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August 16, 2021

What is Remote Desktop Protocol (RDP)? RDP Attack Analysis

In this case study, Darktrace analyzes how a rapid Remote Desktop Protocol (RDP) attack evolved to lateral movement just seven hours within an exposed server.
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
Oakley Cox
Director of Product
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16
Aug 2021

Late on a Saturday evening, a physical security company in the US was targeted by an attack after cyber-criminals exploited an exposed RDP server. By Sunday, all the organization’s internal services had become unusable. This blog will unpack the attack and the dangers of open RDP ports.

What is RDP?

With the shift to remote working, IT teams have relied on remote access tools to manage corporate devices and keep the show running. Remote Desktop Protocol (RDP) is a Microsoft protocol which enables administrators to access desktop computers. Since it gives the user complete control over the device, it is a valuable entry point for threat actors.

‘RDP shops’ selling credentials on the Dark Web have been around for years. xDedic, one of the most notorious crime forums which once boasted over 80,000 hacked servers for sale, was finally shut down by the FBI and Europol in 2019, five years after it had been founded. Selling RDP access is a booming industry because it provides immediate entry into an organization, removing the need to design a phishing email, develop malware, or manually search for zero-days and open ports. For less than $5, an attacker can purchase direct access to their target organization.

In the months following the COVID-19 outbreak, the number of exposed RDP endpoints increased by 127%. RDP usage surged as companies adapted to teleworking conditions, and it became almost impossible for traditional security tools to distinguish between the daily legitimate application of RDP and its exploitation. This led to a dramatic spike in successful server-side attacks. According to the UK’s National Cyber Security Centre, RDP is now the single most common attack vector used by cyber-criminals – particularly ransomware gangs.

Breakdown of an RDP compromise

Initial intrusion

In this real-world attack, the target organization had around 7,500 devices active, one of which was an Internet-facing server with TCP port 3389 – the default port for RDP – open. In other words, the port was configured to accept network packets.

Darktrace detected a successful incoming RDP connection from a rare external endpoint, which utilized a suspicious authentication cookie. Given that the device was subject to a large volume of external RDP connections, it is likely the attacker brute-forced their way in, though they could have used an exploit or bought credentials off the Dark Web.

As incoming connections on port 3389 to this service were commonplace and expected as part of normal business, the connection was not flagged by any other security tool.

Figure 1: Timeline of the attack — the total dwell time was one day

Internal reconnaissance

Following the initial compromise, the device was seen engaging in network scanning activity within its own subnet to escalate access. After the scan, the device made Windows Management Instrumentation (WMI) connections to multiple devices over DCE-RPC, which triggered multiple Darktrace alerts.

Figure 2: The graph highlights spikes in unusual activity events along with an accompanying large volume of model breaches

Command and control (C2)

The device then made a new RDP connection on a non-standard port, using an administrative authentication cookie to an endpoint which had never been seen on the network. Tor connections were observed after this point, indicating potential C2 communication.

Figure 3: Cyber AI Analyst - Darktrace's AI investigation tool - breaks down the different stages of the incident

Lateral movement

The attacker then attempted lateral movement via SMB service control pipes and PsExec to five devices within the breach device’s subnet, which were likely identified during the network scan.

By using native Windows admin tools (PsExec, WMI, and svcctl) for lateral movement, the attacker managed to ‘live off the land’, evading detection from the rest of the security stack.

Ask the Expert

The organization’s own internal services were unavailable, so they reached out to Darktrace’s 24/7 Ask the Expert service. Darktrace’s cyber experts quickly determined the scope and nature of the compromise using the AI and began the remediation process. As a result, the threat was neutralized before the attacker could achieve their objectives, which may have included crypto-mining, deploying ransomware, or exfiltrating sensitive data.

RDP vulnerability: Dangers of exposed servers

Prior to the events described above, Darktrace had observed incoming connections on RDP and SQL from a large variety of rare external endpoints, suggesting that the server had been probed many times before. When unnecessary services are left open to the Internet, compromise is inevitable – it is simply a matter of time.

This is especially true of RDP. In this case, the attacker managed to successfully carry out reconnaissance and open external communication all through their initial access to the RDP port. Threat actors are always looking for a way in, so what could be considered a compliance issue can easily, and quickly, devolve into compromise.

Out of control remote control

The attack happened out of hours – at a time when the security team were off work enjoying their Saturday evenings – and it progressed at remarkable speed, escalating from initial intrusion to lateral movement in less than seven hours. It is very common for attackers to exploit these human vulnerabilities, moving fast and remaining undetected until the IT team are back at their desks on Monday morning.

It is for this reason that a security solution which does not sleep – and which can detect and autonomously respond to threats around the clock – is critical. Self-Learning AI can keep up with threats which escalate at machine speed, stopping them at every turn.

Thanks to Darktrace analyst Steven Sosa for his insights on the above threat find.

Learn how an RDP attack led to the deployment of ransomware

Darktrace model detections:

  • Compliance / Incoming Remote Desktop
  • Device / Network Scan
  • Device / New or Uncommon WMI Activity
  • Device / Suspicious Network Scan Activity
  • Device / RDP Scan
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Outbound RDP to Unusual Port
  • Compliance / Possible Tor Usage
  • Compliance / High Priority Compliance Model Breach
  • Device / New or Unusual Remote Command Execution
  • Anomalous Connection / New or Uncommon Service Control
  • Device / New or Uncommon SMB Named Pipe
  • Device / Multiple Lateral Movement Model Breaches
  • Anomalous Connection / High Volume of New or Uncommon Service Control
  • Compliance / Outbound RDP
  • Anomalous Server Activity / Domain Controller Initiated to Client

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
Oakley Cox
Director of Product

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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.

[related-resource]

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