Blog
/
AI
/
December 13, 2023

Defending Against Personalized Cyber Attacks

Stay informed about the latest trends in cyber threats with Darktrace experts, including how attacks are evolving and becoming more personalized.
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
The Darktrace Community
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
13
Dec 2023

Cyber-attacks are getting personal. The usual opportunistic “spray and pray” attacks that reach many would-be targets at once are still present, but as cyber defence has advanced, today’s more sophisticated campaigns take precise aim at a particular company.

Threat actors willingly put in extra time and effort to realize a bigger payday at the end of it, but developments in the tools they have at their disposal are also making targeted, personal attacks easier.

CAPTCHA-breaking AI techniques like computer vision and convolutional neural networks can be used to gather information on an organization’s attack surface, and Generative AI is able to perform OSINT collection on a specific target, or targets, within an organization. Once inside, attackers can further leverage AI to automatically tweak attacks and create novel, highly targeted threats that elude defenses.

A new white paper, The CISO’s Guide to Cyber AI, explains how CISOs and their teams can make smarter use of defensive AI and machine learning (ML) to protect today’s digital environments from these and more advanced novel threats.

Today’s threats don’t necessarily resemble past attacks  

Darktrace analytics pointed to a sharp rise in novel cyber-attacks earlier this year. Generative AI and large language model (LLM) tools continue to lower the barrier to entry for threat actors, making it easier than ever to build smarter, faster, more targeted attacks.

But while attacks are getting personal, security tools that apply AI in the wrong way won’t see these attacks coming.

Here’s why: most cyber security tools and platforms rely on a combination of supervised machine learning, deep learning, and transformers to train and inform their systems. This entails shipping your company’s data out to a large data lake housed somewhere in the cloud where it gets blended with attack data from thousands of other organizations. The resulting homogenized data set gets used to train AI systems — yours and everyone else’s — to recognize patterns of attack based on previously encountered threats.

At its conception, this was a reasonably smart way of approaching cyber security. For a long time, the assumption that today’s threats will resemble yesterday’s attacks was a valid one. But in an age where the commoditization of cyber-crime has lowered the bar-to-entry for attackers, and where Generative AI and other open-source tools are enabling personalized attacks at scale, this is no longer the case.

Darktrace has seen evidence this year of a marked rise in more sophisticated attack techniques. Between May and July this year, our Cyber AI Research Centre observed that multistage payload attacks, in which a malicious email encourages the recipient to follow a series of steps before delivering a payload or attempting to harvest sensitive information, have increased by an average of 59% across Darktrace customers. Some of this will be QR code phishing, the latest trend in attack tactics, others will include automation. The speed of these types of attacks will likely rise as greater automation and AI are adopted and applied by attackers.

This ‘historical’ approach is not able to identify threats that haven’t been seen before: attacks that use new malware, novel social engineering, and those that are targeted to your organization. There are no indicators of compromise (IoCs) to teach your system to recognize these kinds of attacks.

IoC-based defenses won’t necessarily spot strange and unusual activity by an authorized user, device, or known IP address until threat actors tip their hand — and by then it’s too late. Looking for repeat patterns works well for detecting threats that resemble past attacks, but this increasingly won’t be the case. The only way to spot unique and novel threats is to build cyber security that’s tailored to you, and that requires a whole new approach.

Smarter use of AI levels the playing field

Security teams and adversaries continue to innovate to gain the upper-hand, and the advantage of time.

Since AI equips even novice cyber criminals to mount sophisticated attacks, AI must evolve to do three things:

  • Understand and continue to learn what “normal” looks like for your unique digital environment
  • Detect and alert on any anomalous behavior the instant it occurs
  • Initiate a targeted response to contain threats and give your analysts more time, without disrupting the flow of business

Darktrace uses Self-Learning AI to understand what constitutes ‘normal’ for everyone and everything in your business, including cloud resources, identities, email accounts, endpoint devices, and even OT controllers. As the name suggests, Self-Learning AI trains itself, developing and maintaining deep understanding of ‘patterns of life’ for your business environment. Used in combination with other AI methods such as LLMs, generative AI, and supervised ML, Self-Learning AI identifies novel cyber-threats most static (backward-looking) tools miss.

The technology learns ‘on the job’ and from scratch, without relying on historical data or a massive upfront effort by your team to train the system. Probabilistic mathematics revise assumptions about behavior on a constant basis so the system keeps itself up-to-date without repeat efforts by your team.

The result is that areas of risk, as well as real-time emerging attacks, are brought to the surface – regardless of whether those attacks have been seen before in the wild.

Surgical attacks warrant surgical response

Supervised ML continues to serve a purpose, but the dawning age of novel and AI-led attacks favors a more proactive approach to securing the cloud. Tools must take greater responsibility for their own education and greater initiative via autonomous response.

What some solutions call response ultimately amounts to sending alerts and opening tickets that create more needless work for analysts. Other tools claim to automate response, but either take very limited actions like automating the process of ticket creation, or overly ambitious steps like quarantining entire systems.

Darktrace’s dynamic understanding of your environment enables a truly autonomous and precise cloud-native response. Its understanding of ‘normal’ for every user and device allows it to enforce ‘normal’ – cutting out only the malicious activity, while allowing normal business to continue functioning.

How this response will take place will depend on where Darktrace is deployed in your environment. In the network, it might mean blocking specific, anomalous connections over a certain port. In the cloud, it could mean detaching EC2 instances and applying security groups to contain only assets at risk. In email, this could be locking links or flattening attachments.

Get personal with ‘One on One’ Security

The widespread accessibility of generative AI has altered the threat landscape permanently, allowing cyber-criminals to deploy unique and personalized attacks at scale and at machine speed. In the near future, we can expect to see more novel and sophisticated phishing attacks, new automated creation of malicious code, sustained attack campaigns targeting an individual or company, and even deep fakes designed to elicit human trust.

To meet the needs of today and tomorrow, cyber security needs to leverage AI deeply and intelligently – not just using it to automate outdated historical approaches, or bolting generative AI onto existing products to keep up with the latest trend. Since 2013 Darktrace has been using AI in a fundamentally unique way: a system that learns your unique organization and understands what’s normal at a granular level. Only with this personalized understanding can you be confident in your ability as an organization to identify and shut down novel threats on the first encounter.

This form of personalized, ‘One on One’ security is a no longer a ‘nice to have’ for defenders. ‘Spray and pray’ tactics will continue to exist, but the attacks most likely to slip through the net and cause you damage are the sophisticated, the personal, and the never-before-seen. That’s what Self-Learning AI was built for – learning your business to deliver personalized cyber security, meeting every attack one-on-one.

The CISO’s Guide to Cyber AI overviews the differences between common AI approaches in cyber security and offers a high-level checklist for choosing the ideal solution for stopping attacks — including new novel threats.  To learn more about making the smartest use of AI to stop novel and targeted cloud attacks, download the guide today.

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
The Darktrace Community

More in this series

No items found.

Blog

/

Network

/

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/

Continue reading
About the author
Tyler Rhea
Senior Cyber Analyst

Blog

/

Compliance

/

November 26, 2025

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

Default blog imageDefault blog image

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.

[related-resource]

Continue reading
About the author
The Darktrace Community
Your data. Our AI.
Elevate your network security with Darktrace AI