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May 21, 2020

Securing AWS Cloud Environments

Discover how self-learning AI in AWS environments detects and beats threats early with enterprise-wide analysis.
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
Andrew Tsonchev
VP, Security & AI Strategy, Field CISO
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21
May 2020

Cloud platforms transform the way we build digital infrastructure, allowing us to create incredibly innovative environments for business – but often, it’s at the cost of visibility and control.

With complex hybrid and multi-cloud infrastructures becoming an essential part of increasingly diverse digital estates, the journey to the cloud has fundamentally reshaped the traditional paradigm of the network perimeter, while expanding the attack surface at an alarming rate. Meanwhile, traditional security controls still only offer point solutions that rely on retrospective rules and threat signatures and fail to stop novel and advanced attacks.

To shoulder the weight of shared responsibility for cloud security, organizations require the approach offered by Darktrace DETECT & RESPOND. With Self-Learning AI, DETECT continuously learns what normal ‘patterns of life’ look like for every user, device, virtual machine, and container across an organization. By actively developing a bespoke understanding of ‘self,’ the DETECT can identify the subtle anomalies that point to an advanced attack, without any pre-defined assumptions of ‘good’ or ‘bad' and RESPOND can autonomously interfere to stop emerging threats without disrupting business operations.

As more and more businesses turn to AWS to leverage the benefits of cloud infrastructure, gaining visibility and security for AWS-hosted data and applications is absolutely crucial. The advent of AWS VPC traffic mirroring has allowed Darktrace to shine a light on blind spots in our customers’ AWS environments, ensuring that our Cyber AI security platform can stop any type of threat that emerges. With the AI-powered security securing your AWS environment, you can embrace all the benefits of the cloud with confidence.

Self-learning Cyber AI with granular, real-time visibility

VPC traffic mirroring gives our Self-Learning AI access to granular packet data, allowing DETECT to extract hundreds of features from the raw data and build rich behavioral models for our customers’ AWS cloud environments. This real-time visibility to the underlying fabric of AWS environments provided by VPC traffic mirroring helps Darktrace Cyber AI learn ‘on the job,’ continuously adapting as your business evolves. Darktrace provides the only security solution that learns in real time, a critical feature given the speed and scale of development in the cloud.

Unified control: Correlating patterns across infrastructure

Taking a fundamentally unique approach, DETECT actively correlates activity across AWS and beyond – whether your digital ecosystem includes other cloud environments, SaaS applications, or any range of on- and off-premise infrastructure. From a threat detection perspective, this is crucial, as security events detected in one part of an organization are often part of a broader security incident. This ensures that threats in the cloud are not siloed from monitoring of the rest of the infrastructure, nor are the implications for cloud security ignored when intrusions occur elsewhere in the network.

Neutralizing sophisticated and novel attacks

Legacy security controls miss novel and advanced attacks targeting cloud infrastructure. With VPC traffic mirroring supporting Darktrace Cyber AI’s understanding of an organization’s AWS environment, any slight changes from normal behavior that may indicate a potential threat can be detected immediately. This allows the DETECT to catch the full range of cloud-based attacks, from zero-day malware, to stealthy insider threats.

“Darktrace represents a new frontier in AI-based cyber defense. Our team now has complete real-time coverage across our SaaS applications and cloud containers.”

— CIO, City of Las Vegas

How it works: Using VPC traffic mirroring to analyze AWS traffic

For customers leveraging AWS within an IaaS model, Darktrace uses VPC traffic mirroring to collect metadata from mirrored VPC packets in a Darktrace probe known as a ‘vSensor’. The vSensor captures real-time traffic and selectively forwards relevant metadata to a Darktrace cloud instance or on-premise probe. From here, DETECT correlates VPC traffic with cloud, email, network, and SaaS traffic across a customer’s hybrid and multi-cloud infrastructure for analysis.

By utilizing VPC traffic mirroring in this way, the Immune System can perform deep packet inspection on traffic in the customer’s AWS cloud environment, up to and including the application layer. Hundreds of features are extracted from the raw data, ranging from high-level metrics of data flow quantities, to peer relationship meta-data, to specific application layer events. These features allow Darktrace Cyber AI to build rich behavioral models that let it understand normal patterns of life for the organization and detect malicious activity. It is important that Darktrace is able to construct these metrics from the raw data rather than relying on flow logs alone, as flow logs don't provide the required level of granularity or real-time events within connections.

For non-Nitro AWS instances, we deploy lightweight agents known as ‘OS-Sensors’ that feed relevant traffic to a local vSensor and, in turn, to a Darktrace cloud instance or on-premise probe. Once configured, OS-Sensors can easily be scaled as new instances are spun up. Darktrace also offers a specialized OS-Sensor that provides coverage in containerized systems like Docker and Kubernetes.

Richer context with AWS CloudTrail logs

In addition to analyzing data with VPC traffic mirroring, the DETECT also monitors management and data events within AWS. It does so via HTTP requests for logfiles generated by AWS CloudTrail, which monitors events from all AWS services, including:

  • EC2
  • IAM
  • S3
  • VPC
  • Lambda

Different event types produced via CloudTrail are organized by Darktrace into categories based on the action type and the AWS services that generate it. These different categories show up as metrics in the DETECT user interface, the Threat Visualizer. This information is used to provide even richer context in connection with mirrored traffic in VPCs, as well as all cloud, network, email, and SaaS traffic across a customer’s entire digital environment.

Darktrace deployment scenarios for AWS customers

For IaaS environments, Darktrace deploys a vSensor in each cloud environment. Within AWS environments, the vSensor captures real-time traffic with AWS VPC traffic mirroring. The receiving vSensor processes the data and feeds it back to the cloud-based Darktrace instance. AWS customers additionally have the option of deploying a ‘Darktrace Security Module’ to monitor IaaS management and data events at the API level, such as logins, editing virtual servers, or creating new access credentials.

Figure 1: A cloud-only deployment scenario — Darktrace manages a master cloud probe which receives traffic from sensors and connectors in IaaS and/or SaaS environments.

For hybrid IaaS deployments, Darktrace will similarly deploy vSensors, and OS-Sensors as appropriate. Cloud traffic and event data from AWS and any other cloud environments is then fed to a Darktrace probe in the cloud or on-premise network. For the latter scenario, Darktrace will deploy a physical appliance that ingests real-time network traffic via a SPAN port or network tap, allowing it to correlate patterns across the entire digital ecosystem.

Figure 2: A hybrid cloud deployment scenario, with multi-cloud infrastructure across AWS, Azure and GCP

For hybrid SaaS deployments, Darktrace will deploy provider-specific Darktrace Security Modules on either a physical or cloud-based Darktrace probe, in addition to any other relevant vSensors and OS-Sensors in place. SaaS data is then analyzed and correlated with traffic and user behaviors across AWS, other cloud environments, and any on- and off- premise cyber-physical infrastructure.

Figure 3: A hybrid SaaS deployment scenario

Defense against the full range of threats in the cloud

With the deep insight and powerful reaction capabilities of Cyber AI, Darktrace DETECT & RESPOND are the only proven technologies to stop the full range of cyber-threats in the cloud, including:

  • Critical misconfigurations
  • Insider threat
  • Compromised credentials
  • Novel and advanced malware
  • Password brute-force attacks
  • Data exfiltration
  • Lateral movement
  • Man-in-the-middle attacks
  • Crypto-jacking
  • Violations of policy

Case Studies

Crypto mining malware inadvertently installed

Darktrace detected a mistake from a junior DevOps engineer in a multinational organization with workloads across AWS and Azure and leveraging containerized systems like Docker and Kubernetes. The engineer accidentally downloaded an update that included a crypto miner, which led to an infection across multiple cloud production systems.

After the initial infection, the malware started beaconing out to an external command and control server, which was immediately picked up by Darktrace. With the external connection established and the attack mission instructions delivered, the crypto malware infection was then able to rapidly spread across the organization’s expansive cloud infrastructure at machine speed, infecting 20 cloud servers in under 15 seconds.

Extensive visibility into the organization’s AWS environment via VPC traffic mirroring was a key factor allowing Darktrace Cyber AI to identify the scale of the attack. With the dynamic and unified view across the company’s sprawling hybrid and multi-cloud infrastructure provided by Darktrace, the company’s security team was able to contain the attack within minutes, rather than hours or days. Even though the attack moved at machine speed, by leveraging solutions like VPC traffic mirroring to continuously analyze behavior in the cloud, Darktrace caught the threat at an early enough stage – well before the costs could start to mount.

Developer misuse of AWS cloud infrastructure

At an insurance group, a DevOps Engineer was attempting to build a parallel back-up infrastructure within AWS to replicate the organization’s data center production systems. The technical implementation was perfect, and the back-up systems were created – however, the cost of running the system would have been several million dollars per year.

The DevOps Engineer was unaware of the costs associated with the project and kept management in the dark. The cloud infrastructure was launched, and the costs started rising. Yet with real-time access to the company’s AWS environment provided by VPC traffic mirroring, Darktrace’s Cyber AI was immediately alerted to this unusual behavior, allowing the security team to take preventative action immediately.

With Darktrace Cyber AI, embrace the benefits of AWS

As organizations increasingly turn to the cloud and the threat surface continues to expand, security teams need self-learning AI on their side to gain the strongest insights, illuminate every blind spot, and stop all attacks.

By providing an enterprise-wide Cyber AI platform, Darktrace helps teams overcome the traditional security challenge of manually piecing together incidents across disparate corners of an organization. The unified visibility and control offered by Darktrace PREVENT, DETECTRESPOND, & HEAL reduces the complexity and dashboard fatigue that many teams continue to struggle with, while the system’s multi-dimensional insight enhances its decision-making and threat confidence. Darktrace further augments this process with the Immune System’s AI Analyst capability, which takes the additional step of automatically investigating threats detected by Darktrace and producing concise, AI-generated reports that communicate the full scope of an incident.

With the granular, real-time visibility of VPC traffic mirroring Darktrace, you can be certain your AWS cloud environments are always protected.

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
Andrew Tsonchev
VP, Security & AI Strategy, Field CISO

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November 26, 2025

CastleLoader & CastleRAT: Behind TAG150’s Modular Malware Delivery System

CastleLoader & CastleRAT: Behind TAG150’s Modular Malware Delivery SystemDefault blog imageDefault blog image

What is TAG-150?

TAG-150, a relatively new Malware-as-a-Service (MaaS) operator, has been active since March 2025, demonstrating rapid development and an expansive, evolving infrastructure designed to support its malicious operations. The group employs two custom malware families, CastleLoader and CastleRAT, to compromise target systems, with a primary focus on the United States [1]. TAG-150’s infrastructure included numerous victim-facing components, such as IP addresses and domains functioning as command-and-control (C2) servers associated with malware families like SecTopRAT and WarmCookie, in addition to CastleLoader and CastleRAT [2].

As of May 2025, CastleLoader alone had infected a reported 469 devices, underscoring the scale and sophistication of TAG-150’s campaign [1].

What are CastleLoader and CastleRAT?

CastleLoader is a loader malware, primarily designed to download and install additional malware, enabling chain infections across compromised systems [3]. TAG-150 employs a technique known as ClickFix, which uses deceptive domains that mimic document verification systems or browser update notifications to trick victims into executing malicious scripts. Furthermore, CastleLoader leverages fake GitHub repositories that impersonate legitimate tools as a distribution method, luring unsuspecting users into downloading and installing malware on their devices [4].

CastleRAT, meanwhile, is a remote access trojan (RAT) that serves as one of the primary payloads delivered by CastleLoader. Once deployed, CastleRAT grants attackers extensive control over the compromised system, enabling capabilities such as keylogging, screen capturing, and remote shell access.

TAG-150 leverages CastleLoader as its initial delivery mechanism, with CastleRAT acting as the main payload. This two-stage attack strategy enhances the resilience and effectiveness of their operations by separating the initial infection vector from the final payload deployment.

How are they deployed?

Castleloader uses code-obfuscation methods such as dead-code insertion and packing to hinder both static and dynamic analysis. After the payload is unpacked, it connects to its command-and-control server to retrieve and running additional, targeted components.

Its modular architecture enables it to function both as a delivery mechanism and a staging utility, allowing threat actors to decouple the initial infection from payload deployment. CastleLoader typically delivers its payloads as Portable Executables (PEs) containing embedded shellcode. This shellcode activates the loader’s core module, which then connects to the C2 server to retrieve and execute the next-stage malware.[6]

Following this, attackers deploy the ClickFix technique, impersonating legitimate software distribution platforms like Google Meet or browser update notifications. These deceptive sites trick victims into copying and executing PowerShell commands, thereby initiating the infection kill chain. [1]

When a user clicks on a spoofed Cloudflare “Verification Stepprompt, a background request is sent to a PHP script on the distribution domain (e.g., /s.php?an=0). The server’s response is then automatically copied to the user’s clipboard using the ‘unsecuredCopyToClipboard()’ function. [7].

The Python-based variant of CastleRAT, known as “PyNightShade,” has been engineered with stealth in mind, showing minimal detection across antivirus platforms [2]. As illustrated in Figure 1, PyNightShade communicates with the geolocation API service ip-api[.]com, demonstrating both request and response behavior

Packet Capture (PCAP) of PyNightShade, the Python-based variant of CastleRAT, communicating with the geolocation API service ip-api[.]com.
Figure 1: Packet Capture (PCAP) of PyNightShade, the Python-based variant of CastleRAT, communicating with the geolocation API service ip-api[.]com.

Darktrace Coverage

In mid-2025, Darktrace observed a range of anomalous activities across its customer base that appeared linked to CastleLoader, including the example below from a US based organization.

The activity began on June 26, when a device on the customer’s network was observed connecting to the IP address 173.44.141[.]89, a previously unseen IP for this network along with the use of multiple user agents, which was also rare for the user.  It was later determined that the IP address was a known indicator of compromise (IoC) associated with TAG-150’s CastleRAT and CastleLoader operations [2][5].

Figure 2: Darktrace’s detection of a device making unusual connections to the malicious endpoint 173.44.141[.]89.

The device was observed downloading two scripts from this endpoint, namely ‘/service/download/data_5x.bin’ and ‘/service/download/data_6x.bin’, which have both been linked to CastleLoader infections by open-source intelligence (OSINT) [8]. The archives contains embedded shellcode, which enables attackers to execute arbitrary code directly in memory, bypassing disk writes and making detection by endpoint detection and response (EDR) tools significantly more difficult [2].

 Darktrace’s detection of two scripts from the malicious endpoint.
Figure 3: Darktrace’s detection of two scripts from the malicious endpoint.

In addition to this, the affected device exhibited a high volume of internal connections to a broad range of endpoints, indicating potential scanning activity. Such behavior is often associated with reconnaissance efforts aimed at mapping internal infrastructure.

Darktrace / NETWORK correlated these behaviors and generated an Enhanced Monitoring model, a high-fidelity security model designed to detect activity consistent with the early stages of an attack. These high-priority models are continuously monitored and triaged by Darktrace’s Security Operations Center (SOC) as part of the Managed Threat Detection and Managed Detection & Response services, ensuring that subscribed customers are promptly alerted to emerging threats.

Darktrace detected an unusual ZIP file download alongside the anomalous script, followed by internal connectivity. This activity was correlated under an Enhanced Monitoring model.
Figure 4: Darktrace detected an unusual ZIP file download alongside the anomalous script, followed by internal connectivity. This activity was correlated under an Enhanced Monitoring model.

Darktrace Autonomous Response

Fortunately, Darktrace’s Autonomous Response capability was fully configured, enabling it to take immediate action against the offending device by blocking any further connections external to the malicious endpoint, 173.44.141[.]89. Additionally, Darktrace enforced a ‘group pattern of life’ on the device, restricting its behavior to match other devices in its peer group, ensuring it could not deviate from expected activity, while also blocking connections over 443, shutting down any unwanted internal scanning.

Figure 5: Actions performed by Darktrace’s Autonomous Response to contain the ongoing attack.

Conclusion

The rise of the MaaS ecosystem, coupled with attackers’ growing ability to customize tools and techniques for specific targets, is making intrusion prevention increasingly challenging for security teams. Many threat actors now leverage modular toolkits, dynamic infrastructure, and tailored payloads to evade static defenses and exploit even minor visibility gaps. In this instance, Darktrace demonstrated its capability to counter these evolving tactics by identifying early-stage attack chain behaviors such as network scanning and the initial infection attempt. Autonomous Response then blocked the CastleLoader IP delivering the malicious ZIP payload, halting the attack before escalation and protecting the organization from a potentially damaging multi-stage compromise

Credit to Ahmed Gardezi (Cyber Analyst) Tyler Rhea (Senior Cyber Analyst)
Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

  • Anomalous Connection / Unusual Internal Connections
  • Anomalous File / Zip or Gzip from Rare External Location
  • Anomalous File / Script from Rare External Location
  • Initial Attack Chain Activity (Enhanced Monitoring Model)

MITRE ATT&CK Mapping

  • T15588.001 - Resource Development – Malware
  • TG1599 – Defence Evasion – Network Boundary Bridging
  • T1046 – Discovery – Network Service Scanning
  • T1189 – Initial Access

List of IoCs
IoC - Type - Description + Confidence

  • 173.44.141[.]89 – IP – CastleLoader C2 Infrastructure
  • 173.44.141[.]89/service/download/data_5x.bin – URI – CastleLoader Script
  • 173.44.141[.]89/service/download/data_6x.bin – URI  - CastleLoader Script
  • wsc.zip – ZIP file – Possible Payload

References

[1] - https://blog.polyswarm.io/castleloader

[2] - https://www.recordedfuture.com/research/from-castleloader-to-castlerat-tag-150-advances-operations

[3] - https://www.pcrisk.com/removal-guides/34160-castleloader-malware

[4] - https://www.scworld.com/brief/malware-loader-castleloader-targets-devices-via-fake-github-clickfix-phishing

[5] https://www.virustotal.com/gui/ip-address/173.44.141.89/community

[6] https://thehackernews.com/2025/07/castleloader-malware-infects-469.html

[7] https://www.cryptika.com/new-castleloader-attack-using-cloudflare-themed-clickfix-technique-to-infect-windows-computers/

[8] https://www.cryptika.com/castlebot-malware-as-a-service-deploys-range-of-payloads-linked-to-ransomware-attacks/

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Tyler Rhea
Senior Cyber Analyst

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November 26, 2025

UK Cyber Security & Resilience Bill: What Organizations Need to Know

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Why the Bill has been introduced

The UK’s cyber threat landscape has evolved dramatically since the 2018 NIS regime was introduced. Incidents such as the Synnovis attack against hospitals and the British Library ransomware attack show how quickly operational risk can become public harm. In this context, the UK Department for Science, Innovation and Technology estimates that cyber-attacks cost UK businesses around £14.7 billion each year.

At the same time, the widespread adoption of AI has expanded organisations’ attack surfaces and empowered threat actors to launch more effective and sophisticated activities, including crafting convincing phishing campaigns, exploiting vulnerabilities and initiating ransomware attacks at unprecedented speed and scale.  

The CSRB responds to these challenges by widening who is regulated, accelerating incident reporting and tightening supply chain accountability, while enabling rapid updates that keep pace with technology and emerging risks.

Key provisions of the Cyber Security and Resilience Bill

A wider set of organisations in scope

The Bill significantly broadens the range of organisations regulated under the NIS framework.

  • Managed service providers (MSPs) - medium and large MSPs, including MSSPs, managed SOCs, SIEM providers and similar services,will now fall under NIS obligations due to their systemic importance and privileged access to client systems. The Information Commissioner’s Office (ICO) will act as the regulator. Government analysis anticipates that a further 900 to 1,100 MSPs will be in scope.
  • Data infrastructure is now recognised as essential to the functioning of the economy and public services. Medium and large data centres, as well as enterprise facilities meeting specified thresholds, will be required to implement appropriate and proportionate measures to manage cyber risk. Oversight will be shared between DSIT and Ofcom, with Ofcom serving as the operational regulator.
  • Organisations that manage electrical loads for smart appliances, such as those supporting EV charging during peak times, are now within scope.

These additions sit alongside existing NIS-regulated sectors such as transport, energy, water, health, digital infrastructure, and certain digital services (including online marketplaces, search engines, and cloud computing).

Stronger supply chain requirements

Under the CSRB, regulators can now designate third-party suppliers as ‘designated critical suppliers’ (DCS) when certain threshold criteria are met and where disruption could have significant knock-on effects. Designated suppliers will be subject to the same security and incident-reporting obligations as Operators of Essential Services (OES) and Relevant Digital Service Providers (RDSPs).

Government will scope the supply chain duties for OES and RDSPs via secondary legislation, following consultation. infrastructure incidents where a single supplier’s compromise caused widespread disruption.

Faster incident reporting

Sector-specific regulators, 12 in total, will be responsible for implementing the CSRB, allowing for more effective and consistent reporting. In addition, the CSRB introduces a two-stage reporting process and expands incident reporting criteria. Regulated entities must submit an initial notification within 24 hours of becoming aware of a significant incident, followed by an incident report within 72 hours. Incident reporting criteria are also broadened to capture incidents beyond those which actually resulted in an interruption, ensuring earlier visibility for regulators and the National Cyber Security Centre (NCSC). The importance of information sharing across agencies, law enforcement and regulators is also facilitated by the CSRB.

The reforms also require data centres and managed service providers to notify affected customers where they are likely to have been impacted by a cyber incident.

An agile regulatory framework

To keep pace with technological change, the CSRB will enable the Secretary of State to update elements of the framework via secondary legislation. Supporting materials such as the NCSC Cyber Assessment Framework (CAF) are to be "put on a stronger footing” allowing for requirements to be more easily followed, managed and updated. Regulators will also now be able to recover full costs associated with NIS duties meaning they are better resourced to carry out their associated responsibilities.

Relevant Managed Service Providers must identify and take appropriate and proportionate measures to manage risks to the systems they rely on for providing services within the UK. Importantly, these measures must, having regard to the state of the art, ensure a level of security appropriate to the risk posed, and prevent or minimise the impact of incidents.

The Secretary of State will also be empowered to issue a Statement of Strategic Priorities, setting cross-regime outcomes to drive consistency across the 12 competent authorities responsible for implementation.

Penalties

The enforcement framework will be strengthened, with maximum fines aligned with comparable regimes such as the GDPR, which incorporate maximums tied to turnover. Under the CSRB, maximum penalties for more serious breaches could be up to £17 million or 4% of global turnover, whichever is higher.

Next steps

The Bill is expected to progress through Parliament over the course of 2025 and early 2026, with Royal Assent anticipated in 2026. Once enacted, most operational measures will not take immediate effect. Instead, Government will bring key components into force through secondary legislation following further consultation, providing regulators and industry with time to adjust practices and prepare for compliance.

Anticipated timeline

  • 2025-2026: Parliamentary scrutiny and passage;
  • 2026: Royal Assent;  
  • 2026 consultation: DSIT intends to consult on detailed implementation;
  • From 2026 onwards: Phased implementation via secondary legislation, following further consultation led by DSIT.

How Darktrace can help

The CSRB represents a step change in how the UK approaches digital risk, shifting the focus from compliance to resilience.

Darktrace can help organisations operationalise this shift by using AI to detect, investigate and respond to emerging threats at machine speed, before they escalate into incidents requiring regulatory notification. Proactive tools which can be included in the Darktrace platform allow security teams to stress-test defences, map supply chain exposure and rehearse recovery scenarios, directly supporting the CSRB’s focus on resilience, transparency and rapid response. If an incident does occur, Darktrace’s autonomous agent, Cyber AI Analyst, can accelerate investigations and provide a view of every stage of the attack chain, supporting timely reporting.  

Darktrace’s AI can provide organisations with a vital lens into both internal and external cyber risk. By continuously learning patterns of behaviour across interconnected systems, Darktrace can flag potential compromise or disruption to detect supply chain risk before it impacts your organisation.

In a landscape where compliance and resilience go hand in hand, Darktrace can equip organisations to stay ahead of both evolving threats and evolving regulatory requirements.

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