation is required, leaving no artifact to analyze. This poses a unique challenge for security operations.
  3. Weaker workload boundaries
    1. Kubernetes containers use lightweight, shared-kernel isolation rather than separate virtual machines, improving efficiency but reducing separation between workloads and the host. As a result, a compromised container may have a greater potential impact on the node and neighboring workloads than a compromised virtual machine (VM).2
    2. Recent vulnerabilities have shown that threat actors can escape a container and gain access to the underlying host.3, 4
  4. Everything is API-driven
    1. Dozens or hundreds of microservices communicate over internal APIs, and the Kubernetes control plane itself is an API. This creates a large and dynamic attack surface, making it difficult to distinguish legitimate activity from malicious behavior.
    2. Without identity-aware runtime visibility, threat actors can move laterally between pods and exploit misconfigured role-based access controls (RBAC) with little chance of detection.

Rising to the challenge of securing Kubernetes

With the release of Darktrace 7.2, Darktrace / HYBRID NETWORK can extend behavioral security into Kubernetes, delivering visibility, detection and response capabilities across runtime activity. Darktrace addresses the challenges of securing Kubernetes environments in several ways.

Behavioral detection, not static rules

Many Kubernetes runtime tools rely on predefined rules and signatures - a fixed list of “bad” behaviors to match against. That approach struggles with novel attacks and generates noise at scale. Darktrace takes a different approach: our Adaptive AI builds a Unique Behavioral Profile for each organization from the ground up, learning the normal behavior of every workload, node and identity in your cluster. It then uses this understanding to detect meaningful deviations from expected behavior, which serves as the basis for surfacing suspicious and malicious activity.  

A pod reaching out to an unfamiliar destination, an unexpected executable launching inside a workload, or an identity performing an action it has never performed, all surface as anomalies without anyone having to write a rule in advance.

Darktrace surfaces unexpected Kubernetes activity that does not align with an organization’s Unique Behavioral Profile

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Visibility across the network, the process, and the control plane

Darktrace monitors Kubernetes at three levels that together provide comprehensive runtime visibility:

  1. Network-layer
    1. Traffic between pods, across nodes and out to the internet. This helps to detect lateral movement and unusual connectivity, whether internal or external.
  2. Process-layer
    1. What is executing inside your containers. If a process reads a sensitive credential file or spawns an unexpected shell inside a production pod, Darktrace can identify that activity and use it as context for further detections.
  3. Control plane
    1. Activity against the Kubernetes API itself: someone executes a command in a running pod, RBAC changes, or unusual administrative actions. Many high-impact security events involve the control plane, an area that is often less visible to solutions that only analyze or scan network-layer telemetry.

Misconfiguration and vulnerability awareness

Runtime detection is strongest when combined with posture and exposure management. Darktrace surfaces risky pod configurations, such as privileged containers, host networking, and over-permissive service accounts as workloads are deployed, and links CVEs from images to the workloads running them. Security teams can go beyond simply knowing that a vulnerability exists and see exactly where it is in the cluster. When combined with behavior-based detections, this provides invaluable context that can help prioritize incidents and proactively identify areas that need further attention. For example, anomalous workload behavior on its own might not be very interesting to a security team at first, but if that same workload is known to host sensitive data, is known to have critical vulnerabilities associated with it, or is publicly accessible, then this narrative becomes much more suspicious and demanding of their attention.

Darktrace highlights Kubernetes misconfigurations such as privileged containers, host networking, and missing security controls, helping teams prioritize runtime alerts with valuable exposure and risk context

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Response, tailored to Kubernetes

Detection is most effective when paired with response. Darktrace can take precise actions on Kubernetes workloads, including blocking a malicious connection at the pod level, helping to safely contain threats while security teams investigate. Response actions can be taken fully autonomously or via a human-in-the-loop process, which is completely configurable depending on the desired level of autonomy and scope. Darktrace is expanding the number of Kubernetes-native response actions to give customers even more flexibility when containing threats.

Forensics that survive the workload

Because containers are so short-lived, evidence disappears fast and this can make it very difficult to answer questions related to the initial detections. Darktrace solves this with Forensic Acquisition & Investigation capabilities that extend to Kubernetes containers, allowing investigations to be triggered manually by an analyst, or automatically from an alert, preserving evidence before the workload is gone.

For incident responders, this addresses a key challenge in container security: being able to answer “what actually happened?” after an ephemeral workload has vanished.

One platform, not another silo

Kubernetes doesn't exist in isolation, and neither should its security. Darktrace correlates cluster activity with the surrounding cloud environment, including AWS, Azure and GCP identities, network activity and cloud assets, allowing analysts to pivot between a Kubernetes workload and its wider context. A single anomalous action can be traced from the control plane to the pod, and from the pod to the cloud identity behind it, all within a single investigation workflow. This can also include alerts from other areas of hybrid infrastructure or third-party sources where relevant, adding more context to investigations and speeding up workflows for human analysts.

Built to deploy easily

Operational complexity can be a barrier to security adoption. Darktrace / HYBRID NETWORK for Kubernetes installs with a single command, and for teams that manage their infrastructure as code, it supports raw manifests so the deployment can live in a Git repository and be managed through existing GitOps workflows. The same deployment approach works across Kubernetes environments in AWS, Azure, Google Cloud, and on-premises infrastructure. Early adopters of Darktrace’s Kubernetes capabilities have highlighted the simplicity of deployment and time to value.

Darktrace can deploy across your Kubernetes environment and wider hybrid infrastructure, while also providing visibility into Kubernetes deployment structure and status

Next steps

Kubernetes environments are dynamic, ephemeral and API-driven in ways that challenge traditional security tooling. With Kubernetes usage increasing due to widespread AI adoption, it is essential to secure Kubernetes environments as part of securing what AI can do. Darktrace / HYBRID NETWORK addresses these challenges by learning normal behavior in Kubernetes environments across network, process and control-plane layers, identifying meaningful threats and enabling targeted response. Combined with the context of wider hybrid infrastructure and forensic investigation capabilities, Darktrace provides visibility into both emerging threats and active incidents.

To see how Darktrace can help secure your Kubernetes environment and your wider hybrid infrastructure, get in touch to arrange your free trial. Existing customers can contact their Darktrace Account Team for more information about deployment options and licensing.

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Sources

[1] Gartner, Container Management research, 2025: "By 2028, 95% of new AI deployments will use Kubernetes, up from less than 30% today."  https://www.gartner.com/en/documents/7913109. (quote reproduced under Gartner reprint at https://www.kubermatic.com/magic-quadrant-and-critical-capabilities-for-container-management/)

[2] Palo Alto Networks (Unit 42) – Container Breakouts: Escape Techniques in Cloud Environments. https://unit42.paloaltonetworks.com/container-escape-techniques/

[3] runC container-escape vulnerabilities (CVE-2025-31133, CVE-2025-52565, CVE-2025-52881), disclosed by a SUSE researcher on November 5, 2025. https://www.securityweek.com/runc-vulnerabilities-can-be-exploited-to-escape-containers/

[4] NVIDIAScape (CVE-2025-23266), CVSS 9.0. https://nvidia.custhelp.com/app/answers/detail/a_id/5659

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About the author
Elise Longhurst
Senior Product Executive
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