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November 23, 2022

How Darktrace Could Have Stopped a Surprise DDoS Incident

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Nov 2022
Learn how Darktrace could revolutionize DDoS defense, enabling companies to stop threats without 24/7 monitoring. Read more about how we thwart attacks!

When is the best time to be hit with a cyber-attack?

The answer that springs to most is ‘Never’,  however in today’s threat landscape, this is often wishful thinking. The next best answer is ‘When we’re ready for it’. Yet, this does not take into account the intention of those committing attacks. The reality is that the best time for a cyber-attack is when no one else is around to stop it.

When do cyber attacks happen?

Previous analysis from Mandiant reveals that over half of ransomware compromises occur at out of work hours, a trend Darktrace has also witnessed in the past two years [1]. This is deliberate, as the fewer people that are online, the harder it is to get ahold of security teams and the higher the likelihood there is of an attacker achieving their goals. Given this landscape, it is clear that autonomous response is more important than ever. In the absence of human resources, autonomous security can fill in the gap long enough for IT teams to begin remediation. 

This blog will detail an incident where autonomous response provided by Darktrace RESPOND would have entirely prevented an infection attempt, despite it occurring in the early hours of the morning. Because the customer had RESPOND in human confirmation mode (AI response must first be approved by a human), the attempt by XorDDoS was ultimately successful. Given that the attack occurred in the early hours of the morning, there was likely no one around to confirm Darktrace RESPOND actions and prevent the attack.

XorDDoS Primer

XorDDoS is a botnet, a type of malware that infects devices for the purpose of controlling them as a collective to carry out specific actions. In the case of XorDDoS, it infects devices in order to carry out denial of service attacks using said devices. This year, Microsoft has reported a substantial increase in activity from this malware strain, with an increased focus on Linux based operating systems [2]. XorDDoS most commonly finds its way onto systems via SSH brute-forcing, and once deployed, encrypts its traffic with an XOR cipher. XorDDoS has also been known to download additional payloads such as backdoors and cryptominers. Needless to say, this is not something you have on a corporate network. 

Initial Intrusion of XorDDoS

The incident begins with a device first coming online on 10th August. The device appeared to be internet facing and Darktrace saw hundreds of incoming SSH connections to the device from a variety of endpoints. Over the course of the next five days, the device received thousands of failed SSH connections from several IP addresses that, according to OSINT, may be associated with web scanners [3]. Successful SSH connections were seen from internal IP addresses as well as IP addresses associated with IT solutions relevant to Asia-Pacific (the customer’s geographic location). On midnight of 15th August, the first successful SSH connection occurred from an IP address that has been associated with web scanning. This connection lasted around an hour and a half, and the external IP uploaded around 3.3 MB of data to the client device. Given all of this, and what the industry knows about XorDDoS, it is likely that the client device had SSH exposed to the Internet which was then brute-forced for initial access. 

There were a few hours of dwell until the device downloaded a ZIP file from an Iraqi mirror site, mirror[.]earthlink[.]iq at around 6AM in the customer time zone. The endpoint had only been seen once before and was 100% rare for the network. Since there has been no information on OSINT around this particular endpoint or the ZIP files downloaded from the mirror site, the detection was based on the unusualness of the download.

Following this, Darktrace saw the device make a curl request to the external IP address 107.148.210[.]218. This was highlighted as the user agent associated with curl had not been seen on the device before, and the connection was made directly to an IP address without a hostname (suggesting that the connection was scripted). The URIs of these requests were ‘1.txt’ and ‘2.txt’. 

The ‘.txt’ extensions on the URIs were deceiving and it turned out that both were executable files masquerading as text files. OSINT on both of the hashes revealed that the files were likely associated with XorDDoS. Additionally, judging from packet captures of the connection, the true file extension appeared to be ‘.ELF’. As XorDDoS primarily affects Linux devices, this would make sense as the true extension of the payload. 

Figure 1: Packet capture of the curl request made by the breach device.

C2 Connections

Immediately after the ‘.ELF’ download, Darktrace saw the device attempting C2 connections. This included connections to DGA-like domains on unusual ports such as 1525 and 8993. Luckily, the client’s firewall seems to have blocked these connections, but that didn’t stop XorDDoS. XorDDoS continued to attempt connections to C2 domains, which triggered several Proactive Threat Notifications (PTNs) that were alerted by SOC. Following the PTNs, the client manually quarantined the device a few hours after the initial breach. This lapse in actioning was likely due to an early morning timing with the customer’s employees not being online yet. After the device was quarantined, Darktrace still saw XorDDoS attempting C2 connections. In all, hundreds of thousands of C2 connections were detected before the device was removed from the network sometime on 7th September.

Figure 2: AI Analyst was able to identify the anomalous activity and group it together in an easy to parse format.

An Alternate Timeline 

Although the device was ultimately removed, this attack would have been entirely prevented had RESPOND/Network not been in human confirmation mode. Autonomous response would have kicked in once the device downloaded the ‘.ZIP file’ from the Iraqi mirror site and blocked all outgoing connections from the breach device for an hour:

Figure 3: Screenshot of the first Antigena (RESPOND) breach that would have prevented all subsequent activity.

The model breach in Figure 3 would have prevented the download of the XorDDoS executables, and then prevented the subsequent C2 connections. This hour would have been crucial, as it would have given enough time for members of the customer’s security team to get back online should the compromised device have attempted anything else. With everyone attentive, it is unlikely that this activity would have lasted as long as it did. Had the attack been allowed to progress further, the infected device would have at the very least been an unwilling participant in a future DDoS attack. Additionally, the device could have a backdoor placed within it, and additional malware such as cryptojackers might have been deployed. 

Conclusions 

Unfortunately, we do not exist in the alternate timeline that autonomous response would have prevented this whole series of events.Luckily, although it was not in place, the PTN alerts provided by Darktrace’s SOC team still sped up the process of remediation in an event that was never intended to be discovered given the time it occurred. Unusual times of attack are not just limited to ransomware, so organizations need to have measures in place for the times that are most inconvenient to them, but most convenient to attackers. With Darktrace/RESPOND however, this is just one click away.

Thanks to Brianna Leddy for their contribution.

Appendices

Darktrace Model Detections

Below is a list of model breaches in order of trigger. The Proactive Threat Notification models are in bold and only the first Antigena [RESPOND] breach that would have prevented the initial compromise has been included. A manual quarantine breach has also been added to show when the customer began remediation.

  • Compliance / Incoming SSH, August 12th 23:39 GMT +8
  • Anomalous File / Zip or Gzip from Rare External Location, August 15th, 6:07 GMT +8 
  • Antigena / Network / External Threat / Antigena File then New Outbound Block, August 15th 6:36 GMT +8 [part of the RESPOND functionality]
  • Anomalous Connection / New User Agent to IP Without Hostname, August 15th 6:59 GMT +8‍
  • Anomalous File / Numeric Exe Download, August 15th 6:59 GMT +8‍
  • Anomalous File / Masqueraded File Transfer, August 15th 6:59 GMT +8
  • Anomalous File / EXE from Rare External Location, August 15th 6:59 GMT +8
  • Device / Internet Facing Device with High Priority Alert, August 15th 6:59 GMT +8
  • Compromise / Rare Domain Pointing to Internal IP, August 15th 6:59 GMT +8‍
  • Device / Initial Breach Chain Compromise, August 15th 6:59 GMT +8
  • Compromise / Large Number of Suspicious Failed Connections, August 15th 7:01 GMT +8
  • Compromise / High Volume of Connections with Beacon Score, August 15th 7:04 GMT +8
  • Compromise / Fast Beaconing to DGA, August 15th 7:04 GMT +8‍
  • Compromise / Suspicious File and C2, August 15th 7:04 GMT +8
  • Antigena / Network / Manual / Quarantine Device, August 15th 8:54 GMT +8 [part of the RESPOND functionality]

List of IOCs

MITRE ATT&CK Mapping

Reference List

[1] They Come in the Night: Ransomware Deployment Trends

[2] Rise in XorDdos: A deeper look at the stealthy DDoS malware targeting Linux devices

[3] Alien Vault: Domain Navicatadvvr & https://www.virustotal.com/gui/domain/navicatadvvr.com & https://maltiverse.com/hostname/navicatadvvr.com

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.
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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
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