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December 16, 2020

ZeroLogon Vulnerability Identified & Stopped

Learn how the ZeroLogon exploit was detected within 24 hours of the vulnerability notice and its implications on cybersecurity practices.
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.
Written by
Max Heinemeyer
Global Field CISO
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16
Dec 2020

On September 14, the Cybersecurity and Infrastructure Agency (CISA) announced that a damaging exploit code for CVE-2020-1472 was publicly available. Within 24 hours, Darktrace AI had detected a cyber-attack on a healthcare company exploiting this very flaw.

CVE-2020-1472, or ZeroLogon, is a particularly concerning vulnerability since, despite its sophistication, a low skill level is required to capitalize on it, and successful exploitation results in administrative control over an entire digital system. Attackers have been quick to share and utilize versions of the weaponized exploit code, targeting companies to gain control and cause damage.

The vulnerability comes from the ‘Netlogon’ Remote Protocol (MS-NRPC), which authenticates users accessing Windows Servers. A flaw in the cryptography means that there is a probability – 1/256, that the cipher text will come out as a sequence of zeros and not random numbers. The Initialization Vector (IV) can thus be set to zeros in an average of 128 attempts: a few seconds for an attacker.

Attackers can then take control of any computer, including the root domain controller, by resetting the computer’s password. Hackers commonly use public red-teaming tools to facilitate this attack, such as the use of Cobalt Strike for command and control (C2). If a cyber-criminal is successful in gaining domain admin privileges, the results can be devastating. Once in control, the attacker could launch a denial of service or ransomware attack or exfiltrate sensitive company data.

Darktrace’s unique approach defends against such vulnerabilities by detecting new and unknown threats in their earliest stages. The visibility provided by Darktrace Cyber AI allows security teams to quickly correlate all related activity and respond accordingly.

Attack overview

Figure 1: A timeline of the attack

Darktrace detected a ZeroLogon exploitation at a healthcare company in Europe. Hackers were detected using the CVE-2020-1472 privilege escalation flaw to try to gain domain admin control, with a view to taking over the digital system or perhaps causing a denial of service.

Figure 2: Model Breach Event Log for the unusual RPC detection, detailing the numerous calls to Netlogon within a short time frame

The company had around 50,000 devices across its digital estate. One device began making a large volume of repeated TXT DNS requests, resembling the DNS Beacon from Cobalt Strike. Approximately one week later, the device made a large volume of unusual RPC calls to an internal domain controller. Successful calls to the ‘Netlogon’ service were observed, indicating that this was an exploitation of the ZeroLogon vulnerability.

Darktrace’s Cyber AI Analyst launched an automatic investigation into the incident and generated a high-level summary in natural language, surfacing the key metrics to the security team.

Figure 3: AI Analyst coverage of the initial command and control activity from the device in question

The C2 activity was entirely conducted using DNS. As this was a new vulnerability, the hackers were able to bypass the rest of the security stack, undetected by traditional antivirus and signature-based tools. In total, the time spent in the company’s digital environment was approximately eight days.

Cyber-criminals don’t hang around

CVE-2020-1472 was first published on August 11 and a partial patch was released by Microsoft at the time. On September 14, CISA addressed their awareness of the ZeroLogon exploit code. The Common Vulnerability Scoring System (CVSS) had given it a severity score of 10/10.

The AI detection and response took place less than 24 hours after this notice, demonstrating how quickly modern cyber-criminals move.

Unpatched vulnerabilities account for 60% of all cyber-attacks and are ubiquitous in cyber-space. Human security teams simply cannot keep up with the ever-increasing number of vulnerabilities and patches released by software vendors. There is always a delay as IT teams rush to implement the necessary defenses. Microsoft is planning to release a more complete patch, but this is not scheduled until February 2021.

Crucially, traditional security tools that rely on the ‘legacy approach’ – using pre-defined rules and playbooks of known threats – are blind to these vulnerabilities. The speed at which the attackers moved in this case demonstrates the importance of detecting unusual behaviors at the earliest stages of an attack.

Autonomous Response

Darktrace’s AI picked up on this attack immediately, as soon as the device had begun the Cobalt DNS Beacon. In active mode, Antigena, Darktrace’s Autonomous Response capability, would have actioned a surgical response to block the command and control (C2) activity as well as the suspicious RPC requests to the internal domain controller. In this instance, Darktrace Antigena was set to passive mode, and so the attack was allowed to continue.

In today’s fast-moving cyber landscape, AI defense is instrumental in fighting back against potential threats. Darktrace Cyber AI does not rely on rules and signatures, but spots novel threats by understanding the ‘pattern of life’ for every user and device, and flagging anomalous activity as it happens, protecting companies from zero-day exploits and new vulnerabilities such as ZeroLogon.

Thanks to Darktrace analyst Kendra Gonzalez Duran for her insights on the above threat find.

Learn more about Autonomous Response

Darktrace model detections:

  • Compromise / DNS / Possible DNS Beacon
  • Compromise / DNS / Cobalt DNS
  • Compromise / DNS / DNS Tunnel with TXT Records
  • Compromise / Suspicious Netlogon RPC Calls

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.
Written by
Max Heinemeyer
Global Field CISO

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May 8, 2025

Anomaly-based threat hunting: Darktrace's approach in action

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What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

  • MITRE Attack
  • Tactics, Techniques, Procedures (TTPs)
  • Diamond Model for Intrusion Analysis
  • Adversary, Infrastructure, Victims, Capabilities
  • Threat Hunt Model – Six Steps
  • Purpose, Scope, Equip, Plan, Execute, Feedback
  • Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

  • Hypothesis-based threat hunting
  • Analytics-driven threat hunting
  • Automated/machine learning hunting
  • Indicator of Compromise (IoC) hunting
  • Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

References

  1. https://spur.us/context/191.96.106.219

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

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With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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