How to Protect your Organization Against Microsoft Teams Phishing Attacks
21
May 2024
21
May 2024
In recent months, we’ve seen a dramatic rise in the number of attacks using Microsoft Teams as a threat vector. This blog will explore why Teams is becoming such a popular entry point, how built-in and market security offerings fail to address sophisticated Teams threats, and why behavioral AI is the solution to early detection of Teams-based social engineering and account compromise.
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.
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.
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.
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.
AUTHOR
ABOUT ThE AUTHOR
Carlos Gray
Product Manager
Carlos Gonzalez Gray is a Product Marketing Manager at Darktrace. Based in the Madrid Office, Carlos engages with the global product team to ensure each product supports the company’s overall strategy and goals throughout their entire lifecycle. Previous to his position in the product team, Carlos worked as a Cyber Technology Specialist where he specialized in the OT sector protecting critical infrastructure. His background as a consultant in Spain to IBEX 35 companies led him to become well versed in matters of compliance, auditing and data privacy as well. Carlos holds an Honors BA in Political Science and a Masters in Cybersecurity from IE University.
Safeguarding Distribution Centers in the Digital Age
12
Jun 2024
Challenges securing distribution centers
For large retail providers, e-commerce organizations, logistics & supply chain organizations, and other companies who rely on the distribution of goods to consumers cybersecurity efforts are often focused on an immense IT infrastructure. However, there's a critical, often overlooked segment of infrastructure that demands vigilant monitoring and robust protection: distribution centers.
Distribution centers play a critical role in the business operations of supply chains, logistics, and the retail industry. They serve as comprehensive logistics hubs, with many organizations operating multiple centers worldwide to meet consumer needs. Depending on their size and hours of operation, even just one hour of downtime at these centers can result in significant financial losses, ranging from tens to hundreds of thousands of dollars per hour.
Due to the time-sensitive nature and business criticality of distribution centers, there has been a rise in applying modern technologies now including AI applications to enhance efficiency within these facilities. Today’s distribution centers are increasingly connected to Enterprise IT networks, the cloud and the internet to manage every stage of the supply chain. Additionally, it is common for organizations to allow 3rd party access to the distribution center networks and data for reasons including allowing them to scale their operations effectively.
However, this influx of new technologies and interconnected systems across IT, OT and cloud introduces new risks on the cybersecurity front. Distribution center networks include industrial operational technologies ICS/OT, IoT technologies, enterprise network technology, and cloud systems working in coordination. The convergence of these technologies creates a greater chance that blind spots exist for security practitioners and this increasing presence of networked technology increases the attack surface and potential for vulnerability. Thus, having cybersecurity measures that cover IT, OT or Cloud alone is not enough to secure a complex and dynamic distribution center network infrastructure.
The OT network encompasses various systems, devices, hardware, and software, such as:
Enterprise Resource Planning (ERP)
Warehouse Execution System (WES)
Warehouse Control System (WCS)
Warehouse Management System (WMS)
Energy Management Systems (EMS)
Building Management Systems (BMS)
Distribution Control Systems (DCS)
Enterprise IT devices
OT and IoT: Engineering workstations, ICS application and management servers, PLCs, HMI, access control, cameras, and printers
Cloud applications
Distribution centers: An expanding attack surface
As these distribution centers have become increasingly automated, connected, and technologically advanced, their attack surfaces have inherently increased. Distribution centers now have a vastly different potential for cyber risk which includes:
More networked devices present
Increased routable connectivity within industrial systems
Externally exposed industrial control systems
Increased remote access
IT/OT enterprise to industrial convergence
Cloud connectivity
Contractors, vendors, and consultants on site or remoting in
Given the variety of connected systems, distribution centers are more exposed to external threats than ever before. Simultaneously, distribution center’s business criticality has positioned them as interesting targets to cyber adversaries seeking to cause disruption with significant financial impact.
Increased connectivity requires a unified security approach
When assessing the unique distribution center attack surface, the variety of interconnected systems and devices requires a cybersecurity approach that can cover the diverse technology environment.
From a monitoring and visibility perspective, siloed IT, OT or cloud security solutions cannot provide the comprehensive asset management, threat detection, risk management, and response and remediation capabilities across interconnected digital infrastructure that a solution natively covering IT, cloud, OT, and IoT can provide.
The problem with using siloed cybersecurity solutions to cover a distribution center is the visibility gaps that are inherently created when using multiple solutions to try and cover the totality of the diverse infrastructure. What this means is that for cross domain and multi-stage attacks, depending on the initial access point and where the adversary plans on actioning their objectives, multiple stages of the attack may not be detected or correlated if they security solutions lack visibility into OT, IT, IoT and cloud.
Comprehensive security under one solution
Darktrace leverages Self-Learning AI, which takes a new approach to cybersecurity. Instead of relying on rules and signatures, this AI trains on the specific business to learn a ‘pattern of life’ that models normal activity for every device, user, and connection. It can be applied anywhere an organization has data, and so can natively cover IT, OT, IoT, and cloud.
Visibility: Darktrace is the only OT security solution that natively covers IT, IoT and OT in unison. AI augmented workflows ensure OT cybersecurity analysts and operation engineers can manage IT and OT environments, leveraging a live asset inventory and tailored dashboards to optimize security workflows and minimize operator workload.
Threat detection, investigation, and response: The AI facilitates anomaly detection capable of detecting known, unknown, and insider threats and precise response for OT environments that contains threats at their earliest stages before they can jeopardize control systems. Darktrace immediately understands, identifies, and investigates all anomalous activity in OT networks, whether human or machine driven and uses Explainable AI to generate investigation reports via Darktrace’s Cyber AI Analyst.
Proactive risk identification: Risk management capabilities like attack path modeling can prioritize remediation and mitigation that will most effectively reduce derived risk scores. Rather than relying on knowledge of past attacks and CVE lists and scores, Darktrace AI learns what is ‘normal’ for its environment, discovering previously unknown threats and risks by detecting subtle shifts in behavior and connectivity. Through the application of Darktrace AI for OT environments, security teams can investigate novel attacks, discover blind spots, get live-time visibility across all their physical and digital assets, and reduce the time to detect, respond to, and triage security events.
Medusa Ransomware: Looking Cyber Threats in the Eye with Darktrace
10
Jun 2024
What is Living off the Land attack?
In the face of increasingly vigilant security teams and adept defense tools, attackers are continually looking for new ways to circumvent network security and gain access to their target environments. One common tactic is the leveraging of readily available utilities and services within a target organization’s environment in order to move through the kill chain; a popular method known as living off the land (LotL). Rather than having to leverage known malicious tools or write their own malware, attackers are able to easily exploit the existing infrastructure of their targets.
The Medusa ransomware group in particular are known to extensively employ LotL tactics, techniques and procedures (TTPs) in their attacks, as one Darktrace customer in the US discovered in early 2024.
What is Medusa Ransomware?
Medusa ransomware (not to be confused with MedusaLocker) was first observed in the wild towards the end of 2022 and has been a popular ransomware strain amongst threat actors since 2023 [1]. Medusa functions as a Ransomware-as-a-Service (RaaS) platform, providing would-be attackers, also know as affiliates, with malicious software and infrastructure required to carry out disruptive ransomware attacks. The ransomware is known to target organizations across many different industries and countries around the world, including healthcare, education, manufacturing and retail, with a particular focus on the US [2].
How does medusa ransomware work?
Medusa affiliates are known to employ a number of TTPs to propagate their malware, most prodominantly gaining initial access by exploiting vulnerable internet-facing assets and targeting valid local and domain accounts that are used for system administration.
The ransomware is typically delivered via phishing and spear phishing campaigns containing malicious attachments [3] [4], but it has also been observed using initial access brokers to access target networks [5]. In terms of the LotL strategies employed in Medusa compromises, affiliates are often observed leveraging legitimate services like the ConnectWise remote monitoring and management (RMM) software and PDQ Deploy, in order to evade the detection of security teams who may be unable to distinguish the activity from normal or expected network traffic [2].
According to researchers, Medusa has a public Telegram channel that is used by threat actors to post any data that may have been stolen, likely in an attempt to extort organizations and demand payment [2].
Darktrace’s Coverage of Medusa Ransomware
Established Foothold and C2 activity
In March 2024, Darktrace /NETWORK identified over 80 devices, including an internet facing domain controller, on a customer network performing an unusual number of activities that were indicative of an emerging ransomware attack. The suspicious behavior started when devices were observed making HTTP connections to the two unusual endpoints, “wizarr.manate[.]ch” and “go-sw6-02.adventos[.]de”, with the PowerShell and JWrapperDownloader user agents.
Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the connections and was able to connect the seemingly separate events into one wider incident spanning multiple different devices. This allowed the customer to visualize the activity in chronological order and gain a better understanding of the scope of the attack.
At this point, given the nature and rarity of the observed activity, Darktrace /NETWORK's autonomous response would have been expected to take autonomous action against affected devices, blocking them from making external connections to suspicious locations. However, autonomous response was not configured to take autonomous action at the time of the attack, meaning any mitigative actions had to be manually approved by the customer’s security team.
Internal Reconnaissance
Following these extensive HTTP connections, between March 1 and 7, Darktrace detected two devices making internal connection attempts to other devices, suggesting network scanning activity. Furthermore, Darktrace identified one of the devices making a connection with the URI “/nice ports, /Trinity.txt.bak”, indicating the use of the Nmap vulnerability scanning tool. While Nmap is primarily used legitimately by security teams to perform security audits and discover vulnerabilities that require addressing, it can also be leveraged by attackers who seek to exploit this information.
Figure 1: Darktrace /NETWORK model alert showing the URI “/nice ports, /Trinity.txt.bak”, indicating the use of Nmap.
Darktrace observed actors using multiple credentials, including “svc-ndscans”, which was also seen alongside DCE-RPC activity that took place on March 1. Affected devices were also observed making ExecQuery and ExecMethod requests for IWbemServices. ExecQuery is commonly utilized to execute WMI Query Language (WQL) queries that allow the retrieval of information from WI, including system information or hardware details, while ExecMethod can be used by attackers to gather detailed information about a targeted system and its running processes, as well as a tool for lateral movement.
Lateral Movement
A few hours after the first observed scanning activity on March 1, Darktrace identified a chain of administrative connections between multiple devices, including the aforementioned internet-facing server.
Cyber AI Analyst was able to connect these administrative connections and separate them into three distinct ‘hops’, i.e. the number of administrative connections made from device A to device B, including any devices leveraged in between. The AI Analyst investigation was also able to link the previously detailed scanning activity to these administrative connections, identifying that the same device was involved in both cases.
Figure 2: Cyber AI Analyst investigation into the chain of lateral movement activity.
On March 7, the internet exposed server was observed transferring suspicious files over SMB to multiple internal devices. This activity was identified as unusual by Darktrace compared to the device's normal SMB activity, with an unusual number of executable (.exe) and srvsvc files transferred targeting the ADMIN$ and IPC$ shares.
Figure 3: Cyber AI Analyst investigation into the suspicious SMB write activity.
Figure 4: Graph highlighting the number of successful SMB writes and the associated model alerts.
The threat actor was also seen writing SQLite3*.dll files over SMB using a another credential this time. These files likely contained the malicious payload that resulted in the customer’s files being encrypted with the extension “.s3db”.
Figure 5: Darktrace’s visibility over an affected device performing successful SMB writes.
Encryption of Files
Finally, Darktrace observed the malicious actor beginning to encrypt and delete files on the customer’s environment. More specifically, the actor was observed using credentials previously seen on the network to encrypt files with the aforementioned “.s3db” extension.
Figure 6: Darktrace’s visibility over the encrypted files.
After that, Darktrace observed the attacker encrypting files and appending them with the extension “.MEDUSA” while also dropping a ransom note with the file name “!!!Read_me_Medusa!!!.txt”
Figure 7: Darktrace’s detection of threat actors deleting files with the extension “.MEDUSA”.
Figure 8: Darktrace’s detection of the Medusa ransom note.
At the same time as these events, Darktrace observed the attacker utilizing a number of LotL techniques including SSL connections to “services.pdq[.]tools”, “teamviewer[.]com” and “anydesk[.]com”. While the use of these legitimate services may have bypassed traditional security tools, Darktrace’s anomaly-based approach enabled it to detect the activity and distinguish it from ‘normal’’ network activity. It is highly likely that these SSL connections represented the attacker attempting to exfiltrate sensitive data from the customer’s network, with a view to using it to extort the customer.
Figure 9: Cyber AI Analyst’s detection of “services.pdq[.]tools” usage.
If this customer had been subscribed to Darktrace's Proactive Threat Notification (PTN) service at the time of the attack, they would have been promptly notified of these suspicious activities by the Darktrace Security Operation Center (SOC). In this way they could have been aware of the suspicious activities taking place in their infrastructure before the escalation of the compromise. Despite this, they were able to receive assistance through the Ask the Expert service (ATE) whereby Darktrace’s expert analyst team was on hand to assist the customer by triaging and investigating the incident further, ensuring the customer was well equipped to remediate.
As Darktrace /NETWORK's autonomous response was not enabled in autonomous response mode, this ransomware attack was able to progress to the point of encryption and data exfiltration. Had autonomous response been properly configured to take autonomous action, Darktrace would have blocked all connections by affected devices to both internal and external endpoints, as well as enforcing a previously established “pattern of life” on the device to stop it from deviating from its expected behavior.
Conclusion
The threat actors in this Medusa ransomware attack attempted to utilize LotL techniques in order to bypass human security teams and traditional security tools. By exploiting trusted systems and tools, like Nmap and PDQ Deploy, attackers are able to carry out malicious activity under the guise of legitimate network traffic.
Darktrace’s Self-Learning AI, however, allows it to recognize the subtle deviations in a device’s behavior that tend to be indicative of compromise, regardless of whether it appears legitimate or benign on the surface.
Further to the detection of the individual events that made up this ransomware attack, Darktrace’s Cyber AI Analyst was able to correlate the activity and collate it under one wider incident. This allowed the customer to track the compromise and its attack phases from start to finish, ensuring they could obtain a holistic view of their digital environment and remediate effectively.
Credit to Maria Geronikolou, Cyber Analyst, Ryan Traill, Threat Content Lead
Appendices
Darktrace DETECT Model Detections
Anomalous Connection / SMB Enumeration
Device / Anomalous SMB Followed By Multiple Model Alerts
Device / Suspicious SMB Scanning Activity
Device / Attack and Recon Tools
Device / Suspicious File Writes to Multiple Hidden SMB Share
Compromise / Ransomware / Ransom or Offensive Words Written to SMB
Device / Internet Facing Device with High Priority Alert
Device / Network Scan
Anomalous Connection / Powershell to Rare External
Device / New PowerShell User Agent
Possible HTTP Command and Control
Extensive Suspicious DCE-RPC Activity
Possible SSL Command and Control to Multiple Endpoints
Suspicious Remote WMI Activity
Scanning of Multiple Devices
Possible Ransom Note Accessed over SMB
List of Indicators of Compromise (IoCs)
IoC – Type – Description + Confidence
207.188.6[.]17 - IP address - C2 Endpoint
172.64.154[.]227 - IP address - C2 Endpoint
wizarr.manate[.]ch - Hostname - C2 Endpoint
go-sw6-02.adventos[.]de. Hostname - C2 Endpoint
.MEDUSA - File extension - Extension to encrypted files
.s3db - File extension - Created file extension
SQLite3-64.dll - File - Used tool
!!!Read_me_Medusa!!!.txt - File - Ransom note
Svc-ndscans - Credential - Possible compromised credential
Svc-NinjaRMM - Credential - Possible compromised credential
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