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March 24, 2021

Detecting Vendor Email Compromise in Supply Chain Attacks

Learn how Antigena Email stopped a supply chain attack by identifying a behavioral shift in the emails, while still allowing legitimate traffic through.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Dan Fein
VP, Product
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24
Mar 2021

Supply chain account takeover – also known as Vendor Email Compromise — is the single most pressing issue facing email security at the moment. There has been a wave of high-profile supply chain attacks recently and this trend will only increase over 2021 as cyber-criminals continue to find success in such methods. Traditional security tools are powerless to stop such attacks: since the malicious emails come from trusted partners and suppliers, they slip through the gateway.

The recent SolarWinds cyber-attack shows just how devastating a supply chain compromise can be. Business partnerships are more complex than ever, which means that a single breach can affect dozens of organizations at all levels of a global supply chain, from small local companies to government departments.

This blog examines a compromised third-party provider which sent malicious emails to a customer trialing Antigena Email. Despite the emails coming from a trusted source, the AI technology recognized a behavioral shift – that they were anomalous when compared with the previous behavior of the sender. When the contact realized their account had been compromised and issued a warning via email to their regular contacts, Antigena recognized these emails as benign. But as the attacker continued sending emails of a malicious nature on their account, those threatening emails, which bypassed the company’s other security tools, were held back by Darktrace.

Supply chain compromise

Let’s set the scene. The company in question is one of the world’s largest beverage suppliers with around 15,000 email users alone. It has a sprawling global supply chain with many partners and trusted vendors. After one of these third parties was compromised, malicious actors sent phishing emails across the supply chain to compromise as many organizations as they could. Our beverage company was one of the main targets.

The trusted relationship with the third party can be seen through Darktrace’s Email tags, which were applied to the previous legitimate emails sent in from the supplier.

Figure 1: A snapshot of Antigena Email’s interface

Shortly after this legitimate email was seen, a wave of new emails came in from the same account. Using this authentic traffic to their advantage, the attacker successfully evaded other tools and blended into legitimate communications. In supply chain attacks, such camouflage is key to a successful compromise.

However, due to slight changes in the behavior of the sender when compared with their previous history, Darktrace was able to identify that the account had been compromised, shown by the Out of Character and Suspicious Link tags.

Figure 2: A snapshot of Antigena Email’s interface

Darktrace’s AI detected a range of anomalies, including:

  • the source of the email;
  • the nature of the links and their association with the company;
  • the language and intention of the email body.

These extremely subtle shifts in email can only be detected by using Darktrace’s sophisticated unsupervised machine learning approach. By understanding how the user typically acts and interacts with their peers and other organizations, Darktrace’s AI is able to identify anomalous behavior that indicates account takeover and impersonation.

Attack breakdown: Doubting the trustworthy

In the emails, there was a link directing the user to a legitimate file storage site (‘canva.com’), which was being used to host a malicious payload. This tactic is commonly used by cyber-criminals to bypass legacy security gateways, because traditional gateways fail to defend against malicious file storage links using reputation checks, since the domains themselves are legitimate.

Antigena Email, however, identified that this email did not belong and flagged the email as 100% anomalous. The figure below shows three of the malicious emails, and the scores assigned to them by Antigena Email’s machine learning. The top email is the supplier emailing the company legitimately, to warn of the compromised account. Despite arriving shortly after the malicious emails, the email was given only a 31% threat score, with no actions suggested. This proves just how valuable Antigena Email is not only in stopping the bad emails, but also in accurately identifying benign emails – ensuring it does not interfere with legitimate communication.

Figure 3: Antigena Email clearly distinguishes the benign communication from harmful emails

Antigena Email’s action of choice was to hold the emails back from the inbox entirely, protecting the recipient from any potential solicitation or attempt to take communications to a less secure platform.

Blind trust: The rise of supply chain attacks

This year will undoubtedly see the highly lucrative supply chain fraud continue to rise, as cyber-criminals focus on the weakest links and target partners and suppliers to gain a foothold in a range of organizations. In fact, it is likely that supply chain attacks will become more common than CEO fraud this year. Whereas the C-suite are often well protected by vigilant security teams, third parties offer a plethora of ways into a company. Security teams are blind to third-party environments and gateways rarely flag emails which come from legitimate sources.

Antigena Email is the only email security technology which looks at each individual email in the wider context of the organization, the recipient, and past interactions with the sender, stopping anomalous emails sent with malicious intent, no matter who has sent them.

Learn more about how Cyber AI can defend the inbox: Read the White Paper.

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

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May 8, 2025

Anomaly-based threat hunting: Darktrace's approach in action

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What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

  • MITRE Attack
  • Tactics, Techniques, Procedures (TTPs)
  • Diamond Model for Intrusion Analysis
  • Adversary, Infrastructure, Victims, Capabilities
  • Threat Hunt Model – Six Steps
  • Purpose, Scope, Equip, Plan, Execute, Feedback
  • Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

  • Hypothesis-based threat hunting
  • Analytics-driven threat hunting
  • Automated/machine learning hunting
  • Indicator of Compromise (IoC) hunting
  • Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

References

  1. https://spur.us/context/191.96.106.219

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

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With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

[related-resource]

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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