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July 19, 2021

Data Exfiltration Trends in Latin America

Darktrace reveals key findings on data exfiltration in Latin America. Discover the latest cyber threats and defense strategies.
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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19
Jul 2021

Data exfiltration is a popular enterprise for cyber-criminals. All organizations – from government agencies to small businesses – have sensitive data which can be stolen for extortion, competitive advantage, or further inroads in a company’s system. It is now the preferred technique of ransomware actors. Some Ransomware-as-a-Service (RaaS) groups have even pioneered a new type of ‘extortionware’, which focuses solely on data theft without encryption.

Easy money

At a pharmaceutical manufacturing institute in Latin America, Darktrace recently detected the exfiltration of critical files from the company, as this blog will explore.

The organization was an enticing target for two reasons. Firstly, pharmaceutical companies hold a wealth of valuable IP and patient data, which have come under sustained fire this last year as threat actors and nation states infiltrate vaccine research and distribution.

#1 most affected industry for data breaches: pharmaceuticals.

Secondly, Latin America is a treasure trove for cyber-criminals, thanks to huge economic growth in recent years, the digitization of major industries, alongside sub-standard cyber insurance policies and virtually nonexistent regulations.

Even before the COVID-19 pandemic, Brazil and Mexico were in Europol’s top ten affected countries. Since then, cases have skyrocketed – and many companies remain underprepared, facing limited support and pressure from government bodies. Strikingly, even though it has suffered estimated losses of almost $8 billion, Brazil still has no data protection law in place.

On top of financial crimes, the LATAM region has been targeted by state-sponsored groups linked to Russia, China, and Iran. Cyber-espionage is used as a method to gain the upper hand in negotiations and advance foreign interests in investment and trade.

Furthermore, as supply chains in the criminal world are hit by the effects of the pandemic, organized crime may begin to leverage the digital world – particularly fraud and phishing – as a possible source of income. La Familia Michoacana, a notorious drug cartel in Mexico, has reportedly begun enlisting Dark Web hackers.

Despite the number of threats facing Latin America, organizations have been slow to adopt defensive technology. So when the attacker in the case below chose a small organization in the LATAM region, they probably expected to face only signature-based, legacy security tools. Sensing that this would be easy prey – with little resistance and large profit to be made – the actor launched their first steps.

How the intrusion played out

During a Proof of Value trial with the company, Darktrace detected unusual activity from a server, following external remote connectivity.

Figure 1: Timeline of the attack.

The attack began when an internal server received an unusual connection over RDP from an external IP. The connection lasted five hours. The external IP then established a new SMB session to the same server using administrative credentials. The external IP leveraged SMB to access a file, which appeared to contain unencrypted passwords.

65% of the Colombian population now use the Internet, compared to only 3% in 2000.

From there, the external IP downloaded over 18,000 files over SMB. Based on the file names, it appears that the data was highly sensitive. In total, the external IP downloaded around 150 MB of data from the internal server.

Unusual activity post remote connections

Self-Learning AI detected that the IP address was 100% rare for the organization and server. The data transfer was also detected as unusual for the device’s ‘pattern of life’. Unfortunately, as Antigena was being trialled in passive mode, Darktrace could not intervene and disrupt the attack.

Nevertheless, Darktrace fired a number of high-confidence alerts to warn the security team. The figure below shows five-day activity from an example device in the same situation, with a high volume of clustered alerts. These reflect the unusual increase in volume transferred externally from a breach device.

Figure 2: A similar device received an incoming remote desktop connection, highlighted by the first model breach (orange dot). Shortly after, the external device accessed an unencrypted password file. At the same time, the device transferred an unusual volume of data to a rare external source IP.

Data exfiltration methods: RDP and password file access

The threat actor managed to bypass all the other existing security products in the company. They did this with legitimate administrative credentials, which were used to establish the RDP and SMB connections. RDP credentials are easily bought off the Dark Web and have proved a popular form of initial access, especially this year as employees continue to work remotely.

In addition, improper password management can unlock an organization’s digital kingdom. One of the accessed files was a password file, enabling the actor to quickly escalate privileges. After this point, only an AI-powered defensive tool could keep up with the speed of the intrusion.

Leveraging common protocols such as SMB to exfiltrate data is a common tactic. Internet-exposed servers are still a major risk to organizations as attackers exploit open and unused ports.

Moreover, the files transferred during the activity were saved as receipts with the names of partners and customers. This is extremely dangerous and could have put the company’s reputation at serious risk. Luckily, Self-Learning AI detected the malicious actions and warned the security team immediately, allowing them to stop further exfiltration and any follow-up activity.

Protecting sensitive data

The example above demonstrates that even the smallest of companies can fall victim to an attack. Small and medium-sized enterprises are targeted because they own important data and IP, yet often lack robust security and resources. This makes them simple catches compared to large establishments or governments.

Darktrace’s AI has the ability to detect malicious data exfiltration from subtle changes in behavior. In this case, the targeted server regularly transfers data in and out of the organization, yet Darktrace scored the incoming external IP with the highest rarity. In other words, Darktrace considered the data transfer activity highly unusual and outside of the server’s normal ‘pattern of life’.

This enabled the security team to respond to the threat and take the server offline for further investigation. If Darktrace Antigena had been active in the environment, it would have responded seconds after the initial compromise, stopping the threat at machine speed.

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

Learn how to defend your company from data exfiltration and malicious insiders

Darktrace model detections:

  • Compliance / Incoming Remote Desktop
  • Compliance / Possible Unencrypted Password File On Server
  • Anomalous Connection/ Data Sent to Rare Domain

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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November 27, 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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Ahmed Gardezi
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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