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May 20, 2024

Don’t Take the Bait: How Darktrace Keeps Microsoft Teams Phishing Attacks at Bay

In this blog we examine how Darktrace was able to detect and block malicious phishing emails sent via Microsoft Teams that were impersonating an international hotel chain.
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
Min Kim
Cyber Security Analyst
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20
May 2024

Social Engineering in Phishing Attacks

Faced with increasingly cyber-aware endpoint users and vigilant security teams, more and more threat actors are forced to think psychologically about the individuals they are targeting with their phishing attacks. Social engineering methods like taking advantage of the human emotions of their would-be victims, pressuring them to open emails or follow links or face financial or legal repercussions, and impersonating known and trusted brands or services, have become common place in phishing campaigns in recent years.

Phishing with Microsoft Teams

The malicious use of the popular communications platform Microsoft Teams has become widely observed and discussed across the threat landscape, with many organizations adopting it as their primary means of business communication, and many threat actors using it as an attack vector. As Teams allows users to communicate with people outside of their organization by default [1], it becomes an easy entry point for potential attackers to use as a social engineering vector.

In early 2024, Darktrace/Apps™ identified two separate instances of malicious actors using Microsoft Teams to launch a phishing attack against Darktrace customers in the Europe, the Middle East and Africa (EMEA) region. Interestingly, in this case the attackers not only used a well-known legitimate service to carry out their phishing campaign, but they were also attempting to impersonate an international hotel chain.

Despite these attempts to evade endpoint users and traditional security measures, Darktrace’s anomaly detection enabled it to identify the suspicious phishing messages and bring them to the customer’s attention. Additionally, Darktrace’s autonomous response capability, was able to follow-up these detections with targeted actions to contain the suspicious activity in the first instance.

Darktrace Coverage of Microsoft Teams Phishing

Chats Sent by External User and Following Actions by Darktrace

On February 29, 2024, Darktrace detected the presence of a new external user on the Software-as-a-Service (SaaS) environment of an EMEA customer for the first time. The user, “REDACTED@InternationalHotelChain[.]onmicrosoft[.]com” was only observed on this date and no further activities were detected from this user after February 29.

Later the same day, the unusual external user created its first chat on Microsoft Teams named “New Employee Loyalty Program”. Over the course of around 5 minutes, the user sent 63 messages across 21 different chats to unique internal users on the customer’s SaaS platform. All these chats included the ‘foreign tenant user’ and one of the customer’s internal users, likely in an attempt to remain undetected. Foreign tenant user, in this case, refers to users without access to typical internal software and privileges, indicating the presence of an external user.

Darktrace’s detection of unusual messages being sent by a suspicious external user via Microsoft Teams.
Figure 1: Darktrace’s detection of unusual messages being sent by a suspicious external user via Microsoft Teams.
Advanced Search results showing the presence of a foreign tenant user on the customer’s SaaS environment.
Figure 2: Advanced Search results showing the presence of a foreign tenant user on the customer’s SaaS environment.

Darktrace identified that the external user had connected from an unusual IP address located in Poland, 195.242.125[.]186. Darktrace understood that this was unexpected behavior for this user who had only previously been observed connecting from the United Kingdom; it further recognized that no other users within the customer’s environment had connected from this external source, thereby deeming it suspicious. Further investigation by Darktrace’s analyst team revealed that the endpoint had been flagged as malicious by several open-source intelligence (OSINT) vendors.

External Summary highlighting the rarity of the rare external source from which the Teams messages were sent.
Figure 3: External Summary highlighting the rarity of the rare external source from which the Teams messages were sent.

Following Darktrace’s initial detection of these suspicious Microsoft Teams messages, Darktrace's autonomous response was able to further support the customer by providing suggested mitigative actions that could be applied to stop the external user from sending any additional phishing messages.

Unfortunately, at the time of this attack Darktrace's autonomous response capability was configured in human confirmation mode, meaning any autonomous response actions had to be manually actioned by the customer. Had it been enabled in autonomous response mode, it would have been able promptly disrupt the attack, disabling the external user to prevent them from continuing their phishing attempts and securing precious time for the customer’s security team to begin their own remediation procedures.

Darktrace autonomous response actions that were suggested following the ’Large Volume of Messages Sent from New External User’ detection model alert.
Figure 4: Darktrace autonomous response actions that were suggested following the ’Large Volume of Messages Sent from New External User’ detection model alert.

External URL Sent within Teams Chats

Within the 21 Teams chats created by the threat actor, Darktrace identified 21 different external URLs being sent, all of which included the domain "cloud-sharcpoint[.]com”. Many of these URLs had been recently established and had been flagged as malicious by OSINT providers [3]. This was likely an attempt to impersonate “cloud-sharepoint[.]com”, the legitimate domain of Microsoft SharePoint, with the threat actor attempting to ‘typo-squat’ the URL to convince endpoint users to trust the legitimacy of the link. Typo-squatted domains are commonly misspelled URLs registered by opportunistic attackers in the hope of gaining the trust of unsuspecting targets. They are often used for nefarious purposes like dropping malicious files on devices or harvesting credentials.

Upon clicking this malicious link, users were directed to a similarly typo-squatted domain, “InternatlonalHotelChain[.]sharcpoInte-docs[.]com”. This domain was likely made to appear like the SharePoint URL used by the international hotel chain being impersonated.

Redirected link to a fake SharePoint page attempting to impersonate an international hotel chain.
Figure 5: Redirected link to a fake SharePoint page attempting to impersonate an international hotel chain.

This fake SharePoint page used the branding of the international hotel chain and contained a document named “New Employee Loyalty Program”; the same name given to the phishing messages sent by the attacker on Microsoft Teams. Upon accessing this file, users would be directed to a credential harvester, masquerading as a Microsoft login page, and prompted to enter their credentials. If successful, this would allow the attacker to gain unauthorized access to a user’s SaaS account, thereby compromising the account and enabling further escalation in the customer’s environment.

Figure 6: A fake Microsoft login page that popped-up when attempting to open the ’New Employee Loyalty Program’ document.

This is a clear example of an attacker attempting to leverage social engineering tactics to gain the trust of their targets and convince them to inadvertently compromise their account. Many corporate organizations partner with other companies and well-known brands to offer their employees loyalty programs as part of their employment benefits and perks. As such, it would not necessarily be unexpected for employees to receive such an offer from an international hotel chain. By impersonating an international hotel chain, threat actors would increase the probability of convincing their targets to trust and click their malicious messages and links, and unintentionally compromising their accounts.

In spite of the attacker’s attempts to impersonate reputable brands, platforms, Darktrace/Apps was able to successfully recognize the malicious intent behind this phishing campaign and suggest steps to contain the attack. Darktrace recognized that the user in question had deviated from its ‘learned’ pattern of behavior by connecting to the customer’s SaaS environment from an unusual external location, before proceeding to send an unusually large volume of messages via Teams, indicating that the SaaS account had been compromised.

A Wider Campaign?

Around a month later, in March 2024, Darktrace observed a similar incident of a malicious actor impersonating the same international hotel chain in a phishing attacking using Microsoft Teams, suggesting that this was part of a wider phishing campaign. Like the previous example, this customer was also based in the EMEA region.  

The attack tactics identified in this instance were very similar to the previously example, with a new external user identified within the network proceeding to create a series of Teams messages named “New Employee Loyalty Program” containing a typo-squatted external links.

There were a few differences with this second incident, however, with the attacker using the domain “@InternationalHotelChainExpeditions[.]onmicrosoft[.]com” to send their malicious Teams messages and using differently typo-squatted URLs to imitate Microsoft SharePoint.

As both customers targeted by this phishing campaign were subscribed to Darktrace’s Proactive Threat Notification (PTN) service, this suspicious SaaS activity was promptly escalated to the Darktrace Security Operations Center (SOC) for immediate triage and investigation. Following their investigation, the SOC team sent an alert to the customers informing them of the compromise and advising urgent follow-up.

Conclusion

While there are clear similarities between these Microsoft Teams-based phishing attacks, the attackers here have seemingly sought ways to refine their tactics, techniques, and procedures (TTPs), leveraging new connection locations and creating new malicious URLs in an effort to outmaneuver human security teams and conventional security tools.

As cyber threats grow increasingly sophisticated and evasive, it is crucial for organizations to employ intelligent security solutions that can see through social engineering techniques and pinpoint suspicious activity early.

Darktrace’s Self-Learning AI understands customer environments and is able to recognize the subtle deviations in a device’s behavioral pattern, enabling it to effectively identify suspicious activity even when attackers adapt their strategies. In this instance, this allowed Darktrace to detect the phishing messages, and the malicious links contained within them, despite the seemingly trustworthy source and use of a reputable platform like Microsoft Teams.

Credit to Min Kim, Cyber Security Analyst, Raymond Norbert, Cyber Security Analyst and Ryan Traill, Threat Content Lead

Appendix

Darktrace Model Detections

SaaS Model

Large Volume of Messages Sent from New External User

SaaS / Unusual Activity / Large Volume of Messages Sent from New External User

Indicators of Compromise (IoCs)

IoC – Type - Description

https://cloud-sharcpoint[.]com/[a-zA-Z0-9]{15} - Example hostname - Malicious phishing redirection link

InternatlonalHotelChain[.]sharcpolnte-docs[.]com – Hostname – Redirected Link

195.242.125[.]186 - External Source IP Address – Malicious Endpoint

MITRE Tactics

Tactic – Technique

Phishing – Initial Access (T1566)

References

[1] https://learn.microsoft.com/en-us/microsoftteams/trusted-organizations-external-meetings-chat?tabs=organization-settings

[2] https://www.virustotal.com/gui/ip-address/195.242.125.186/detection

[3] https://www.virustotal.com/gui/domain/cloud-sharcpoint.com

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
Min Kim
Cyber Security Analyst

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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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