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Log4Shell Vulnerability Detection & Response With Darktrace

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14
Dec 2021
14
Dec 2021
Learn how Darktrace's AI detects and responds to Log4Shell attacks. Explore real-world examples and see how Darktrace identified and mitigated cyber threats.

En el blog echaremos un vistazo a la vulnerabilidad de Log4Shell y proporcionaremos ejemplos reales de cómo Darktrace detecta y responde a los ataques que intentan aprovechar Log4Shell.

Log4Shell es el ya famoso nombre de CVE-2021-44228, una vulnerabilidad de día cero de gravedad 10 que aprovecha una conocida utilidad de registro de Java conocida como Log4J. Las vulnerabilidades se descubren diariamente y algunas son más graves que otras, pero el hecho de que esta utilidad de código abierto está anidada en casi todo, incluyendo el dron de Mars Ingenuity, hace que esto sea mucho más amenazador. Los detalles y las actualizaciones adicionales sobre Log4Shell todavía están emergiendo en la fecha de la publicación de este blog.

Normalmente, los días cero con el poder de alcanzar estos muchos sistemas no corren riesgos y solo los estados nacionales los utilizan para objetivos u operaciones de alto valor. Este, sin embargo, se descubrió por primera vez que se utilizaba contra los servidores de juegos de Minecraft, compartidos en el chat entre los jugadores.

Aunque se deben tomar todos los pasos para implementar las mitigaciones en la vulnerabilidad Log4Shell, estas pueden tomar tiempo. Como se evidencia aquí, la detección de comportamiento se puede utilizar para buscar señales de actividad post-explotación como exploración, minería de monedas, movimiento lateral y otras actividades.

Darktrace detectó inicialmente la vulnerabilidad de Log4Shell dirigida a uno de los servidores orientados a Internet de nuestros clientes, como verá en detalle a continuación en una investigación de amenazas anonimizadas. Esto se destacó y se informó mediante Cyber AI Analyst, desempacado aquí por nuestro equipo de SOC. Tenga en cuenta que estaba utilizando algoritmos preexistentes sin clasificadores de reentrenamiento ni mecanismos de respuesta de ajuste en reacción a los ciberataques de Log4Shell.

Cómo funciona Log4Shell

La vulnerabilidad funciona al aprovechar la validación incorrecta de entradas por parte de Java Naming and Directory Interface (JNDI, por sus siglas en inglés). Un comando procede de un agente de usuario HTTP, una conexión HTTPS cifrada o incluso un mensaje de sala de chat y JNDI lo envía al sistema de destino en el que se ejecuta. La mayoría de las bibliotecas y aplicaciones tienen controles y protecciones para evitar que esto suceda, pero como se ve aquí, a veces se pierden.

Varios ciberdelincuentes han comenzado a aprovechar la vulnerabilidad en sus ataques, desde mediante campañas indiscriminadas de criptografía y minería hasta ataques dirigidos y más sofisticados.

Ejemplo del mundo real 1: Log4Shell implementado en la fecha de lanzamiento del ID de CVE

Darktrace vio este primer ejemplo el 10 de diciembre, el mismo día en que se publicó el ID de CVE. A menudo vemos vulnerabilidades públicamente documentadas que están siendo armadas en pocos días por ciberdelincuentes. Este ataque afectó a un dispositivo orientado a Internet en la zona desmilitarizada (DMZ) de una organización. Darktrace había clasificado automáticamente el servidor como un dispositivo orientado a Internet en función de su comportamiento.

La organización había implementado Darktrace en la red de la empresa como una de las muchas áreas de cobertura que incluyen la nube, el correo electrónico y SaaS. En esta implementación, Darktrace tenía una buena visibilidad del tráfico DMZ. Antigena no estaba activo en este entorno y Darktrace solo estaba en modo de detección. A pesar de esto, el cliente en cuestión pudo identificar y remediar este incidente en pocas horas tras la alerta inicial. El ataque fue automatizado y tuvo el objetivo de desplegar un criptominero conocido como Kinsing.

En este ataque, el atacante hizo más difícil detectar el ataque mediante el cifrado de la inyección inicial de comandos mediante HTTPS sobre el HTTP más común que se ve habitualmente. A pesar de que este método podía omitir las reglas tradicionales y los sistemas basados en firmas, Darktrace pudo detectar varios comportamientos inusuales segundos después de la conexión inicial.

Detalles del ataque inicial

A través del análisis por pares, Darktrace había aprendido previamente lo que este dispositivo DMZ específico y su grupo de pares hacen normalmente en el entorno. Durante la explotación inicial, Darktrace detectó varias anomalías sutiles que juntas hicieron obvio el ataque.

  1. 15:45:32 Conexión HTTPS entrante al servidor DMZ desde una dirección IP rusa rara — 45.155.205[.]233;
  2. 15:45:38 El servidor DMZ realiza una nueva conexión saliente a la misma IP rusa rara mediante dos nuevos agentes de usuario: Agente de usuario Java y "curl" sobre un puerto que es inusual para servir HTTP en comparación con el comportamiento anterior;
  3. 15:45:39 El servidor DMZ utiliza una conexión HTTP con otro nuevo agente de usuario de cURL ('curl/7.47.0') a la misma IP rusa. El URI contiene información de reconocimiento del servidor DMZ.

Toda esta actividad se detectó no porque Darktrace la hubiera visto antes, sino porque se desviaba fuertemente del “patrón de vida” regular para este y otros servidores similares en esta organización específica.

Este servidor nunca llegó a direcciones IP raras en Internet, sino que utilizó agentes de usuario que nunca antes se habían utilizado, a través de combinaciones de protocolo y puerto que no utiliza habitualmente. Cada anomalía puntual puede haber presentado un comportamiento ligeramente inusual, pero, en conjunto y analizado en el contexto de este dispositivo y entorno en particular, las detecciones cuentan claramente una historia más amplia de un ciberataque en curso.

Darktrace detectó esta actividad con varios modelos, por ejemplo:

  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Connection / Callback on Web Facing Device

Descarga de herramientas adicionales y criptominería

Menos de 90 minutos después del ataque inicial, el servidor infectado comenzó a descargar secuencias de comandos y ejecutables malintencionados de una rara IP ucraniana 80.71.158[.]12.

Las siguientes cargas útiles se descargaron posteriormente desde la IP de Ucrania en orden:

  • hXXp://80.71.158[.]12//lh.sh
  • hXXp://80.71.158[.]12/Expl[REDACTED].class
  • hXXp://80.71.158[.]12/kinsing
  • hXXp://80.71.158[.]12//libsystem.so
  • hXXp://80.71.158[.]12/Expl[REDACTED].class

Sin utilizar inteligencia de amenazas ni detecciones basadas en indicadores estáticos de compromiso (IOC) como IP, nombres de dominio o hash de archivo, Darktrace detectó este siguiente paso en el ataque en tiempo real.

El servidor DMZ en cuestión nunca se había comunicado con esta dirección IP ucraniana en el pasado a través de estos puertos poco comunes. También era muy inusual que este dispositivo y sus pares descargaran secuencias de comandos o archivos ejecutables de este tipo de destino externo y de esta manera. Poco después de estas descargas, el servidor DMZ comenzó la criptominería.

Darktrace detectó esta actividad con varios modelos, por ejemplo:

  • Anomalous File / Script from Rare External Location
  • Anomalous File / Internet Facing System File Download
  • Device / Internet Facing System with High Priority Alert

Detección inmediata de la vulnerabilidad Log4Shell

Además de que Darktrace detectara cada paso individual de este ataque en tiempo real, Darktrace Cyber AI Analyst también hizo surgir el incidente de seguridad general, que contenía una narrativa coherente para el ataque general, como el incidente de mayor prioridad en una semana de incidentes y alertas en Darktrace. Esto significa que este incidente fue el tema más obvio e inmediato destacado a los equipos de seguridad humana a medida que se fue desarrollando. Cyber AI Analyst de Darktrace encontró cada etapa de este incidente y formuló las mismas preguntas que esperaría de sus analistas humanos del SOC. A partir del informe en lenguaje natural generado por Cyber AI Analyst, se presenta (en un formato fácil de entender) un resumen de cada etapa del incidente seguido de los puntos de datos vitales que necesitan los analistas humanos. Cada pestaña significa una parte diferente de esta incidencia y describe los pasos reales que se han tomado durante cada proceso de investigación.

El resultado es que no hay que pasar por alertas de bajo nivel, no hay necesidad de clasificar las detecciones puntuales, no hay que colocar las detecciones en un contexto de incidentes más grande, no hay necesidad de escribir un informe. Todo esto fue completado automáticamente por AI Analyst, ahorrando a los equipos humanos un valioso tiempo.

El siguiente informe de incidencias se creó automáticamente y se puede descargar en formato PDF en varios idiomas.

Ilustración 1: Cyber AI Analyst de Darktrace aborda varias etapas del ataque y explica su proceso de investigación

Ejemplo del mundo real 2: Respuesta a un ataque diferente de Log4Shell

El 12 de diciembre, el servidor orientado a Internet de otra organización se vio atacado inicialmente a través de Log4Shell. Si bien los detalles de lo que se vio afectado son diferentes (hay otras IOC involucradas), Darktrace detectó e hizo emerger el ataque de forma similar al primer ejemplo.

Curiosamente, esta organización tenía a Darktrace Antigena en modo autónomo en su servidor, lo que significa que la IA pudo tomar acciones de forma autónoma para responder a los ciberataques. Estas respuestas se pueden entregar a través de una variedad de mecanismos, por ejemplo, interacciones API con firewalls y otras herramientas de seguridad, o respuestas nativas emitidas por Darktrace.

En este ataque, la nada habitual IP externa 164.52.212[.]196 se utilizó para la comunicación de comando y control (C2) y la entrega de malware, utilizando HTTP sobre el puerto 88, lo que era muy inusual para este dispositivo, grupo de pares y organización.

Antigena reaccionó en tiempo real en esta organización, basándose en el contexto específico del ataque, sin ningún ser humano en el medio. Antigena interactuó con el firewall de la empresa en este caso para bloquear cualquier conexión hacia o desde la dirección IP maliciosa (en este caso 164.52.212[.]196) a través del puerto 88 durante 2 horas con la opción de escalar el bloque y la duración si el ataque parecía persistir. Esto se puede ver en la siguiente ilustración:

Ilustración 2: La respuesta de Antigena

Aquí viene el truco: gracias a la IA de autoaprendizaje, Darktrace sabe exactamente lo que el servidor orientado a Internet suele hacer y no, incluidos los más mínimos detalles. En base a las diversas anomalías, Darktrace está seguro de que esto representa un gran ciberataque.

Antigena ahora se encarga de aplicar el patrón de vida habitual de este servidor en la DMZ. Esto significa que el servidor puede seguir haciendo lo que normalmente hace, pero todas las acciones altamente anómalas se interrumpen a medida que se producen en tiempo real, como hablar con una rara IP externa a través del puerto 88 que sirve HTTP para descargar ejecutables.

Por supuesto, el ser humano puede cambiar o levantar el bloqueo en cualquier momento dado. Antigena también se puede configurar para que esté en modo de confirmación humana, teniendo a una persona presente a ciertas horas durante el día (por ejemplo, horas de oficina) o en todo momento, dependiendo de las necesidades y requisitos de una organización.

Conclusion

Este blog ilustra otros aspectos de los ciberataques que aprovechan la vulnerabilidad de Log4Shell. También demuestra cómo Darktrace detecta y responde a los ataques de día cero si Darktrace tiene visibilidad de las entidades atacadas.

Si bien Log4Shell domina las noticias de TI y seguridad, en el pasado han surgido vulnerabilidades similares que aparecerán en el futuro. Hemos hablado de nuestro enfoque para detectar y responder a vulnerabilidades similares y ataques cibernéticos en torno, por ejemplo:

  • la 'reciente vulnerabilidad Gitlab;
  • las vulnerabilidades de ProxyShell Exchange Server cuando todavía estaban a día cero;
  • y la vulnerabilidad de Citrix Netscaler

Como siempre, las empresas deben buscar una estrategia de defensa en profundidad que combine controles de seguridad preventivos con mecanismos de detección y respuesta, así como una sólida gestión de parches.

Gracias a Brianna Leddy (Directora de análisis de Darktrace) por sus conocimientos sobre el hallazgo de las amenazas anteriormente citadas.

DENTRO DEL SOC
Darktrace son expertos de talla mundial en inteligencia de amenazas, caza de amenazas y respuesta a incidentes, y proporcionan apoyo al SOC las 24 horas del día a miles de clientes de Darktrace en todo el mundo. Inside the SOC está redactado exclusivamente por estos expertos y ofrece un análisis de los ciberincidentes y las tendencias de las amenazas, basado en la experiencia real sobre el terreno.
AUTOR
SOBRE EL AUTOR
Max Heinemeyer
Chief Product Officer

Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.

Justin Fier
SVP, Red Team Operations

Justin is one of the US’s leading cyber intelligence experts, and holds the position of SVP, Red Team Operations at Darktrace. His insights on cyber security and artificial intelligence have been widely reported in leading media outlets, including the Wall Street Journal, CNN, The Washington Post, and VICELAND. With over 10 years’ experience in cyber defense, Justin has supported various elements in the US intelligence community, holding mission-critical security roles with Lockheed Martin, Northrop Grumman Mission Systems and Abraxas. Justin is also a highly-skilled technical specialist, and works with Darktrace’s strategic global customers on threat analysis, defensive cyber operations, protecting IoT, and machine learning.

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Email

How to Protect your Organization Against Microsoft Teams Phishing Attacks

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21
May 2024

The problem: Microsoft Teams phishing attacks are on the rise

Around 83% of Fortune 500 companies rely on Microsoft Office products and services1, with Microsoft Teams and Microsoft SharePoint in particular emerging as critical platforms to the business operations of the everyday workplace. Researchers across the threat landscape have begun to observe these legitimate services being leveraged more and more by malicious actors as an initial access method.

As Teams becomes a more prominent feature of the workplace many employees rely on it for daily internal and external communication, even surpassing email usage in some organizations. As Microsoft2 states, "Teams changes your relationship with email. When your whole group is working in Teams, it means you'll all get fewer emails. And you'll spend less time in your inbox, because you'll use Teams for more of your conversations."

However, Teams can be exploited to send targeted phishing messages to individuals either internally or externally, while appearing legitimate and safe. Users might receive an external message request from a Teams account claiming to be an IT support service or otherwise affiliated with the organization. Once a user has accepted, the threat actor can launch a social engineering campaign or deliver a malicious payload. As a primarily internal tool there is naturally less training and security awareness around Teams – due to the nature of the channel it is assumed to be a trusted source, meaning that social engineering is already one step ahead.

Screenshot of a Microsoft Teams message request from a Midnight Blizzard-controlled account (courtesy of Microsoft)
Figure 1: Screenshot of a Microsoft Teams message request from a Midnight Blizzard-controlled account (courtesy of Microsoft)

Microsoft Teams Phishing Examples

Microsoft has identified several major phishing attacks using Teams within the past year.

In July 2023, Microsoft announced that the threat actor known as Midnight Blizzard – identified by the United States as a Russian state-sponsored group – had launched a series of phishing campaigns via Teams with the aim of stealing user credentials. These attacks used previously compromised Microsoft 365 accounts and set up new domain names that impersonated legitimate IT support organizations. The threat actors then used social engineering tactics to trick targeted users into sharing their credentials via Teams, enabling them to access sensitive data.  

At a similar time, threat actor Storm-0324 was observed sending phishing lures via Teams containing links to malicious SharePoint-hosted files. The group targeted organizations that allow Teams users to interact and share files externally. Storm-0324’s goal is to gain initial access to hand over to other threat actors to pursue more dangerous follow-on attacks like ransomware.

For a more in depth look at how Darktrace stops Microsoft Teams phishing read our blog: Don’t Take the Bait: How Darktrace Keeps Microsoft Teams Phishing Attacks at Bay

The market: Existing Microsoft Teams security solutions are insufficient

Microsoft’s native Teams security focuses on payloads, namely links and attachments, as the principal malicious component of any phishing. These payloads are relatively straightforward to detect with their experience in anti-virus, sandboxing, and IOCs. However, this approach is unable to intervene before the stage at which payloads are delivered, before the user even gets the chance to accept or deny an external message request. At the same time, it risks missing more subtle threats that don’t include attachments or links – like early stage phishing, which is pure social engineering – or completely new payloads.

Equally, the market offering for Teams security is limited. Security solutions available on the market are always payload-focused, rather than taking into account the content and context in which a link or attachment is sent. Answering questions like:

  • Does it make sense for these two accounts to speak to each other?
  • Are there any linguistic indicators of inducement?

Furthermore, they do not correlate with email to track threats across multiple communication environments which could signal a wider campaign. Effectively, other market solutions aren’t adding extra value – they are protecting against the same types of threats that Microsoft is already covering by default.

The other aspect of Teams security that native and market solutions fail to address is the account itself. As well as focusing on Teams threats, it’s important to analyze messages to understand the normal mode of communication for a user, and spot when a user’s Teams activity might signal account takeover.

The solution: How Darktrace protects Microsoft Teams against sophisticated threats

With its biggest update to Darktrace/Email ever, Darktrace now offers support for Microsoft Teams. With that, we are bringing the same AI philosophy that protects your email and accounts to your messaging environment.  

Our Self-Learning AI looks at content and context for every communication, whether that’s sent in an email or Teams message. It looks at actual user behavior, including language patterns, relationship history of sender and recipient, tone and payloads, to understand if a message poses a threat. This approach allows Darktrace to detect threats such as social engineering and payloadless attacks using visibility and forensic capabilities that Microsoft security doesn’t currently offer, as well as early symptoms of account compromise.  

Unlike market solutions, Darktrace doesn’t offer a siloed approach to Teams security. Data and signals from Teams are shared across email to inform detection, and also with the wider Darktrace ActiveAI security platform. By correlating information from email and Teams with network and apps security, Darktrace is able to better identify suspicious Teams activity and vice versa.  

Interested in the other ways Darktrace/Email augments threat detection? Read our latest blog on how improving the quality of end-user reporting can decrease the burden on the SOC. To find our more about Darktrace's enduring partnership with Microsoft, click here.

References

[1] Essential Microsoft Office Statistics in 2024

[2] Microsoft blog, Microsoft Teams and email, living in harmony, 2024

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About the author
Carlos Gray
Product Manager

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Dentro del SOC

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

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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

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About the author
Min Kim
Cyber Security Analyst
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