Blog
/
/
April 16, 2025

AI Uncovered: Introducing Darktrace Incident Graph Evaluation for Security Threats (DIGEST)

Discover how Darktrace’s new DIGEST model enhances Cyber AI Analyst by using GNNs and RNNs to score and prioritize threats with expert-level precision before damage is done.
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
Margaret Cunningham, PhD
VP, Security & AI Strategy, Field CISO
man looking at computer screenDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
16
Apr 2025

DIGEST advances how Cyber AI Analyst scores and prioritizes incidents. Trained on over a million anonymized incident graphs, our model brings deeper context to severity scoring by analyzing how threats are structured and how they evolve. DIGEST assesses threats as an expert, before damage is done. For more details beyond this overview, please read our Technical Research Paper.

Darktrace combines machine learning (ML) and artificial intelligence (AI) approaches using a multi-layered, multi-method approach. The result is an AI system that continuously ingests data from across an organization’s environment, learns from it, and adapts in real time. DIGEST adds a new layer to this system, specifically to our Cyber AI Analyst, the first and most experienced AI Analyst in cybersecurity, dedicated to refining how incidents are scored and prioritized. DIGEST improves what your team uses to focus on what matters the most first.

To build DIGEST, we combined Graph Neural Networks (GNNs) to interpret incident structure with Recurrent Neural Networks (RNNs) to analyze how incidents evolve over time. This pairing allows DIGEST to reliably determine the potential severity of an incident even at an early stage to give the Cyber AI Analyst a critical edge in identifying high-risk threats early and recognizing when activity is unlikely to escalate.

DIGEST works locally in real-time regardless of whether your Darktrace deployment is on prem or in the cloud, without requiring data to be sent externally for decisions to be made. It was built to support teams in all environments, including those with strict data controls and limited connectivity.

Our approach to AI is unique, drawing inspiration from multiple disciplines to tackle the toughest cybersecurity challenges. DIGEST demonstrates how a novel application of GNNs and RNNs improves the prioritization and triage of security incidents. By blending interdisciplinary expertise with innovative AI techniques, we are able to push the boundaries of what’s possible and deliver it where it is needed most. We are eager to share our findings to accelerate progress throughout the broader field of AI development.

DIGEST: Pattern, progression, and prioritization

Most security incidents start quietly. A device contacting an unusual domain. Credentials are used at unexpected hours. File access patterns shift. The fundamental challenge is not always detecting these anomalies but knowing what to address first. DIGEST gives us this capability.

To understand DIGEST, it helps to start with Cyber AI Analyst, a critical component of our Self-Learning AI system and a front-line triage partner in security investigations. It combines supervised and unsupervised machine learning (ML) techniques, natural language processing (NLP), and graph-based reasoning to investigate and summarize security incidents.

DIGEST was built as an additional layer of analysis within Cyber AI Analyst. It enhances its capabilities by refining how incidents are scored and prioritized, helping teams focus on what matters most more quickly. For a general view of the ML and AI methods that power Darktrace products, read our AI Arsenal whitepaper. This paper provides insights regarding the various approaches we use to detect, investigate, and prioritize threats.

Cyber AI Analyst is constantly investigating alerts and produces millions of critical incidents every year. The dynamic graphs produced by Cyber AI Analyst investigations represent an abstract understanding of security incidents that is fully anonymized and privacy preserving. This allowed us to use the Call Home and aianalyst.darktrace.com services to produce a dataset comprising the broad structure of millions of incidents that Cyber AI analyst detected on customer deployments, without containing any sensitive data. (Read our technical research paper for more details about our dataset).

The dynamic graphs from Cyber AI Analyst capture the structure of security incidents where nodes represent entities like users, devices or resources, and edges represent the multitude of relationships between them. As new activity is observed, the graph expands, capturing the progression of incidents over time. Our dataset contained everything from benign administrative behavior to full-scale ransomware attacks.

Unique data, unmatched insights

Key terms

Graph Neural Networks (GNNs): A type of neural network designed to analyze and interpret data structured as graphs, capturing relationships between nodes.

Recurrent Neural Networks (RNNs): A type of neural network designed to model sequences where the order of events matters, like how activity unfolds in a security incident.

The Cyber AI Analyst dataset used to train DIGEST reflects over a decade of work in AI paired with unmatched expertise in cybersecurity. Prior to training DIGEST on our incident graph data set, we performed rigorous data preprocessing to ensure to remove issues such as duplicate or ill-formed incidents. Additionally, to validate DIGEST’s outputs, expert security analysts assessed and verified the model’s scoring.

Transforming data into insights requires using the right strategies and techniques. Given the graphical nature of Cyber AI Analyst incident data, we used GNNs and RNNs to train DIGEST to understand incidents and how they are likely to change over time. Change does not always mean escalation. DIGEST’s enhanced scoring also keeps potentially legitimate or low-severity activity from being prioritized over threats that are more likely to get worse. At the beginning, all incidents might look the same to a person. To DIGEST, it looks like the beginning of a pattern.

As a result, DIGEST enhances our understanding of security incidents by evaluating the structure of the incident, probable next steps in an incident’s trajectory, and how likely it is to grow into a larger event.

To illustrate these capabilities in action, we are sharing two examples of DIGEST’s scoring adjustments from use cases within our customers’ environments.

First, Figure 1 shows the graphical representation of a ransomware attack, and Figure 2 shows how DIGEST scored incident progression of that ransomware attack. At hour two, DIGEST’s score escalated to 95% well before observation of data encryption. This means that prior to seeing malicious encryption behaviors, DIGEST understood the structure of the incident and flagged these early activities as high-likelihood precursors to a severe event. Early detection, especially when flagged prior to malicious encryption behaviors, gives security teams a valuable head start and can minimize the overall impact of the threat, Darktrace Autonomous Response can also be enabled by Cyber AI Analyst to initiate an immediate action to stop the progression, allowing the human security team time to investigate and implement next steps.

Graph representation of a ransomware attack
Figure 1: Graph representation of a ransomware attack
Timeline of DIGEST incident score escalation. Note that timestep does not equate to hours, the spike in score to 95% occurred approximately 2 hours into the attack, prior to data encryption.
Figure 2:  Timeline of DIGEST incident score escalation. Note that timestep does not equate to hours, the spike in score to 95% occurred approximately 2 hours into the attack, prior to data encryption.

In contrast, our second example shown in Figure 3 and Figure 4 illustrates how DIGEST’s analysis of an incident can help teams avoid wasting time on lower risk scenarios. In this instance, Figure 3 illustrates a graph of unusual administrative activity, where we observed connection to a large group of devices. However, the incident score remained low because DIGEST determined that high risk malicious activity was unlikely. This determination was based on what DIGEST observed in the incident's structure, what it assessed as the probable next steps in the incident lifecycle and how likely it was to grow into a larger adverse event.

Graph representation of unusual admin activity connecting to a large group of devices.
Figure 3: Graph representation of unusual admin activity connecting to a large group of devices.
Timeline of DIGEST incident scoring, where the score remained low as the unusual event was determined to be low risk.
Figure 4: Timeline of DIGEST incident scoring, where the score remained low as the unusual event was determined to be low risk.

These examples show the value of enhanced scoring. DIGEST helps teams act sooner on the threats that count and spend less time chasing the ones that do not.

The next phase of advanced detection is here

Darktrace understands what incidents look like. We have seen, investigated, and learned from them at scale, including over 90 million investigations in 2024. With DIGEST, we can share our deep understanding of incidents and their behaviors with you and triage these incidents using Cyber AI Analyst.

Our ability to innovate in this space is grounded in the maturity of our team and the experiences we have built upon in over a decade of building AI solutions for cybersecurity. This experience, along with our depth of understanding of our data, techniques, and strategic layering of AI/ML components has shaped every one of our steps forward.

With DIGEST, we are entering a new phase, with another line of defense that helps teams prioritize and reason over incidents and threats far earlier in an incident’s lifecycle. DIGEST understands your incidents when they start, making it easier for your team to act quickly and confidently.

DIGEST is available in Darktrace 6.3, along with a new embedding model – DEMIST-2 – designed to provide reliable, high-accuracy detections for critical security use cases.

[related-resource]

Want to learn more?

If you are curious about the details of DIGEST’s dataset, model design, training, experiments, and model deployment, read our technical brief.

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
Margaret Cunningham, PhD
VP, Security & AI Strategy, Field CISO

More in this series

No items found.

Blog

/

Email

/

December 1, 2025

From Amazon to Louis Vuitton: How Darktrace Detects Black Friday Phishing Attacks

Default blog imageDefault blog image

Why Black Friday Drives a Surge in Phishing Attacks

In recent years, Black Friday has shifted from a single day of online retail sales and discounts to an extended ‘Black Friday Week’, often preceded by weeks of online hype. During this period, consumers are inundated with promotional emails and marketing campaigns as legitimate retailers compete for attention.

Unsurprisingly, this surge in legitimate communications creates an ideal environment for threat actors to launch targeted phishing campaigns designed to mimic legitimate retail emails. These campaigns often employ social engineering techniques that exploit urgency, exclusivity, and consumer trust in well-known brands, tactics designed to entice recipients into opening emails and clicking on malicious links.

Additionally, given the seasonal nature of Black Friday and the ever-changing habits of consumers, attackers adopt new tactics and register fresh domains each year, rather than reusing domains previously flagged as spam or phishing endpoints. While this may pose a challenge for traditional email security tools, it presents no such difficulty for Darktrace / EMAIL and its anomaly-based approach.

In the days and weeks leading up to ‘Black Friday’, Darktrace observed a spike in sophisticated phishing campaigns targeting consumers, demonstrating how attackers combine phycological manipulation with technical evasion to bypass basic security checks during this high-traffic period. This blog showcases several notable examples of highly convincing phishing emails detected and contained by Darktrace / EMAIL in mid to late November 2025.

Darktrace’s Black Friday Detections

Brand Impersonation: Deal Watchdogs’ Amazon Deals

The impersonation major online retailers has become a common tactic in retail-focused attacks, none more so than Amazon, which ranked as the fourth most impersonated brand in 2024, only behind Microsoft, Apple, Google, and Facebook [1]. Darktrace’s own research found Amazon to be the most mimicked brand, making up 80% of phishing attacks in its analysis of global consumer brands.

When faced with an email that appears to come from a trusted sender like Amazon, recipients are far more likely to engage, increasing the success rate of these phishing campaigns.

In one case observed on November 16, Darktrace detected an email with the subject line “NOW LIVE: Amazon’s Best Early Black Friday Deals on Gadgets Under $60”. The email was sent to a customer by the sender ‘Deal Watchdogs’, in what appeared to be an attempt to masquerade as a legitimate discount-finding platform. No evidence indicated that the company was legitimate. In fact, the threat actor made no attempt to create a convincing name, and the domain appeared to be generated by a domain generation algorithm (DGA), as shown in Figure 2.

Although the email was sent by ‘Deal Watchdogs’, it attempted to impersonate Amazon by featuring realistic branding, including the Amazon logo and a shade of orange similar to that used by them for the ‘CLICK HERE’ button and headline text.

Figure 1: The contents of the email observed by Darktrace, featuring authentic-looking Amazon branding.

Darktrace identified that the email, marked as urgent by the sender, contained a suspicious link to a Google storage endpoint (storage.googleapis[.]com), which had been hidden by the text “CLICK HERE”. If clicked, the link could have led to a credential harvester or served as a delivery vector for a malicious payload hosted on the Google storage platform.

Fortunately, Darktrace immediately identified the suspicious nature of this email and held it before delivery, preventing recipients from ever receiving or interacting with the malicious content.

Figure 2: Darktrace / EMAIL’s detection of the malicious phishing email sent to a customer.

Around the same time, Darktrace detected a similar email attempting to spoof Amazon on another customer’s network with the subject line “Our 10 Favorite Deals on Amazon That Started Today”, also sent by ‘Deal Watchdogs,’ suggesting a broader campaign.

Analysis revealed that this email originated from the domain petplatz[.]com, a fake marketing domain previously linked to spam activity according to open-source intelligence (OSINT) [2].

Brand Impersonation: Louis Vuitton

A few days later, on November 20, Darktrace / EMAIL detected a phishing email attempting to impersonate the luxury fashion brand Louis Vuitton. At first glance, the email, sent under the name ‘Louis Vuitton’ and titled “[Black Friday 2025] Discover Your New Favorite Louis Vuitton Bag – Elegance Starts Here”, appeared to be a legitimate Black Friday promotion. However, Darktrace’s analysis uncovered several red flags indicating a elaborate brand impersonation attempt.

The email was not sent by Louis Vuitton but by rskkqxyu@bookaaatop[.]ru, a Russia-based domain never before observed on the customer’s network. Darktrace flagged this as suspicious, noting that .ru domains were highly unusual for this recipient’s environment, further reinforcing the likelihood of malicious intent. Subsequent analysis revealed that the domain had only recently registered and was flagged as malicious by multiple OSINT sources [3].

Figure 3: Darktrace / EMAIL’s detection of the malicious email attempting to spoofLouis Vuitton, originating from a suspicious Russia-based domain.

Darktrace further noted that the email contained a highly suspicious link hidden behind the text “View Collection” and “Unsubscribe,” ensuring that any interaction, whether visiting the supposed ‘handbag store’ or attempting to opt out of marketing emails, would direct recipients to the same endpoint. The link resolved to xn--80aaae9btead2a[.]xn--p1ai (топааабоок[.]рф), a domain confirmed as malicious by multiple OSINT sources [4]. At the time of analysis, the domain was inaccessible, likely due to takedown efforts or the short-lived nature of the campaign.

Darktrace / EMAIL blocked this email before it reached customer inboxes, preventing recipients from interacting with the malicious content and averting any disruption.

Figure 4: The suspicious domain linked in the Louis Vuitton phishing email, now defunct.

Too good to be true?

Aside from spoofing well-known brands, threat actors frequently lure consumers with “too good to be true” luxury offers, a trend Darktrace observed in multiple cases throughout November.

In one instance, Darktrace identified an email with the subject line “[Black Friday 2025] Luxury Watches Starting at $250.” Emails contained a malicious phishing link, hidden behind text like “Rolex Starting from $250”, “Shop Now”, and “Unsubscribe”.

Figure 5: Example of a phishing email detected by Darktrace, containing malicious links concealed behind seemingly innocuous text.

Similarly to the Louis Vuitton email campaign described above, this malicious link led to a .ru domain (hxxps://x.wwwtopsalebooks[.]ru/.../d65fg4er[.]html), which had been flagged as malicious by multiple sources [5].

Figure 6: Darktrace / EMAIL’s detection of a malicious email promoting a fake luxury watch store, which was successfully held from recipient inboxes.

If accessed, this domain would redirect users to luxy-rox[.]com, a recently created domain (15 days old at the time of writing) that has also been flagged as malicious by OSINT sources [6]. When visited, the redirect domain displayed a convincing storefront advertising high-end watches at heavily discounted prices.

Figure 7: The fake storefront presented upon visiting the redirectdomain, luxy-rox[.]com.

Although the true intent of this domain could not be confirmed, it was likely a scam site or a credential-harvesting operation, as users were required to create an account to complete a purchase. As of the time or writing, the domain in no longer accessible .

This email illustrates a layered evasion tactic: attackers employed multiple domains, rapid domain registration, and concealed redirects to bypass detection. By leveraging luxury branding and urgency-driven discounts, the campaign sought to exploit seasonal shopping behaviors and entice victims into clicking.

Staying Protected During Seasonal Retail Scams

The investigation into these Black Friday-themed phishing emails highlights a clear trend: attackers are exploiting seasonal shopping events with highly convincing campaigns. Common tactics observed include brand impersonation (Amazon, Louis Vuitton, luxury watch brands), urgency-driven subject lines, and hidden malicious links often hosted on newly registered domains or cloud services.

These campaigns frequently use redirect chains, short-lived infrastructure, and psychological hooks like exclusivity and luxury appeal to bypass user scepticism and security filters. Organizations should remain vigilant during retail-heavy periods, reinforcing user awareness training, link inspection practices, and anomaly-based detection to mitigate these evolving threats.

Credit to Ryan Traill (Analyst Content Lead) and Owen Finn (Cyber Analyst)

Appendices

References

1.        https://keepnetlabs.com/blog/top-5-most-spoofed-brands-in-2024

2.        https://www.virustotal.com/gui/domain/petplatz.com

3.        https://www.virustotal.com/gui/domain/bookaaatop.ru

4.        https://www.virustotal.com/gui/domain/xn--80aaae9btead2a.xn--p1ai

5.        https://www.virustotal.com/gui/url/e2b868a74531cd779d8f4a0e1e610ec7f4efae7c29d8b8ab32c7a6740d770897?nocache=1

6.        https://www.virustotal.com/gui/domain/luxy-rox.com

Indicators of Compromise (IoCs)

IoC – Type – Description + Confidence

petplatz[.]com – Hostname – Spam domain

bookaaatop[.]ru – Hostname – Malicious Domain

xn--80aaae9btead2a[.]xn--p1ai (топааабоок[.]рф) – Hostname - Malicious Domain

hxxps://x.wwwtopsalebooks[.]ru/.../d65fg4er[.]html) – URL – Malicious Domain

luxy-rox[.]com – Hostname -  Malicious Domain

MITRE ATT&CK Mapping  

Tactic – Technique – Sub-Technique  

Initial Access - Phishing – (T1566)  

Continue reading
About the author
Ryan Traill
Analyst Content Lead

Blog

/

Network

/

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/

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