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December 21, 2020

How AI Stopped a WastedLocker Ransomware Intrusion & Fast

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21
Dec 2020
Stop WastedLocker ransomware in its tracks with Darktrace AI technology. Learn about how AI detected a recent attack using 'Living off the Land' techniques.

Since first being discovered in May 2020, WastedLocker has made quite a name for itself, quickly becoming an issue for businesses and cyber security firms around the world. WastedLocker is known for its sophisticated methods of obfuscation and steep ransom demands.

Its use of ‘Living off the Land’ techniques makes a WastedLocker attack extremely difficult for legacy security tools to detect. An ever-decreasing dwell time – the time between initial intrusion and final execution – means human responders alone struggle to contain the ransomware variant before damage is done.

This blog examines the anatomy of a WastedLocker intrusion that targeted a US agricultural organization in December. Darktrace’s AI detected and investigated the incident in real time, and we can see how Darktrace RESPOND would have autonomously taken action to stop the attack before encryption had begun.

As ransomware dwell time shrinks to hours rather than days, security teams are increasingly relying on artificial intelligence to stop threats from escalating at the earliest signs of compromise – containing attacks even when they strike at night or on the weekend.

How the WastedLocker attack unfolded

Figure 1: A timeline of the attack

Initial intrusion

The initial infection appears to have taken place when an employee was deceived into downloading a fake browser update. Darktrace AI was monitoring the behavior of around 5,000 devices at the organization, continuously adapting its understanding of the evolving ‘pattern of life’. It detected the first signs of a threat when a virtual desktop device started making HTTP and HTTPS connections to external destinations that were deemed unusual for the organization. The graph below depicts how the patient zero device exhibited a spike in internal connections around December 4.

Figure 2: The patient zero device exhibiting a spike in internal connections, with orange dots indicating model breaches of varying severity

Reconnaissance

Attempted reconnaissance began just 11 minutes after the initial intrusion. Again, Darktrace immediately picked up on the activity, detecting unusual ICMP ping scans and targeted address scans on ports 135, 139 and 445; presumably as the attacker looked for potential further Windows targets. The below demonstrates the scanning detections based on the unusual number of new failed connections.

Figure 3: Darktrace detecting an unusual number of failed connections

Lateral movement

The attacker used an existing administrative credential to authenticate against a Domain Controller, initiating new service control over SMB. Darktrace picked this up immediately, identifying it as unusual behavior.

Figure 4: Darktrace identifying the DCE-RPC requests
Figure 5: Darktrace surfacing the SMB writes

Several hours later – and in the early hours of the morning – the attacker used a temporary admin account ‘tempadmin’ to move to another Domain Controller over SMB. Darktrace instantly detected this as it was highly unusual to use a temporary admin account to connect from a virtual desktop to a Domain Controller.

Figure 6: Further anomalous connections detected the following day

Lock and load: WastedLocker prepares to strike

During the beaconing activity, the attacker also conducted internal reconnaissance and managed to establish successful administrative and remote connections to other internal devices by using tools already present. Soon after, a transfer of suspicious .csproj files was detected by Darktrace, and at least four other devices began exhibiting similar command and control (C2) communications.

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However, with Darktrace’s real-time detections – and Cyber AI Analyst investigating and reporting on the incident in a number of minutes, the security team were able to contain the attack, taking the infected devices offline.

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Automated investigations with Cyber AI Analyst

Darktrace’s Cyber AI Analyst launched an automatic investigation around every anomaly detection, forming hypotheses, asking questions about its own findings, and forming accurate answers at machine speed. It then generated high-level, intuitive incident summaries for the security team. Over the 48 hour period, the AI Analyst surfaced just six security incidents in total, with three of these directly relating to the WastedLocker intrusion.

Figure 7: The Cyber AI Analyst threat tray

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The snapshot below shows a VMWare device (patient zero) making repeated external connections to rare destinations, scanning the network and using new admin credentials.

Figure 8: Cyber AI Analyst investigates

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Darktrace RESPOND: AI that responds when the security team cannot

Darktrace RESPOND – the world’s first and only Autonomous Response technology – was configured in passive mode, meaning it did not actively interfere with the attack, but if we dive back into the Threat Visualizer we can see that Antigena in fully autonomous mode would have responded to the attack at this early stage, buying the security team valuable time.

In this case, after the initial unusual SSL C2 detection (based on a combination of destination rarity, JA3 unusualness and frequency analysis), RESPOND (formerly known as 'Antigena', as shown in the screenshots below) suggested instantly blocking the C2 traffic on port 443 and parallel internal scanning on port 135.

Figure 9: The Threat Visualizer reveals the action Antigena would have taken

When beaconing was later observed to bywce.payment.refinedwebs[.]com, this time over HTTP to /updateSoftwareVersion, RESPOND escalated its response by blocking the further C2 channels.

Figure 10: Antigena escalates its response

The vast majority of response tools rely on hard-coded, pre-defined rules, formulated as ‘If X, do Y’. This can lead to false positives that unnecessarily take devices offline and hamper productivity. Darktrace RESPOND's actions are proportionate, bespoke to the organization, and not created in advance. Darktrace Antigena autonomously chose what to block and the severity of the blocks based on the context of the intrusion, without a human pre-eminently hard-coding any commands or set responses.

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Every response over the 48 hours was related to the incident – RESPOND did not try to take action on anything else during the intrusion period. It simply would have actioned a surgical response to contain the threat, while allowing the rest of the business to carry on as usual. There were a total of 59 actions throughout the incident time period – excluding the ‘Watched Domain Block’ actions shown below – which are used during incident response to proactively shut down C2 communication.

Figure 11: All Antigena action attempts during the intrusion period across the whole organization

RESPOND would have delivered those blocks via whatever integration is most suitable for the organization – whether that be Firewall integrations, NACL integrations or other native integrations. The technology would have blocked the malicious activity on the relevant ports and protocols for several hours – surgically interrupting the threat actors’ intrusion activity, thus preventing further escalation and giving the security team air cover.

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Stopping WastedLocker ransomware before encryption ensues

This attack used many notable Tools, Techniques and Procedures (TTPs) to bypass signature-based tools. It took advantage of ‘Living off the Land’ techniques, including Windows Management Instrumentation (WMI), Powershell, and default admin credential use. Only one of the involved C2 domains had a single hit on Open Source Intelligence Lists (OSINT); the others were unknown at the time. The C2 was also encrypted with legitimate Thawte SSL Certificates.

For these reasons, it is plausible that without Darktrace in place, the ransomware would have been successful in encrypting files, preventing business operations at a critical time and possibly inflicting huge financial and reputational losses to the organization in question.

Darktrace’s AI detects and stops ransomware in its tracks without relying on threat intelligence. Ransomware has thrived this year, with attackers constantly coming up with new attack TTPs. However, the above threat find demonstrates that even targeted, sophisticated strains of ransomware can be stopped with AI technology.

Thanks to Darktrace analyst Signe Zaharka for her insights on the above threat find.

Learn more about Autonomous Response

Darktrace model detections:

  • Compliance / High Priority Compliance Model Breach
  • Compliance / Weak Active Directory Ticket Encryption
  • Anomalous Connection / Cisco Umbrella Block Page
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compliance / Default Credential Usage
  • Compromise / Suspicious TLS Beaconing To Rare External
  • Anomalous Server Activity / Rare External from Server
  • Device / Lateral Movement and C2 Activity
  • Compromise / SSL Beaconing to Rare Destination
  • Device / New or Uncommon WMI Activity
  • Compromise / Watched Domain
  • Antigena / Network / External Threat / Antigena Watched Domain Block
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Slow Beaconing Activity To External Rare
  • Device / Multiple Lateral Movement Model Breaches
  • Compromise / High Volume of Connections with Beacon Score
  • Device / Large Number of Model Breaches
  • Compromise / Beaconing Activity To External Rare
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Anomalous Connection / New or Uncommon Service Control
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Compromise / SSL or HTTP Beacon
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block
  • Compromise / Sustained SSL or HTTP Increase
  • Unusual Activity / Unusual Internal Connections
  • Device / ICMP Address Scan


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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
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.

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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.

Learn More:

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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