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May 5, 2020

Old But Still Dangerous: Dharma Ransomware Attack

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05
May 2020
See how Darktrace's Cyber AI detected a fast-acting Dharma ransomware attack at every stage of the life cycle to prevent further damage to the system.

Executive summary

  • In the past few weeks, Darktrace has observed an increase in attacks against internet-facing systems, such as RDP. The initial intrusions usually take place via existing vulnerabilities or stolen, legitimate credentials. The Dharma ransomware attack described in this blog post is one such example.
  • Old threats can be damaging – Dharma and its variants have been around for four years. This is a classic example of ‘legacy’ ransomware morphing and adapting to bypass traditional defenses.
  • The intrusion shows signs that indicate the threat-actors are aware of – and are actively exploiting – the COVID-19 situation.
  • In the current threat landscape surrounding COVID-19, Darktrace recommends monitoring internet-facing systems and critical servers closely – keeping track of administrative credentials and carefully considering security when rapidly deploying internet-facing infrastructure.

Introduction

In mid-April, Darktrace detected a targeted Dharma ransomware attack on a UK company. The initial point of intrusion was via RDP – this represents a very common attack method of infection that Darktrace has observed in the broader threat landscape over the past few weeks.

This blog post highlights every stage of the attack lifecycle and details the attacker’s techniques, tools and procedures (TTP) – all detected by Darktrace.

Dharma – a varient of the CrySIS malware family – first appeared in 2016 and uses multiple intrusion vectors. It distributes its malware as an attachment in a spam email, by disguising it as an installation file for legitimate software, or by exploiting an open RDP connection through internet-facing servers. When Dharma has finished encrypting files, it drops a ransom note with the contact email address in the encrypted SMB files.

Darktrace had strong, real-time detections of the attack – however the absence of eyes on the user interface prior to the encryption activity, and without Autonomous Response deployed in Active Mode, these alerts were only actioned after the ransomware was unleashed. Fortunately, it was unable to spread within the organization, thanks to human intervention at the peak of the attack. However, Darktrace Antigena in active mode would have significantly slowed down the attack.

Timeline

The timeline below provides a rough overview of the major attack phases over five days of activity.

Figure 1: A timeline of the attack

Technical analysis

Darktrace detected that the main device hit by the attack was an internet-facing RDP server (‘RDP server’). Dharma used network-level encryption here: the ransomware activity takes place over the network protocol SMB.

Below is a chronological overview of all Darktrace detections that fired during this attack: Darktrace detected and reported every single unusual or suspicious event occurring on the RDP server.

Figure 2: An overview of Darktrace detections

Initial compromise

On April 7, the RDP server began receiving a large number of incoming connections from rare IP addresses on the internet.

On April 7, the RDP server began receiving a large number of incoming connections from rare IP addresses on the internet. This means a lot of IP addresses on the internet that usually don’t connect to this company started connection attempts over RDP. The top five cookies used to authenticate show that the source IPs were located in Russia, the Netherlands, Korea, the United States, and Germany.

It is highly likely that the RDP credential used in this attack had been compromised prior to the attack – either via common brute-force methods, credential stuffing attacks, or phishing. Indeed, a TTP growing in popularity is to buy RDP credentials on marketplaces and skip to initial access.

Attempted privilege escalation

The following day, the malicious actor abused the SMB version 1 protocol, notorious for always-on null sessions which offer unauthenticated users’ information about the machine – such as password policies, usernames, group names, machine names, user and host SIDs. What followed was very unusual: the server connected externally to a rare IP address located in Morocco.

Next, the attacker attempted a failed SMB session to the external IP over an unusual port. Darktrace detected this activity as highly anomalous, as it had previously learned that SMB is usually not used in this fashion within this organization – and certainly not for external communication over this port.

Figure 3: Darktrace detecting the rare external IP address

Figure 4: The SMB session failure and the rare connection over port 1047

Command and control traffic

As the entire attack occurred over five days, this aligns with a smash-and-grab approach, rather than a highly covert, low-and-slow operation.

Two hours later, the server initiated a large number of anomalous and rare connections to external destinations located in India, China, and Italy – amongst other destinations the server had never communicated with before. The attacker was now attempting to establish persistence and create stronger channels for command and control (C2). As the entire attack occurred over five days, this aligns with a smash-and-grab approach, rather than a highly covert, low-and-slow operation.

Actions on target

Notwithstanding this approach, the malicious actor remained dormant for two days, biding their time until April 10 — a public holiday in the UK — when security teams would be notably less responsive. This pause in activity provides supporting evidence that the attack was human-driven.

Figure 5: The unusual RDP connections detected by Darktrace

The RDP server then began receiving incoming remote desktop connections from 100% rare IP addresses located in the Netherlands, Latvia, and Poland.

Internal reconnaissance

The IP address 85.93.20[.]6, hosted at the time of investigation in Panama, made two connections to the server, using an administrative credential. On April 12, as other inbound RDP connections scanned the network, the volume of data transferred by the RDP server to this IP address spiked. The RDP server never scans the internal network. Darktrace identified this as highly unusual activity.

Figure 6: Darktrace detects the anomalous external data transfer

Lateral movement and payload execution

Finally, on April 12, the attackers executed the Dharma payload at 13:45. The RDP server wrote a number of files over the SMB protocol, appended with a file extension containing a throwaway email account possibly evoking the current COVID-19 pandemic, ‘cov2020@aol[.]com’. The use of string ‘…@aol.com].ROGER’ and presence of a file named ‘FILES ENCRYPTED.txt’ resembles previous Dharma compromises.

Parallel to the encryption activity, the ransomware tried to spread and infect other machines by initiating successful SMB authentications using the same administrator credential seen during the internal reconnaissance. However, the destination devices did not encrypt any files themselves.

It was during the encryption activity that the internal IT staff pulled the plug from the compromised RDP server, thus ending the ransomware activity.

Conclusion

This incident supports the idea that ‘legacy’ ransomware may morph to resurrect itself to exploit vulnerabilities in remote working infrastructure during this pandemic.

Dharma executed here a fast-acting, planned, targeted, ransomware attack. The attackers used off-the-shelf tools (RDP, abusing SMB1 protocol) blurring detection and attribution by blending in with typical administrator activity.

Darktrace detected every stage of the attack without having to depend on threat intelligence or rules and signatures, and the internal security team acted on the malicious activity to prevent further damage.

This incident supports the idea that ‘legacy’ ransomware may morph to resurrect itself to exploit vulnerabilities in remote working infrastructure during this pandemic. Poorly-secured public-facing systems have been rushed out and security is neglected as companies prioritize availability – sacrificing security in the process. Financially-motivated actors weaponize these weak points.

The use of the COVID-related email ‘cov2020@aol[.]com’ during the attack indicates that the threat-actor is aware of and abusing the current global pandemic.

Recent attacks, such as APT41’s exploitation of the Zoho Manage Engine vulnerability last March, show that attacks against internet-facing infrastructure are gaining popularity as the initial intrusion vector. Indeed, as many as 85% of ransomware attacks use RDP as an entry vector. Ensuring that backups are isolated, configurations are hardened, and systems are patched is not enough – real-time detection of every anomalous action can help protect potential victims of ransomware.

Technical Details

Some of the detections on the RDP server:

  • Compliance / Internet Facing RDP server – exposure of critical server to Internet
  • Anomalous Connection / Application Protocol on Uncommon Port – external connections using an unusual port to rare endpoints
  • Device / Large Number of Connections to New Endpoints – indicative of peer-to-peer or scanning activity
  • Compliance / Incoming Remote Desktop – device is remotely controlled from an external source, increased rick of bruteforce
  • Compromise / Ransomware / Suspicious SMB Activity – reading and writing similar volumes of data to remote file shares, indicative of files being overwritten and encrypted
  • Anomalous File / Internal / Additional Extension Appended to SMB File – device is renaming network share files with an added extension, seen during ransomware activity

The graph below shows the timeline of Darktrace detections on the RDP server. The attack lifecycle is clearly observable.

Figure 7: The model breaches occurring over time

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.

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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
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