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
Jamie Woodland
Head of Technology at Community Housing Limited
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04
Mar 2024
About Community Housing Limited
Community Housing Limited is a non-profit organization based in Australia that focuses on providing affordable, long-term housing and creating employment opportunities where possible. We give people the security of having a home so that they can focus on other essential pathways. As such, we are responsible for sensitive information on our clients.
As part of our commitment to strengthening our cyber security, we sought to simplify and unify our incident response plans and equip our engineers and desktop support teams with all the information we need at our fingertips.
Why Community Housing Limited chose Darktrace
Our team hoped to achieve a response procedure that allowed us to have oversight over any potential security risks, even cases that don’t overtly seem like a security risk. For example, an incident could start as a payroll issue and end up in the hands of HR, instead of surfacing as a security problem. In this case, our security team has no way of knowing the real number of events or how the threat had actually started and played out, making incident response and mitigation even more challenging.
AI-generated playbooks save time during incident response
I wanted to reduce the time and resources it took our security team to appropriately respond to a threat. Darktrace automates several steps of the recovery process to accelerate the rate of incident response by using AI that learns the granular details of the specific organization, building a dynamic understanding of the devices, connections, and user behaviors that make up the normal “pattern of life.”
The AI then uses this understanding to create bespoke, AI-generated incident response playbooks that leverage an evolving understanding of our organization to determine recovery steps that are tailored not only to the specific incident but also to our unique environment.
For my security team, this means having access to all the information we need to respond to a threat. When running through an incident, rather than going to different places to synthesize relevant information, which takes up valuable resources and time, we can speed up its remediation with Darktrace.
The playbooks created by Darktrace help lower the technical skills required to respond to incidents by elevating the workload of the staff, tripling our capacity for incident response.
Realistic attack simulations upskill teams while saving resources
We have differing levels of experience on the team which means some members know exactly what to do during incident response while others are slower and need more guidance. Thus, we have to either outsource skilled security professionals or add a security solution that could lower the technical skills bar.
You don’t want to be second guessing and searching for the right move – it’s urgent – there should be certainty. Our goal with running attack simulations is to test and train our team's response capabilities in a “realistic” scenario. But this takes considerable time to plan and execute or can be expensive if outsourced, which can be a challenge for organizations short on resources.
Darktrace provides AI-assisted incident response and cyber-attack simulation using AI that understands the organization to run simulations that effectively map onto the real digital environment and the assets within it, providing training for actual incidents.
It is one thing to sit together in a meeting and discuss various outcomes of a cyber-attack, talking through the best response strategies. It is a huge benefit being able to run attack simulations that emulate real-world scenarios.
Our team can now see how an incident would play out over several days to resemble a real-world scenario or it can play through the simulation quickly to ascertain outcomes immediately. It then uses these insights to strengthen its technology, processes, and training.
AI-Powered Incident Response
Darktrace helps my security team save resources and upskill staff using AI to generate bespoke playbooks and run realistic simulations. Its real-time understanding of our business ensures incident preparedness and incident response are tailored to not only the specific threat in question, but also to the contextual infrastructure of the organization.
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.
Patch Smarter, Not Harder: Now Empowering Security Teams with Business-Aligned Threat Context Agents
This blog introduces new innovations in Darktrace / Proactive Exposure Management that bring precision and clarity to vulnerability prioritization. Learn how No-Telemetry Endpoints provide real device context without network data and how new Cost-Benefit Analysis capabilities quantify patching ROI—helping teams cut noise, act faster, and strengthen proactive risk management.
Darktrace Announces Extended Visibility Between Confirmed Assets and Leaked Credentials from the Deep and Dark Web
This blog explores how Continuous Threat Exposure Management (CTEM) is reshaping defense strategies and how new Darktrace / Attack Surface Management capabilities, including Exploit Prediction Assessment and Deep & Dark Web Monitoring, help organizations turn CTEM from strategy into action.
From Amazon to Louis Vuitton: How Darktrace Detects Black Friday Phishing Attacks
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)
CastleLoader & CastleRAT: Behind TAG150’s Modular Malware Delivery System
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 Step” prompt, 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
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].
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.
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)