Blog
/
Email
/
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
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
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

More in this series

No items found.

Blog

/

Email

/

May 1, 2026

How email-delivered prompt injection attacks can target enterprise AI – and why it matters

Default blog imageDefault blog image

What are email-delivered prompt injection attacks?

As organizations rapidly adopt AI assistants to improve productivity, a new class of cyber risk is emerging alongside them: email-delivered AI prompt injection. Unlike traditional attacks that target software vulnerabilities or rely on social engineering, this is the act of embedding malicious or manipulative instructions into content that an AI system will process as part of its normal workflow. Because modern AI tools are designed to ingest and reason over large volumes of data, including emails, documents, and chat histories, they can unintentionally treat hidden attacker-controlled text as legitimate input.  

At Darktrace, our analysis has shown an increase of 90% in the number of customer deployments showing signals associated with potential prompt injection attempts since we began monitoring for this type of activity in late 2025. While it is not always possible to definitively attribute each instance, internal scoring systems designed to identify characteristics consistent with prompt injection have recorded a growing number of high-confidence matches. The upward trend suggests that attackers are actively experimenting with these techniques.

Recent examples of prompt injection attacks

Two early examples of this evolving threat are HashJack and ShadowLeak, which illustrate prompt injection in practice.

HashJack is a novel prompt injection technique discovered in November 2025 that exploits AI-powered web browsers and agentic AI browser assistants. By hiding malicious instructions within the URL fragment (after the # symbol) of a legitimate, trusted website, attackers can trick AI web assistants into performing malicious actions – potentially inserting phishing links, fake contact details, or misleading guidance directly into what appears to be a trusted AI-generated output.

ShadowLeak is a prompt injection method to exfiltrate PII identified in September 2025. This was a flaw in ChatGPT (now patched by OpenAI) which worked via an agent connected to email. If attackers sent the target an email containing a hidden prompt, the agent was tricked into leaking sensitive information to the attacker with no user action or visible UI.

What’s the risk of email-delivered prompt injection attacks?

Enterprise AI assistants often have complete visibility across emails, documents, and internal platforms. This means an attacker does not need to compromise credentials or move laterally through an environment. If successful, they can influence the AI to retrieve relevant information seamlessly, without the labor of compromise and privilege escalation.

The first risk is data exfiltration. In a prompt injection scenario, malicious instructions may be embedded within an ordinary email. As in the ShadowLeak attack, when AI processes that content as part of a legitimate task, it may interpret the hidden text as an instruction. This could result in the AI disclosing sensitive data, summarizing confidential communications, or exposing internal context that would otherwise require significant effort to obtain.

The second risk is agentic workflow poisoning. As AI systems take on more active roles, prompt injection can influence how they behave over time. An attacker could embed instructions that persist across interactions, such as causing the AI to include malicious links in responses or redirect users to untrusted resources. In this way, the attacker inserts themselves into the workflow, effectively acting as a man-in-the-middle within the AI system.

Why can’t other solutions catch email-delivered prompt injection attacks?

AI prompt injection challenges many of the assumptions that traditional email security is built on. It does not fit the usual patterns of phishing, where the goal is to trick a user into clicking a link or opening an attachment.  

Most security solutions are designed to detect signals associated with user engagement: suspicious links, unusual attachments, or social engineering cues. Prompt injection avoids these indicators entirely, meaning there are fewer obvious red flags.

In this case, the intention is actually the opposite of user solicitation. The objective is simply for the email to be delivered and remain in the inbox, appearing benign and unremarkable. The malicious element is not something the recipient is expected to engage with, or even notice.

Detection is further complicated by the nature of the prompts themselves. Unlike known malware signatures or consistent phishing patterns, injected prompts can vary widely in structure and wording. This makes simple pattern-matching approaches, such as regex, unreliable. A broad rule set risks generating large numbers of false positives, while a narrow one is unlikely to capture the diversity of possible injections.

How does Darktrace catch these types of attacks?

The Darktrace approach to email security more generally is to look beyond individual indicators and assess context, which also applies here.  

For example, our prompt density score identifies clusters of prompt-like language within an email rather than just single occurrences. Instead of treating the presence of a phrase as a blocking signal, the focus is on whether there is an unusual concentration of these patterns in a way that suggests injection. Additional weighting can be applied where there are signs of obfuscation. For example, text that is hidden from the user – such as white font or font size zero – but still readable by AI systems can indicate an attempt to conceal malicious prompts.

This is combined with broader behavioral signals. The same communication context used to detect other threats remains relevant, such as whether the content is unusual for the recipient or deviates from normal patterns.

Ask your email provider about email-delivered AI prompt injection

Prompt injection targets not just employees, but the AI systems they rely on, so security approaches need to account for both.

Though there are clear indications of emerging activity, it remains to be seen how popular prompt injection will be with attackers going forward. Still, considering the potential impact of this attack type, it’s worth checking if this risk has been considered by your email security provider.

Questions to ask your email security provider

  • What safeguards are in place to prevent emails from influencing AI‑driven workflows over time?
  • How do you assess email content that’s benign for a human reader, but may carry hidden instructions intended for AI systems?
  • If an email contains no links, no attachments, and no social engineering cues, what signals would your platform use to identify malicious intent?

Visit the Darktrace / EMAIL product hub to discover how we detect and respond to advanced communication threats.  

Learn more about securing AI in your enterprise.

Continue reading
About the author
Kiri Addison
Senior Director of Product

Blog

/

AI

/

April 30, 2026

Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

mythos vulnerability discoveryDefault blog imageDefault blog image

Anthropic’s Mythos and what it means for security teams

Recent attention on systems such as Anthropic Mythos highlights a notable problem for defenders. Namely that disclosure’s role in coordinating defensive action is eroding.

As AI systems gain stronger reasoning and coding capability, their usefulness in analyzing complex software environments and identifying weaknesses naturally increases. What has changed is not attacker motivation, but the conditions under which defenders learn about and organize around risk. Vulnerability discovery and exploitation increasingly unfold in ways that turn disclosure into a retrospective signal rather than a reliable starting point for defense.

Faster discovery was inevitable and is already visible

The acceleration of vulnerability discovery was already observable across the ecosystem. Publicly disclosed vulnerabilities (CVEs) have grown at double-digit rates for the past two years, including a 32% increase in 2024 according to NIST, driven in part by AI even prior to Anthropic’s Mythos model. Most notably XBOW topped the HackerOne US bug bounty leaderboard, marking the first time an autonomous penetration tester had done so.  

The technical frontier for AI capabilities has been described elsewhere as jagged, and the implication is that Mythos is exceptional but not unique in this capability. While Mythos appears to make significant progress in complex vulnerability analysis, many other models are already able to find and exploit weaknesses to varying degrees.  

What matters here is not which model performs best, but the fact that vulnerability discovery is no longer a scarce or tightly bounded capability.

The consequence of this shift is not simply earlier discovery. It is a change in the defender-attacker race condition. Disclosure once acted as a rough synchronization point. While attackers sometimes had earlier knowledge, disclosure generally marked the moment when risk became visible and defensive action could be broadly coordinated. Increasingly, that coordination will no longer exist. Exploitation may be underway well before a CVE is published, if it is published at all.

Why patch velocity alone is not the answer

The instinctive response to this shift is to focus on patching faster, but treating patch velocity as the primary solution misunderstands the problem. Most organizations are already constrained in how quickly they can remediate vulnerabilities. Asset sprawl, operational risk, testing requirements, uptime commitments, and unclear ownership all limit response speed, even when vulnerabilities are well understood.

If discovery and exploitation now routinely precede disclosure, then patching cannot be the first line of defense. It becomes one necessary control applied within a timeline that has already shifted. This does not imply that organizations should patch less. It means that patching cannot serve as the organizing principle for defense.

Defense needs a more stable anchor

If disclosure no longer defines when defense begins, then defense needs a reference point that does not depend on knowing the vulnerability in advance.  

Every digital environment has a behavioral character. Systems authenticate, communicate, execute processes, and access resources in relatively consistent ways over time. These patterns are not static rules or signatures. They are learned behaviors that reflect how an organization operates.

When exploitation occurs, even via previously unknown vulnerabilities, those behavioral patterns change.

Attackers may use novel techniques, but they still need to gain access, create processes, move laterally, and will ultimately interact with systems in ways that diverge from what is expected. That deviation is observable regardless of whether the underlying weakness has been formally named.

In an environment where disclosure can no longer be relied on for timing or coordination, behavioral understanding is no longer an optional enhancement; it becomes the only consistently available defensive signal.

Detecting risk before disclosure

Darktrace’s threat research has consistently shown that malicious activity often becomes visible before public disclosure.

In multiple cases, including exploitation of Ivanti, SAP NetWeaver, and Trimble Cityworks, Darktrace detected anomalous behavior days or weeks ahead of CVE publication. These detections did not rely on signatures, threat intelligence feeds, or awareness of the vulnerability itself. They emerged because systems began behaving in ways that did not align with their established patterns.

This reflects a defensive approach grounded in ‘Ethos’, in contrast to the unbounded exploration represented by ‘Mythos’. Here, Mythos describes continuous vulnerability discovery at speed and scale. Ethos reflects an understanding of what is normal and expected within a specific environment, grounded in observed behavior.

Revisiting assume breach

These conditions reinforce a principle long embedded in Zero Trust thinking: assume breach.

If exploitation can occur before disclosure, patching vulnerabilities can no longer act as the organizing principle for defense. Instead, effective defense must focus on monitoring for misuse and constraining attacker activity once access is achieved. Behavioral monitoring allows organizations to identify early‑stage compromise and respond while uncertainty remains, rather than waiting for formal verification.

AI plays a critical role here, not by predicting every exploit, but by continuously learning what normal looks like within a specific environment and identifying meaningful deviation at machine speed. Identifying that deviation enables defenders to respond by constraining activity back towards normal patterns of behavior.

Not an arms race, but an asymmetry

AI is often framed as fueling an arms race between attackers and defenders. In practice, the more important dynamic is asymmetry.

Attackers operate broadly, scanning many environments for opportunities. Defenders operate deeply within their own systems, and it’s this business context which is so significant. Behavioral understanding gives defenders a durable advantage. Attackers may automate discovery, but they cannot easily reproduce what belonging looks like inside a particular organization.

A changed defensive model

AI‑accelerated vulnerability discovery does not mean defenders have lost. It does mean that disclosure‑driven, patch‑centric models no longer provide a sufficient foundation for resilience.

As vulnerability volumes grow and exploitation timelines compress, effective defense increasingly depends on continuous behavioral understanding, detection that does not rely on prior disclosure, and rapid containment to limit impact. In this model, CVEs confirm risk rather than define when defense begins.

The industry has already seen this approach work in practice. As AI continues to reshape both offense and defense, behavioral detection will move from being complementary to being essential.

Continue reading
About the author
Andrew Hollister
Principal Solutions Engineer, Cyber Technician
Your data. Our AI.
Elevate your network security with Darktrace AI