Bytesize Security: A Guide to HTML Phishing Attachments
Darktrace guides you through the common signs of HTML phishing attachments, including common phishing emails, clever impersonations, fake webpages, and more.
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.
One of the most common types of phishing email seen by the Darktrace SOC, involves the use of HTML attachments (Figure 1). These emails make use of an attachment to hide redirects to overtly malicious or suspicious domains. Some even impersonate legitimate web pages and send any entered or captured information back to the attacker's infrastructure once opened or filled out by the recipient. Indicators of these attempts can be identified from a few key patterns found across multiple emails.
Figure 1: An example of a suspicious HTML attachment containing dynamic content
A typical feature of these HTML attachments is the use of a generic-sounding filename that relates to the message's subject line, but with no specific information pertaining to the recipient or their line of business. These files almost always contain some form of Javascript code, as they often make use of external Javascript libraries to accomplish whatever goal is being pursued. For example, an attacker might use Javascript to convincingly impersonate a trustworthy website and trick the recipient into providing credentials or sensitive information, or they might use it to deploy malware and get a foothold on the device for further compromise once opened. This can be further identified by the presence of certain links in the HTML file itself (Figure 2).
Figure 2: The HTML file previously referenced contained multiple rare and suspicious links
Figure 2 above is an example of an HTML file containing multiple links with calls for .js files. This shows that the attachment contains Javascript and is making calls for external libraries for an undetermined purpose.
Another common red flag is when the file contains links to common Product or Service images from domains wholly unrelated to those services, as seen below (Figure 3).
Figure 3: An example of an unusual .png call from a rare domain. The subsequent image called is for a company with no apparent relation to the hosting domain
The examples above imply an obvious (and poor) attempt by the HTML file to impersonate a Microsoft webpage, likely a fake login page set up for credential harvesting, as the ‘Microsoft’ logo is being pulled from a domain entirely unrelated to Microsoft or any common image-hosting service.
Rather than impersonating a website directly in the file and loading resources from external sources, these HTML files will instead directly point toward a webpage that already contains these elements. This comes with its own set of pros and cons: by hosting their phishing page in a public setting, they are far more likely to be taken down, however it may be easier to appear legitimate than if they were to build it all out in the HTML file itself.
The final routine element in these types of HTML phishing emails is the mechanism by which the attacker intends to receive any successfully scammed credentials or information. If the fake webpage is entirely contained in the HTML file, this often presents as a suspicious PHP link present in the file itself (Figure 4).
Figure 4: Phishing HTMLs often include links to rare domains with PHP destinations as an indication that it will engage in some form of HTTP POST communication
PHP calls suggest that some part of the webpage is intended to submit an HTTP POST or equivalent ‘submission’ call, often present in the ‘Login’ button in these scenarios. After the victim clicks this button, the webpage sends all the form-submission items to the endpoint hosting the PHP page, which is commonly an indicator of the webserver hosting the attacker infrastructure running the phishing attack.
If the HTML file redirects to an externally hosted phishing page, identical PHP links are often found in the source code of those pages (Figure 5). This serves the same function as sending any entered credentials back to the attacker.
Figure 5: The source-code of an external-hosted phishing page, showing calls for PHP pages hosted on alternate attacker infrastructure
The process of HTML attacks is so standardized that they are commonly released in the form of easily deployable phishing kits. These can be deployed on unsuspecting compromised webservers with little to no modification, resulting in virtually identical attacks being seen year-round. WordPress seems to be a prime target for hosting such attacks, with the site owners often becoming unsuspecting victims in propagating these phishing campaigns. An unfortunate side effect of these kits being readily available is that the attackers often don't bother to set any sort of access restrictions on their phishing servers once established, which can result in their entire setup being publicly viewable with a simple link modification. One example is seen below (Figure 6).
Figure 6: The parent directory of the website hosting a suspicious PHP page was fully accessible without restriction
In this incident, the website hosting the PHP link seen earlier had a publicly accessible parent directory structure, where both the PHP file above and an additional suspicious .txt file could be seen. This .txt file appears to be where any information submitted by victims ultimately ended up written to (Figure 7).
Figure 7: The TXT file in the parent directory above appeared to contain the login information that was likely submitted to the PHP page referred to in the initial HTML attachment
Figure 7 above presents the unusual risk of not only having the victims’ credentials at the disposal of the original attacker, but also potentially exposed to any malicious actor that can get creative with a web-crawler to identify key elements of the files used by these particular phishing kits.
Fortunately, due to the standardized nature of these ready-made phishing kits, these types of attacks often conform to a series of common behaviors that Darktrace / EMAIL excels in identifying. Despite being a popular technique, it is extremely rare for attempts using this HTML attachment method to successfully get through a correct Darktrace / EMAIL deployment. Overall, this means one less risk for the end user to worry about.
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.
Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows
New capabilities help employees understand suspicious content, give administrators more control over sensitive data, and show security teams where risky workflows sit across the organization.
Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models
Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.
At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.
Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.
The problem we're solving
Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.
Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.
What we're building
Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.
The first aligns Darktrace's behavioral understanding with OpenAI models to support security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.
Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.
Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.
These capabilities are still in development, and we're excited to continue building on this work.
To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.
Botnet Behind the Camera: Mirai Katana Activity on a Video Recording Device
Key takeaways
Darktrace identified a camera device infected with the Mirai/Katana botnet in a sports-sector customer environment, showing how exposed IoT devices can become active participants in wider attack chains.
The compromise involved suspicious Wget behavior, file downloads from rare external IPs, unusual incoming HTTP connections to video recorder management interfaces, and large outbound data transfers to infrastructure associated with botnet activity.
The incident highlights the importance of extending visibility and response beyond traditional endpoints, as unmanaged or overlooked connected devices can be exploited for command-and-control, malware delivery, and data exfiltration.
Mirai and the Katana variant
Mirai is a botnet that first emerged in August 2016 and is well known for launching large-scale distributed-denial-of-service (DDoS) attacks, typically targeting exposed Internet of Things (IoT) devices. It identifies vulnerable IoT devices ,often by abusing default credentials or exposed services, and recruiting them into a remotely controlled botnet that can be used in DDoS campaigns [1].
Katana, one of the many variants that arose after Mirai’s source code was released publicly, was first observed in late 2020 and has been seen using more advanced capabilities, including custom command-and-control (C2), persistence mechanisms, and DDoS functionality [2].
In March 2026, research from the Nokia Deepfield Emergency Response Team (ERT) identified Katana as a Mirai-derived DDoS botnet targeting Android-based TV set-top boxes through exposed Android Debug Bridge (ADB) access. Observed capabilities included custom C2, runtime domain rotation, multiple DDoS methods, and an on-device compiled kernel rootkit used for persistence and stealth [3].
Darktrace’s detection of Mirai Botnet activity on a camera device
In early 2026, Darktrace identified a Network/Digital Video Recorder (NVR/DVR) on the network of a sports-sector customer that had been infected with the Mirai Katana botnet and subsequently used to exfiltrate data from the customer’s environment. Seemingly related follow-up activity was observed on the same device several months later.
In both instances, the Darktrace Security Operations Centre (SOC) alerted the customer as part of the Managed Threat Detection (MTD) service. However, as Darktrace’s Autonomous Response capability was not fully enabled on the affected device, Darktrace was unable to proactively block the suspicious activity or prevent the compromise from continuing and recurring.
The initial compromise appears to have occurred when the affected device was seen using Wget to download Linux-based Executable and Linkable Format (ELF) files from a rare external IP, 195.177.94[.]105, which had not previously been observed in the customer’s network. Further analysis downloaded file hashes identified files related to the Mirai botnet.
Figure 1: Darktrace’s Real-Time AI Analyst investigation into the unusual outbound connection where the ELF files were downloaded.
Within a few hours, Darktrace detected the device uploading close to 3GB of data to another external IP, 50.7.49[.]4:3017 (ASN AS30058 FDCSERVERS), suggesting that the activity was likely routed via a virtual private server (VPS) hosted by FDC Servers [2]. Attackers often abuse VPS infrastructure from legitimate cloud providers to blend in with legitimate traffic and evade IP reputation and geolocation-based detections.
Figure 2: Darktrace’s detection of the unusual data upload activity by the affected camera device.
Darktrace continued to observe similar data transfers to multiple rare endpoints including 171.225.223[.]53, 95.161.128[.]62, 61.7.209[.]88, 95.161.128[.]62, which have been linked to Mirai by open-source intelligence (OSINT).
Figure 3: Darktrace’s detection of spikes in unusual external data transfer activity from the camera device.
Exploitation continued
Several months later, Darktrace identified the same exfiltration pattern on the device again, this time with stronger indications of associations with Mirai Katana botnet infection.
The device received incoming HTTP connections from 129.121.114[.]124, an external IP known to be associated with the Katana botnet IP [3]. The connections targeted the ‘/dvr/cmd’ path using the root username and user agent Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246.
The ‘/dvr/cmd’ path appears to be associated with the affected device’s web management functionality. This API endpoint has historically been targeted by Mirai and other IoT botnets through the exploitation of critical command injection vulnerabilities and automated botnet exploitation [4].
Figure 4: Darktrace’s detection of HTTP connectivity from the external IP associated with Mirai Katana Botnet.
A few days later, Darktrace observed the Wget utility being used to download ELF files, including “/lil”, from the IP 129.121.114[.]124. OSINT reporting has since associated this IP address with the Mirai Katana botnet. Notably, the IP observed earlier in the year, 195.177.94[.]105, had also hosted a file named “lil”, indicating a link between the observed activity.
Over the following days, the device received a sudden spike in connections from multiple rare external endpoints, suggesting a possible successful brute force attack. Darktrace also observed the device exfiltrating just under 4GB of data to another Mirai-associated IP address, 66.92.198[.]194, over ports 3344, 954922, and 80. Finally, the device was seen uploading data to the Mirai botnet IP 5.175.249[.]53 over port138 and exhibited an increase in UDP connections to 34.18.28[.]10 over port 9068.
Following both file download events, Darktrace identified spikes in external data transfers and connection attempts to rare destinations. While Darktrace’s Threat Research team could not confirm with high confidence that this to activity was directly associated with Mirai, it may indicate that Mirai Katana includes data exfiltration functionality.
Darktrace’s threat researchers also identified an internet-facing NTP server belonging to a separate customer receiving incoming connection attempts from the same initially observed IP, 195.177.94[.]105,over the port 123. This suggests that Mirai Katana may not exclusively target IoT devices.
Conclusion
This case demonstrates how threat actors can exploit overlooked IoT and OT devices to support broader malicious objectives. Here, a camera device infected with a botnet was used to exfiltrate data from the customer's environment, showing how peripheral assets can become active participants in an attack chain.
This case also reinforces a challenge many organizations face today: extending security visibility beyond traditional endpoints and servers. Cameras, sensors, and other connected devices often operate with limited monitoring and may fall outside established security processes, despite maintaining network connectivity and access to potentially sensitive environments. This is particularly relevant in the sports sector, where growing reliance on connected cameras, smart stadium technologies, and other IoT devices continues to expand the attack surface, as highlighted in Darktrace's Sports Sector Threat Report.
As botnets like Kata and Mirai continue to evolve, defenders need visibility across unmanaged IoT and edge devices, as well as security solutions that can recognize subtle deviations in device behavior that may indicate an emerging compromise.
Credit to Parvatha Ananthakannan (Cyber Analyst), Signe Zaharka (Principal Analyst)
Edited by Ryan Traill (Content Manager)
Appendices
Darktrace Model Detections
· Anomalous File / EXE from Rare External Location
· Anomalous File / Multiple EXE from Rare External Locations
· Device / Initial Attack Chain Activity
· Unusual Activity / Unusual External Data to New Endpoint
· Anomalous Connection / Data Sent to Rare Domain
· Unusual Activity / Enhanced Unusual External Data Transfer
· Anomalous Connection / Uncommon 1 GiB Outbound
· Device / Significant UDP Increase
· Anomalous Connection / Low and Slow Exfiltration to IP
· Compromise / Large Number of Suspicious Failed Connections
· Compromise / Large Number of Suspicious Successful Connections
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