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May 18, 2021

The Dangers of Double Extortion Ransomware Attacks

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18
May 2021
Learn about the latest trend in ransomware attacks known as double extortion. Discover how Darktrace can help protect your organization from this threat.

A year and a half ago, ‘double extortion’ ransomware was being used by only one known threat actor. Now, over 16 ransomware groups actively utilize this tactic. So, what is it, and why has it become so popular?

What is double extortion ransomware?

The traditional story of ransomware was one of malicious code rapidly encrypting files with public-key RSA encryption, and then deleting those files if the victim did not pay the ransom.

However, after the infamous WannaCry and NotPetya ransomware campaigns over 2017, companies ramped up their cyber defense. More emphasis was placed on backups and restoration processes, so that even if files were destroyed, organizations had copies in place and could easily restore their data.

Yet in turn, cyber-criminals have also adapted their techniques. Now, rather than just encrypting files, double extortion ransomware exfiltrates the data first. This means that if the company refuses to pay up, information can be leaked online or sold to the highest bidder. Suddenly, all those backups and data recovery plans became worthless.

Maze ransomware and friends

In late 2019, Maze ransomware emerged as the first high-profile case of double extortion. Other strains soon followed, with the Sodinokibi attack — which crippled foreign exchange company Travelex — occurring on the final day of that year.

By mid-2020, hundreds of organizations were falling victim to double extortion attacks, various websites on the dark net were leaking company data, and the Ransomware-as-a-Service business was booming as developers sold and rented new types of malware.

Furthermore, cyber security regulations started being weaponized by cyber-criminals who could leverage the threat of having to pay a hefty compliance fine (CCPA, GDPR, NYSDFS regulations) to encourage their victims to keep quiet by offering them a ransom smaller than the penalty fee.

There were 1,200 double extortion ransomware incidents in 2020, across 63 countries, with over 60% of these aimed at the US and the UK.

Despite new legislation being written regularly to try and mitigate these attacks, they aren’t slowing down. According to a recent study by RUSI, there were 1,200 double extortion ransomware incidents in 2020 alone, across 63 different countries. 60% of these were aimed at organizations headquartered in the US, and the UK suffered the second highest number of breaches.

Last month, the cyber-criminal gang known as REvil released details about Apple’s new Macbook Pro on their site ‘Happy Blog’, threatening to release more blueprints and demanding a ransom of $50 million. And last week, Colonial Pipeline purportedly paid $5 million in bitcoin to recover from a devastating OT ransomware attack.

Anatomy of a double extortion ransomware attack

Darktrace has detected a huge upsurge in double extortion ransomware threats in the last year, most recently at an energy company based in Canada. The hackers had clearly done their homework, tailoring the attack to the company and moving quickly and stealthily once inside. Below is a timeline of this real-world incident, which was mostly carried out in the space of 24 hours.

Figure 1: A timeline of the attack

Darktrace detected every stage of the intrusion and notified the security team with high-priority alerts. If Darktrace Antigena had been active in the environment, the compromised server would have been isolated as soon as it began to behave anomalously, preventing the infection from spreading.

Encryption and exfiltration

The initial infection vector is not known, but the admin account was compromised most likely from a phishing link or a vulnerability exploit. This is indicative of a trend away from the widespread ‘spray and pray’ ransomware campaigns of the last decade, towards a more targeted approach.

Cyber AI identified an internal server engaging in unusual network scanning and attempted lateral movement using the Remote Desktop Protocol (RDP). Compromised admin credentials were used to spread rapidly from the server to another internal device, ‘serverps’.

The device ‘serverps’ initiated an outbound connection to TeamViewer, a legitimate file storage service, which was active for nearly 21 hours. This connection was used for remote control of the device and to facilitate the further stages of attack. Although TeamViewer was not in wide operation in the company’s digital environment, it was not blocked by any of the legacy defenses.

The device then connected to an internal file server and downloaded 1.95 TB of data, and uploaded the same volume of data to pcloud[.]com. This exfiltration took place during work hours to blend in with regular admin activity.

The device was also seen downloading Rclone software – an open source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud.

The compromised admin credential allowed the threat actor to move laterally during this time. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000.

As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.

AI-powered investigation

Cyber AI Analyst reported on four incidents related to the attack, highlighting the suspicious behavior to the security team and providing a report on the affected devices for immediate remediation. Such concise reporting allowed the security team to quickly identify the scope of the infection and respond accordingly.

Figure 2: Cyber AI Analyst incident tray for a week

Cyber AI Analyst investigates on demand

Following further analysis on March 13, the security team employed Cyber AI Analyst to conduct on-demand investigations into the compromised admin credential in Microsoft 365, as well as another device which was identified as a potential threat.

Cyber AI Analyst created an incident for this other device, which resulted in the identification of unusual port scanning during the time period of infection. The device was promptly removed from the network.

Figure 3: Cyber AI Analyst incident for a compromised device, detailing an unusual internal download

Double trouble

The use of legitimate tools and ‘Living off the Land’ techniques (using RDP and a compromised admin credential) allowed the threat actors to carry out the bulk of the attack in less than 24 hours. By exploiting TeamViewer as a legitimate file storage solution for the data exfiltration, as opposed to relying on a known ‘bad’ or recently registered domain, the hackers easily circumvented all the existing signature-based defenses.

If Darktrace had not detected this intrusion and immediately alerted the security team, the attack could have resulted not only in a ‘denial of business’ with employees locked out of their files, but also in sensitive data loss. The AI went a step further in saving the team vital time with automatic investigation and on-demand reporting.

There is so much more to lose from double extortion ransomware. Exfiltration provides another layer of risk, leading to compromised intellectual property, reputational damage, and compliance fines. Once a threat group has your data, they might easily ask for more payments down the line. It is important therefore to defend against these attacks before they happen, proactively implementing cyber security measures that can detect and autonomously respond to threats as soon as they emerge.

Learn more about double extortion ransomware.

Darktrace model detections:

  • Device / Suspicious Network Scan Activity
  • Device / RDP Scan
  • Device / Network Scan
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous Connection / Unusual Admin RDP Session
  • Device / Multiple Lateral Movement Model Breaches
  • User / New Admin Credentials on Client
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration
  • Device / Anomalous SMB Followed By Multiple Model
  • Anomalous Connection / Download and Upload
  • Anomalous Connection / Suspicious Activity On High Risk Device
  • Anomalous File / Internal::Additional Extension Appended to SMB File
  • Compromise / Ransomware::Suspicious SMB Activity
  • Anomalous Connection / Sustained MIME Type Conversion
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / Large Number of Model Breaches
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.
Author
Brianna Leddy
Director of Analysis

Based in San Francisco, Brianna is Director of Analysis at Darktrace. She joined the analyst team in 2016 and has since advised a wide range of enterprise customers on advanced threat hunting and leveraging Self-Learning AI for detection and response. Brianna works closely with the Darktrace SOC team to proactively alert customers to emerging threats and investigate unusual behavior in enterprise environments. Brianna holds a Bachelor’s degree in Chemical Engineering from Carnegie Mellon University.

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October 4, 2024

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Inside the SOC

From Call to Compromise: Darktrace’s Response to a Vishing-Induced Network Attack

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What is vishing?

Vishing, or voice phishing, is a type of cyber-attack that utilizes telephone devices to deceive targets. Threat actors typically use social engineering tactics to convince targets that they can be trusted, for example, by masquerading as a family member, their bank, or trusted a government entity. One method frequently used by vishing actors is to intimidate their targets, convincing them that they may face monetary fines or jail time if they do not provide sensitive information.

What makes vishing attacks dangerous to organizations?

Vishing attacks utilize social engineering tactics that exploit human psychology and emotion. Threat actors often impersonate trusted entities and can make it appear as though a call is coming from a reputable or known source.  These actors often target organizations, specifically their employees, and pressure them to obtain sensitive corporate data, such as privileged credentials, by creating a sense of urgency, intimidation or fear. Corporate credentials can then be used to gain unauthorized access to an organization’s network, often bypassing traditional security measures and human security teams.

Darktrace’s coverage of vishing attack

On August 12, 2024, Darktrace / NETWORK identified malicious activity on the network of a customer in the hospitality sector. The customer later confirmed that a threat actor had gained unauthorized access through a vishing attack. The attacker successfully spoofed the IT support phone number and called a remote employee, eventually leading to the compromise.

Figure 1: Timeline of events in the kill chain of this attack.

Establishing a Foothold

During the call, the remote employee was requested to authenticate via multi-factor authentication (MFA). Believing the caller to be a member of their internal IT support, using the legitimate caller ID, the remote user followed the instructions and confirmed the MFA prompt, providing access to the customer’s network.

This authentication allowed the threat actor to login into the customer’s environment by proxying through their Virtual Private Network (VPN) and gain a foothold in the network. As remote users are assigned the same static IP address when connecting to the corporate environment, the malicious actor appeared on the network using the correct username and IP address. While this stealthy activity might have evaded traditional security tools and human security teams, Darktrace’s anomaly-based threat detection identified an unusual login from a different hostname by analyzing NTLM requests from the static IP address, which it determined to be anomalous.

Observed Activity

  • On 2024-08-12 the static IP was observed using a credential belonging to the remote user to initiate an SMB session with an internal domain controller, where the authentication method NTLM was used
  • A different hostname from the usual hostname associated with this remote user was identified in the NTLM authentication request sent from a device with the static IP address to the domain controller
  • This device does not appear to have been seen on the network prior to this event.

Darktrace, therefore, recognized that this login was likely made by a malicious actor.

Internal Reconnaissance

Darktrace subsequently observed the malicious actor performing a series of reconnaissance activities, including LDAP reconnaissance, device hostname reconnaissance, and port scanning:

  • The affected device made a 53-second-long LDAP connection to another internal domain controller. During this connection, the device obtained data about internal Active Directory (AD) accounts, including the AD account of the remote user
  • The device made HTTP GET requests (e.g., HTTP GET requests with the Target URI ‘/nice ports,/Trinity.txt.bak’), indicative of Nmap usage
  • The device started making reverse DNS lookups for internal IP addresses.
Figure 2: Model alert showing the IP address from which the malicious actor connected and performed network scanning activities via port 9401.
Figure 3: Model Alert Event Log showing the affected device connecting to multiple internal locations via port 9401.

Lateral Movement

The threat actor was also seen making numerous failed NTLM authentication requests using a generic default Windows credential, indicating an attempt to brute force and laterally move through the network. During this activity, Darktrace identified that the device was using a different hostname than the one typically used by the remote employee.

Cyber AI Analyst

In addition to the detection by Darktrace / NETWORK, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing activity. The investigation was able to correlate the seemingly separate events together into a broader incident, continuously adding new suspicious linked activities as they occurred.

Figure 4: Cyber AI Analyst investigation showing the activity timeline, and the activities associated with the incident.

Upon completing the investigation, Cyber AI Analyst provided the customer with a comprehensive summary of the various attack phases detected by Darktrace and the associated incidents. This clear presentation enabled the customer to gain full visibility into the compromise and understand the activities that constituted the attack.

Figure 5: Cyber AI Analyst displaying the observed attack phases and associated model alerts.

Darktrace Autonomous Response

Despite the sophisticated techniques and social engineering tactics used by the attacker to bypass the customer’s human security team and existing security stack, Darktrace’s AI-driven approach prevented the malicious actor from continuing their activities and causing more harm.

Darktrace’s Autonomous Response technology is able to enforce a pattern of life based on what is ‘normal’ and learned for the environment. If activity is detected that represents a deviation from expected activity from, a model alert is triggered. When Darktrace’s Autonomous Response functionality is configured in autonomous response mode, as was the case with the customer, it swiftly applies response actions to devices and users without the need for a system administrator or security analyst to perform any actions.

In this instance, Darktrace applied a number of mitigative actions on the remote user, containing most of the activity as soon as it was detected:

  • Block all outgoing traffic
  • Enforce pattern of life
  • Block all connections to port 445 (SMB)
  • Block all connections to port 9401
Figure 6: Darktrace’s Autonomous Response actions showing the actions taken in response to the observed activity, including blocking all outgoing traffic or enforcing the pattern of life.

Conclusion

This vishing attack underscores the significant risks remote employees face and the critical need for companies to address vishing threats to prevent network compromises. The remote employee in this instance was deceived by a malicious actor who spoofed the phone number of internal IT Support and convinced the employee to perform approve an MFA request. This sophisticated social engineering tactic allowed the attacker to proxy through the customer’s VPN, making the malicious activity appear legitimate due to the use of static IP addresses.

Despite the stealthy attempts to perform malicious activities on the network, Darktrace’s focus on anomaly detection enabled it to swiftly identify and analyze the suspicious behavior. This led to the prompt determination of the activity as malicious and the subsequent blocking of the malicious actor to prevent further escalation.

While the exact motivation of the threat actor in this case remains unclear, the 2023 cyber-attack on MGM Resorts serves as a stark illustration of the potential consequences of such threats. MGM Resorts experienced significant disruptions and data breaches following a similar vishing attack, resulting in financial and reputational damage [1]. If the attack on the customer had not been detected, they too could have faced sensitive data loss and major business disruptions. This incident underscores the critical importance of robust security measures and vigilant monitoring to protect against sophisticated cyber threats.

Credit to Rajendra Rushanth (Cyber Security Analyst) and Ryan Traill (Threat Content Lead)

Appendices

Darktrace Model Detections

  • Device / Unusual LDAP Bind and Search Activity
  • Device / Attack and Recon Tools
  • Device / Network Range Scan
  • Device / Suspicious SMB Scanning Activity
  • Device / RDP Scan
  • Device / UDP Enumeration
  • Device / Large Number of Model Breaches
  • Device / Network Scan
  • Device / Multiple Lateral Movement Model Breaches (Enhanced Monitoring)
  • Device / Reverse DNS Sweep
  • Device / SMB Session Brute Force (Non-Admin)

List of Indicators of Compromise (IoCs)

IoC - Type – Description

/nice ports,/Trinity.txt.bak - URI – Unusual Nmap Usage

MITRE ATT&CK Mapping

Tactic – ID – Technique

INITIAL ACCESS – T1200 – Hardware Additions

DISCOVERY – T1046 – Network Service Scanning

DISCOVERY – T1482 – Domain Trust Discovery

RECONNAISSANCE – T1590 – IP Addresses

T1590.002 – DNS

T1590.005 – IP Addresses

RECONNAISSANCE – T1592 – Client Configurations

T1592.004 – Client Configurations

RECONNAISSANCE – T1595 – Scanning IP Blocks

T1595.001 – Scanning IP Blocks

T1595.002 – Vulnerability Scanning

References

[1] https://www.bleepingcomputer.com/news/security/securing-helpdesks-from-hackers-what-we-can-learn-from-the-mgm-breach/

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About the author
Rajendra Rushanth
Cyber Analyst

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October 3, 2024

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Cloud

Introducing real-time multi-cloud detection & response powered by AI

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We are delighted to announce the general availability of Microsoft Azure support for Darktrace / CLOUD, enabling real-time cloud detection and response across dynamic multi-cloud environments. Built on Self-Learning AI, Darktrace / CLOUD leverages Microsoft’s new virtual network flow logs (VNet flow) to offer an agentless-first approach that dramatically simplifies detection and response within Azure, unifying cloud-native security with Darktrace’s innovative ActiveAI Security Platform.

As organizations increasingly adopt multi-cloud architectures, the need for advanced, real-time threat detection and response is critical to keep pace with evolving cloud threats. Security teams face significant challenges, including increased complexity, limited visibility, and siloed tools. The dynamic nature of multi-cloud environments introduces ever-changing blind spots, while traditional security tools struggle to provide real-time insights, often offering static snapshots of risk. Additionally, cloud security teams frequently operate in isolation from SOC teams, leading to fragmented visibility and delayed responses. This lack of coordination, especially in hybrid environments, hinders effective threat detection and response. Compounding these challenges, current security solutions are split between agent-based and agentless approaches, with agentless solutions often lacking real-time awareness and agent-based options adding complexity and scalability concerns. Darktrace / CLOUD helps to solve these challenges with real-time detection and response designed specifically for dynamic cloud environments like Azure and AWS.

Pioneering AI-led real-time cloud detection & response

Darktrace has been at the forefront of real-time detection and response for over a decade, continually pushing the boundaries of AI-driven cybersecurity. Our Self-Learning AI uniquely positions Darktrace with the ability to automatically understand and instantly adapt to changing cloud environments. This is critical in today’s landscape, where cloud infrastructures are highly dynamic and ever-changing.  

Built on years of market-leading network visibility, Darktrace / CLOUD understands ‘normal’ for your unique business across clouds and networks to instantly reveal known, unknown, and novel cloud threats with confidence. Darktrace Self-Learning AI continuously monitors activity across cloud assets, containers, and users, and correlates it with detailed identity and network context to rapidly detect malicious activity. Platform-native identity and network monitoring capabilities allow Darktrace / CLOUD to deeply understand normal patterns of life for every user and device, enabling instant, precise and proportionate response to abnormal behavior - without business disruption.

Leveraging platform-native Autonomous Response, AI-driven behavioral containment neutralizes malicious activity with surgical accuracy while preventing disruption to cloud infrastructure or services. As malicious behavior escalates, Darktrace correlates thousands of data points to identify and instantly respond to unusual activity by blocking specific connections and enforcing normal behavior.

Figure 1: AI-driven behavioral containment neutralizes malicious activity with surgical accuracy while preventing disruption to cloud infrastructure or services.

Unparalleled agentless visibility into Azure

As a long-term trusted partner of Microsoft, Darktrace leverages Azure VNet flow logs to provide agentless, high-fidelity visibility into cloud environments, ensuring comprehensive monitoring without disrupting workflows. By integrating seamlessly with Azure, Darktrace / CLOUD continues to push the envelope of innovation in cloud security. Our Self-learning AI not only improves the detection of traditional and novel threats, but also enhances real-time response capabilities and demonstrates our commitment to delivering cutting-edge, AI-powered multi-cloud security solutions.

  • Integration with Microsoft Virtual network flow logs for enhanced visibility
    Darktrace / CLOUD integrates seamlessly with Azure to provide agentless, high-fidelity visibility into cloud environments. VNet flow logs capture critical network traffic data, allowing Darktrace to monitor Azure workloads in real time without disrupting existing workflows. This integration significantly reduces deployment time by 95%1 and cloud security operational costs by up to 80%2 compared to traditional agent-based solutions. Organizations benefit from enhanced visibility across dynamic cloud infrastructures, scaling security measures effortlessly while minimizing blind spots, particularly in ephemeral resources or serverless functions.
  • High-fidelity agentless deployment
    Agentless deployment allows security teams to monitor and secure cloud environments without installing software agents on individual workloads. By using cloud-native APIs like AWS VPC flow logs or Azure VNet flow logs, security teams can quickly deploy and scale security measures across dynamic, multi-cloud environments without the complexity and performance overhead of agents. This approach delivers real-time insights, improving incident detection and response while reducing disruptions. For organizations, agentless visibility simplifies cloud security management, lowers operational costs, and minimizes blind spots, especially in ephemeral resources or serverless functions.
  • Real-time visibility into cloud assets and architectures
    With real-time Cloud Asset Enumeration and Dynamic Architecture Modeling, Darktrace / CLOUD generates up-to-date architecture diagrams, giving SecOps and DevOps teams a unified view of cloud infrastructures. This shared context enhances collaboration and accelerates threat detection and response, especially in complex environments like Kubernetes. Additionally, Cyber AI Analyst automates the investigation process, correlating data across networks, identities, and cloud assets to save security teams valuable time, ensuring continuous protection and efficient cloud migrations.
Figure 2: Real-time visibility into Azure assets and architectures built from network, configuration and identity and access roles.

Unified multi-cloud security at scale

As organizations increasingly adopt multi-cloud strategies, the complexity of managing security across different cloud providers introduces gaps in visibility. Darktrace / CLOUD simplifies this by offering agentless, real-time monitoring across multi-cloud environments. Building on our innovative approach to securing AWS environments, our customers can now take full advantage of robust real-time detection and response capabilities for Azure. Darktrace is one of the first vendors to leverage Microsoft’s virtual network flow logs to provide agentless deployment in Azure, enabling unparalleled visibility without the need for installing agents. In addition, Darktrace / CLOUD offers automated Cloud Security Posture Management (CSPM) that continuously assesses cloud configurations against industry standards.  Security teams can identify and prioritize misconfigurations, vulnerabilities, and policy violations in real-time. These capabilities give security teams a complete, live understanding of their cloud environments and help them focus their limited time and resources where they are needed most.

This approach offers seamless integration into existing workflows, reducing configuration efforts and enabling fast, flexible deployment across cloud environments. By extending its capabilities across multiple clouds, Darktrace / CLOUD ensures that no blind spots are left uncovered, providing holistic, multi-cloud security that scales effortlessly with your cloud infrastructure. diagrams, visualizes cloud assets, and prioritizes risks across cloud environments.

Figure 3: Unified view of AWS and Azure cloud posture and compliance over time.

The future of cloud security: Real-time defense in an unpredictable world

Darktrace / CLOUD’s support for Microsoft Azure, powered by Self-Learning AI and agentless deployment, sets a new standard in multi-cloud security. With real-time detection and autonomous response, organizations can confidently secure their Azure environments, leveraging innovation to stay ahead of the constantly evolving threat landscape. By combining Azure VNet flow logs with Darktrace’s AI-driven platform, we can provide customers with a unified, intelligent solution that transforms how security is managed across the cloud.

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References

1. Based on internal research and customer data

2. Based on internal research

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About the author
Adam Stevens
Director of Product, Cloud Security
Your data. Our AI.
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