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April 2, 2024

Darktrace's Investigation of Raspberry Robin Worm

Discover how Darktrace is leading the hunt for Raspberry Robin. Explore early insights and strategies in the battle against cyber threats.
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
Alexandra Sentenac
Cyber Analyst
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02
Apr 2024

Introduction

In the face of increasingly hardened digital infrastructures and skilled security teams, malicious actors are forced to constantly adapt their attack methods, resulting in sophisticated attacks that are designed to evade human detection and bypass traditional network security measures.  

One such example that was recently investigated by Darktrace is Raspberry Robin, a highly evasive worm malware renowned for merging existing and novel techniques, as well as leveraging both physical hardware and software, to establish a foothold within organization’s networks and propagate additional malicious payloads.

What is Raspberry Robin?

Raspberry Robin, also known as ‘QNAP worm’, is a worm malware that was initially discovered at the end of 2023 [1], however, its debut in the threat landscape may have predated this, with Microsoft uncovering malicious artifacts linked to this threat (which it tracks under the name Storm-0856) dating back to 2019 [4]. At the time, little was known regarding Raspberry Robin’s objectives or operators, despite the large number of successful infections worldwide. While the identity of the actors behind Raspberry Robin still remains a mystery, more intelligence has been gathered about the malware and its end goals as it was observed delivering payloads from different malware families.

Who does Raspberry Robin target?

While it was initially reported that Raspberry Robin primarily targeted the technology and manufacturing industries, researchers discovered that the malware had actually targeted multiple sectors [3] [4]. Darktrace’s own investigations echoed this, with Raspberry Robin infections observed across various industries, including public administration, finance, manufacturing, retail education and transportation.

How does Raspberry Robin work?

Initially, it appeared that Raspberry Robin's access to compromised networks had not been utilized to deliver final-stage malware payloads, nor to steal corporate data. This uncertainty led researchers to question whether the actors involved were merely “cybercriminals playing around” or more serious threats [3]. This lack of additional exploitation was indeed peculiar, considering that attackers could easily escalate their attacks, given Raspberry Robin’s ability to bypass User Account Control using legitimate Windows tools [4].

However, at the end of July 2022, some clarity emerged regarding the operators' end goals. Microsoft researchers revealed that the access provided by Raspberry Robin was being utilized by an access broker tracked as DEV-0206 to distribute the FakeUpdates malware downloader [2]. Researchers further discovered malicious activity associated with Evil Corp TTPs (i.e., DEV-0243) [5] and payloads from the Fauppod malware family leveraging Raspberry Robin’s access [8]. This indicates that Raspberry Robin may, in fact, be an initial access broker, utilizing its presence on hundreds of infected networks to distribute additional payloads for paying malware operators. Thus far, Raspberry Robin has been observed distributing payloads linked to FIN11, Clop Gang, BumbleBee, IcedID, and TrueBot on compromised networks [12].

Raspberry Robin’s Continued Evolution

Since it first appeared in the wild, Raspberry Robin has evolved from "being a widely distributed worm with no observed post-infection actions [...] to one of the largest malware distribution platforms currently active" [8]. The fact that Raspberry Robin has become such a prevalent threat is likely due to the continual addition of new features and evasion capabilities to their malware [6] [7].  

Since its emergence, the malware has “changed its communication method and lateral movement” [6] in order to evade signature detections based on threat intelligence and previous versions. Endpoint security vendors commonly describe it as heavily obfuscated malware, employing multiple layers of evasion techniques to hinder detection and analysis. These include for example dropping a fake payload when analyzed in a sandboxed environment and using mixed-case executing commands, likely to avoid case-sensitive string-based detections.  

In more recent campaigns, Raspberry Robin further appears to have added a new distribution method as it was observed being downloaded from archive files sent as attachments using the messaging service Discord [11]. These attachments contained a legitimate and signed Windows executable, often abused by attackers for side-loading, alongside a malicious dynamic-link library (DLL) containing a Raspberry Robin sample.

Another reason for the recent success of the malware may be found in its use of one-day exploits. According to researchers, Raspberry Robin now utilizes several local privilege escalation exploits that had been recently disclosed, even before a proof of concept had been made available [9] [10]. This led cyber security professionals to believe that operators of the malware may have access to an exploit seller [6]. The use of these exploits enhances Raspberry Robin's detection evasion and persistence capabilities, enabling it to propagate on networks undetected.

Darktrace’s Coverage of Raspberry Robin

Through two separate investigations carried out by Darktrace’s Threat Research team, first in late 2022 and then in November 2023, it became evident that Raspberry Robin was capable of integrating new functionalities and tactics, techniques and procedures (TTPs) into its attacks. Darktrace DETECT™ provided full visibility over the evolving campaign activity, allowing for a comparison of the threat across both investigations. Additionally, if Darktrace RESPOND™ was enabled on affected networks, it was able to quickly mitigate and contain emerging activity during the initial stages, thwarting the further escalation of attacks.

Raspberry Robin Initial Infection

The most prevalent initial infection vector appears to be the introduction of an infected external drive, such as a USB stick, containing a malicious .LNK file (i.e., a Windows shortcut file) disguised as a thumb drive or network share. When clicked, the LNK file automatically launches cmd.exe to execute the malicious file stored on the external drive, and msiexec.exe to connect to a Raspberry Robin command-and-control (C2) endpoint and download the main malware component. The whole process leverages legitimate Windows processes and is therefore less likely to raise any alarms from more traditional security solutions. However, Darktrace DETECT was able to identify the use of Msiexec to connect to a rare endpoint as anomalous in every case investigated.

Little is currently known regarding how the external drives are infected and distributed, but it has been reported that affected USB drives had previously been used for printing at printing and copying shops, suggesting that the infection may have originated from such stores [13].

A method as simple as leaving an infected USB on a desk in a public location can be a highly effective social engineering tactic for attackers. Exploiting both curiosity and goodwill, unsuspecting individuals may innocently plug in a found USB, hoping to identify its owner, unaware that they have unwittingly compromised their device.

As Darktrace primarily operates on the network layer, the insertion of a USB endpoint device would not be within its visibility. Nevertheless, Darktrace did observe several instances wherein multiple Microsoft endpoints were contacted by compromised devices prior to the first connection to a Raspberry Robin domain. For example, connections to the URI '/fwlink/?LinkID=252669&clcid=0x409' were observed in multiple customer environments prior to the first Raspberry Robin external connection. This connectivity seems to be related to Windows attempting to retrieve information about installed hardware, such as a printer, and could also be related to the inserting of an external USB drive.

Figure 1: Device Event Log showing an affected device making connections to Microsoft endpoints, prior to contacting the Raspberry Robin C2 endpoint ‘vqdn[.]net’.
Figure 1: Device Event Log showing an affected device making connections to Microsoft endpoints, prior to contacting the Raspberry Robin C2 endpoint ‘vqdn[.]net’.

Raspberry Robin Command-and-Control Activity

In all cases investigated by Darktrace, compromised devices were detected making HTTP GET connections via the unusual port 8080 to Raspberry Robin C2 endpoints using the new user agent 'Windows Installer'.

The C2 hostnames observed were typically short and matched the regex /[a-zA-Z0-9]{2,4}.[a-zA-Z0-9]{2,6}/, and were hosted on various top-level domains (TLD) such as ‘.rocks’, ‘.pm’, and ‘.wf’. On one customer network, Darktrace observed the download of an MSI file from the Raspberry Robin domain ‘wak[.]rocks’. This package contained a heavily protected malicious DLL file whose purpose was unknown at the time.  

However, in September 2022, external researchers revealed that the main purpose of this DLL was to download further payloads and enable lateral movement, persistence and privilege escalation on compromised devices, as well as exfiltrating sensitive information about the device. As worm infections spread through networks automatically, exfiltrating device data is an essential process for threat actor to keep track of which systems have been infected.

On affected networks investigated by Darktrace, compromised devices were observed making C2 connections that contained sensitive device information, including hostnames and credentials, with additional host information likely found within the data packets [12].

Figure 2: Model Breach Event Log displaying the events that triggered the the ‘New User Agent and Suspicious Request Data’ DETECT model breach.
Figure 2: Model Breach Event Log displaying the events that triggered the the ‘New User Agent and Suspicious Request Data’ DETECT model breach.

As for C2 infrastructure, Raspberry Robin leverages compromised Internet of Things (IoT) devices such as QNAP network attached storage (NAS) systems with hijacked DNS settings [13]. NAS devices are data storage servers that provide access to the files they store from anywhere in the world. These features have been abused by Raspberry Robin operators to distribute their malicious payloads, as any uploaded file could be stored and shared easily using NAS features.

However, Darktrace found that QNAP servers are not the only devices being exploited by Raspberry Robin, with DETECT identifying other IoT devices being used as C2 infrastructure, including a Cerio wireless access point in one example. Darktrace recognized that this connection was new to the environment and deemed it as suspicious, especially as it also used new software and an unusual port for the HTTP protocol (i.e., 8080 rather than 80).

In several instances, Darktrace observed Raspberry Robin utilizing TOR exit notes as backup C2 infrastructure, with compromised devices detected connecting to TOR endpoints.

Figure 3: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 3: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 4: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 4: Raspberry Robin C2 endpoint when viewed in a sandbox environment.

Raspberry Robin in 2022 vs 2023

Despite the numerous updates and advancements made to Raspberry Robin between the investigations carried out in 2022 and 2023, Darktrace’s detection of the malware was largely the same.

DETECT models breached during first investigation at the end of 2022:

  • Device / New User Agent
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Device / New User Agent and New IP
  • Compromise / Suspicious Request Data
  • Compromise / Uncommon Tor Usage
  • Possible Tor Usage

DETECT models breached during second investigation in late 2023:

  • Device / New User Agent and New IP
  • Device / New User Agent and Suspicious Request Data
  • Device / New User Agent
  • Device / Suspicious Domain
  • Possible Tor Usage

Darktrace’s anomaly-based approach to threat detection enabled it to consistently detect the TTPs and IoCs associated with Raspberry Robin across the two investigations, despite the operator’s efforts to make it stealthier and more difficult to analyze.

In the first investigation in late 2022, Darktrace detected affected devices downloading addition executable (.exe) files following connections to the Raspberry Robin C2 endpoint, including a numeric executable file that appeared to be associated with the Vidar information stealer. Considering the advanced evasion techniques and privilege escalation capabilities of Raspberry Robin, early detection is key to prevent the malware from downloading additional malicious payloads.

In one affected customer environment investigated in late 2023, a total of 12 devices were compromised between mid-September and the end of October. As this particular customer did not have Darktrace RESPOND, the Raspberry Robin infection was able to spread through the network unabated until the customer acted upon Darktrace DETECT’s alerts.

Had Darktrace RESPOND been enabled in autonomous response mode, it would have been able to take immediate action following the first observed connection to a Raspberry Robin C2 endpoint, by blocking connections to the suspicious endpoint and enforcing a device’s normal ‘pattern of life’.

By enforcing a pattern of life on an affected device, RESPOND would prevent it from carrying out any activity that deviates from this learned pattern, including connections to new endpoints using new software as was the case in Figure 5, effectively shutting down the attack in the first instance.

Model Breach Event Log showing RESPOND’s actions against connections to Raspberry Robin C2 endpoints.
Figure 5: Model Breach Event Log showing RESPOND’s actions against connections to Raspberry Robin C2 endpoints.

Conclusion

Raspberry Robin is a highly evasive and adaptable worm known to evolve and change its TTPs on a regular basis in order to remain undetected on target networks for as long as possible. Due to its ability to drop additional malware variants onto compromised devices, it is crucial for organizations and their security teams to detect Raspberry Robin infections at the earliest possible stage to prevent the deployment of potentially disruptive secondary attacks.

Despite its continued evolution, Darktrace's detection of Raspberry Robin remained largely unchanged across the two investigations. Rather than relying on previous IoCs or leveraging existing threat intelligence, Darktrace DETECT’s anomaly-based approach allows it to identify emerging compromises by detecting the subtle deviations in a device’s learned behavior that would typically come with a malware compromise.

By detecting the attacks at an early stage, Darktrace gave its customers full visibility over malicious activity occurring on their networks, empowering them to identify affected devices and remove them from their environments. In cases where Darktrace RESPOND was active, it would have been able to take autonomous follow-up action to halt any C2 communication and prevent the download of any additional malicious payloads.  

Credit to Alexandra Sentenac, Cyber Analyst, Trent Kessler, Senior Cyber Analyst, Victoria Baldie, Director of Incident Management

Appendices

Darktrace DETECT Model Coverage

Device / New User Agent and New IP

Device / New User Agent and Suspicious Request Data

Device / New User Agent

Compromise / Possible Tor Usage

Compromise / Uncommon Tor Usage

MITRE ATT&CK Mapping

Tactic - Technique

Command & Control - T1090.003 Multi-hop Proxy

Lateral Movement - T1210 Exploitation of remote services

Exfiltration over C2 Data - T1041 Exfiltration over C2 Channel

Data Obfuscation - T1001 Data Obfuscation

Vulnerability Scanning - T1595.002 Vulnerability Scanning

Non-Standard Port - T1571 Non-Standard Port

Persistence - T1176 Browser Extensions

Initial Access - T1189 Drive By Compromise / T1566.002  Spearphishing Link

Collection - T1185 Man in the browser

List of IoCs

IoC - Type - Description + Confidence

vqdn[.]net - Hostname - C2 Server

mwgq[.]net - Hostname - C2 Server

wak[.]rocks - Hostname - C2 Server

o7car[.]com - Hostname - C2 Server

6t[.]nz - Hostname - C2 Server

fcgz[.]net - Hostname - Possible C2 Server

d0[.]wf - Hostname - C2 Server

e0[.]wf - Hostname - C2 Server

c4z[.]pl - Hostname - C2 Server

5g7[.]at - Hostname - C2 Server

5ap[.]nl - Hostname - C2 Server

4aw[.]ro - Hostname - C2 Server

0j[.]wf - Hostname - C2 Server

f0[.]tel - Hostname - C2 Server

h0[.]pm - Hostname - C2 Server

y0[.]pm - Hostname - C2 Server

5qy[.]ro - Hostname - C2 Server

g3[.]rs - Hostname - C2 Server

5qe8[.]com - Hostname - C2 Server

4j[.]pm - Hostname - C2 Server

m0[.]yt - Hostname - C2 Server

zk4[.]me - Hostname - C2 Server

59.15.11[.]49 - IP address - Likely C2 Server

82.124.243[.]57 - IP address - C2 Server

114.32.120[.]11 - IP address - Likely C2 Server

203.186.28[.]189 - IP address - Likely C2 Server

70.124.238[.]72 - IP address - C2 Server

73.6.9[.]83 - IP address - Likely C2 Server

References

[1] https://redcanary.com/blog/raspberry-robin/  

[2] https://www.bleepingcomputer.com/news/security/microsoft-links-raspberry-robin-malware-to-evil-corp-attacks/

[3] https://7095517.fs1.hubspotusercontent-na1.net/hubfs/7095517/FLINT%202022-016%20-%20QNAP%20worm_%20who%20benefits%20from%20crime%20(1).pdf

[4] https://www.bleepingcomputer.com/news/security/microsoft-finds-raspberry-robin-worm-in-hundreds-of-windows-networks/

[5] https://therecord.media/microsoft-ties-novel-raspberry-robin-malware-to-evil-corp-cybercrime-syndicate

[6] https://securityaffairs.com/158969/malware/raspberry-robin-1-day-exploits.html

[7] https://research.checkpoint.com/2024/raspberry-robin-keeps-riding-the-wave-of-endless-1-days/

[8] https://redmondmag.com/articles/2022/10/28/microsoft-details-threat-actors-leveraging-raspberry-robin-worm.aspx

[9] https://www.bleepingcomputer.com/news/security/raspberry-robin-malware-evolves-with-early-access-to-windows-exploits/

[10] https://www.bleepingcomputer.com/news/security/raspberry-robin-worm-drops-fake-malware-to-confuse-researchers/

[11] https://thehackernews.com/2024/02/raspberry-robin-malware-upgrades-with.html

[12] https://decoded.avast.io/janvojtesek/raspberry-robins-roshtyak-a-little-lesson-in-trickery/

[13] https://blog.bushidotoken.net/2023/05/raspberry-robin-global-usb-malware.html

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
Alexandra Sentenac
Cyber Analyst

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June 25, 2026

Shadow AI Detection: The First Step Toward Securing AI

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Why shadow AI is emerging  

Imagine you’re an employee under pressure, deadlines stacking up, repetitive tasks piling higher by the day. You find a free AI tool online that promises to automate the work in seconds; no approvals are needed. It feels like a simple win, paste in some data, write a quick prompt, and move faster.

But in that moment, something changed.  

Sensitive customer information is entered into a tool your organization doesn’t monitor, doesn’t govern, and can’t see and suddenly, that data is no longer where it should be, and no one knows where it’s gone.

This is the reality of Shadow AI: employees using unsanctioned AI tools to move faster, while unintentionally creating risk that exists entirely outside visibility and control.  

This is not just a one off case, research across businesses indicate that nearly half of employees report using unsanctioned AI tools, often prioritizing speed and productivity over security. Additionally, 51% of employees report connecting AI tools to work systems or apps without IT approval, creating significant operational risk where the average cost of security incidents in organizations with a high level of shadow AI usage can reach $670k.

While shadow AI is often top of mind for security professionals, it is just one component of how AI use can increase risk. Understanding and managing shadow AI use should be considered as part of a broader, comprehensive risk management strategy that aims to secure AI systems, including human and agent identities, interactions, human-AI partnerships, and behaviors operating across the digital enterprise from visibility and governance through detection, response, and recovery.  

Effective risk management calls for a layered and interdisciplinary strategy. It requires addressing issues across governance and visibility; identity, access and agent control, data security and privacy, secure MLOps / LLMOps, runtime security, behavior-based detection, autonomous response and recovery.  

This blog explores a specific governance and visibility use case linked to shadow AI and reveals the challenges it presents as well as the defensive strategies that security teams can adopt.

Why shadow AI is hard to detect  

When it comes to AI, what organizations can easily see does not always reflect the full scope of AI activity occurring within the tools, applications, and workflows used across an enterprise. As a result, organizations using traditional rule-based methods to flag unusual activity may struggle to distinguish unsanctioned AI usage from legitimate operational behavior, particularly as SaaS applications, APIs, and orchestration layers increasingly have AI embedded into normal business workflows. Identifying threats using previously observed intelligence or depending on hard to maintain allow and block lists does not provide a dynamic enough strategy to manage risk. Also, many organizations are focusing on identifying Shadow AI in their governed infrastructure, like gateways, endpoints, or SASE, which is foundational. But, organizations require visibility and Shadow AI detection across all networked infrastructure from on-prem, hybrid, data centers, and cloud infrastructure that may not have endpoint agent visibility. This uncovers the utilization of MCP, data flows, and autonomous agents across these domains.

For example, employees interact with AI assistants across approved SaaS platforms every day. However, browser extensions and other types of plug-ins can route prompts that include enterprise data to embedded AI services in ways that are not visible to the security team. AI enabled workflows may invoke multiple APIs, orchestration layers, and cloud services behind the scenes, making it difficult for traditional security tooling to determine where data is processed, stored, or retransmitted. Because much of this activity occurs within trusted browser sessions and encrypted SaaS traffic, conventional network monitoring, DLP, and application allowlisting controls often lack the context needed to accurately identify or govern these interactions

Identifying AI tools in the environment is one part of the equation. Understanding the behavior surrounding their use is where the real challenge lies. An AI application is not inherently risky, but the way users or other assets interact with it may be. Sensitive data exposure, abnormal access patterns, and misuse of AI-assisted workflows often appear legitimate in isolation and only become visible through behavioral analysis across the broader environment.  

What Shadow AI visibility does and doesn’t show

Comprehensive Shadow AI visibility allows organizations to answer several important questions:

  • What types of AI are we using? What AI platforms, agents, MCP clients/servers, and services are active across the enterprise?  
  • Who is using AI services? Which users, business units, or systems are interacting with those AI services?  
  • Is our data safe? Is sensitive or regulated data being exposed through prompts, workflows, or integrations?  
  • Are AI systems behaving as expected? Are AI systems behaving anomalously or operating outside approved governance processes?  
  • Are our AI systems under attack? Is an attacker attempting to manipulate prompts, influence agent behavior, or abuse AI-enabled workflows?

Answering these questions is foundational to broader AI governance efforts. However, it is limited to helping teams understand initial interactions and fails to offer insight into dependencies and outcomes that are critical to securing AI across an enterprise.  

Deeper visibility that includes the ability to understand dependencies and outcomes are not always available in AI security point products. Answering the questions below requires understanding runtime behavior and operational outcomes:  

  • What actions did the AI interaction trigger?  
  • What systems, applications, or data did it access? Did the AI operate beyond its intended permissions or scope?  
  • Could a low-risk interaction lead to high-risk outcomes?  
  • What is the risk and context understanding of an anomalous activity to assist in prioritization of analysis and autonomous response action?

The distinction between these two sets of questions offers two different layers of AI security. The first set of questions focuses on discovery and interaction visibility. The second set focuses on providing visibility that includes the context and outcomes that are critical for managing follow-on risks associated with obfuscated downstream activities.  

Together, these layers help organizations move beyond simply identifying AI usage toward understanding how AI behaves operationally across the enterprise.

How organizations are addressing shadow AI

Most organizations still approach shadow AI as an application control problem, relying on policies, browser restrictions, and allow/block lists. However, AI adoption is evolving faster than most governance processes can realistically keep pace with. New assistants, plugins, and embedded AI features appear continuously, creating pressure to enable business productivity while simultaneously containing risk.  

Existing governance processes were designed for a more traditional SaaS adoption cycle, where new applications could be reviewed, approved, and monitored over longer time horizons. AI adoption operates differently. New capabilities can appear overnight inside existing platforms employees already use, making it difficult for security and governance teams to maintain an accurate understanding of enterprise AI exposure. This means that many organizations are experiencing significant operational overhead, particularly in large environments where AI usage is decentralized across teams, departments, and third-party services.  

Where should organizations start when securing their AI systems?

Shadow AI identification is an on-going critical component for AI Risk/Governance Boards as well as security organizations. As organizations seek AI certifications like ISO 42001 AI Management Systems, visibility into all AI adoption from enterprise use to custom innovation and development is crucial. Shadow AI identification provides organizations with the visibility needed to decide whether an AI tool should be brought into governed environments to reduce data loss (DLP) risks or whether policies should be established and enforced to restrict their use.

As organizations rapidly innovate and adopt AI, they are taking on more and more risk. Organizations need to have a strategy in place to mitigate the assumed risk, especially with third-party adoption. Visibility, monitoring, governance enforcement, behavioral-based detection of non-deterministic systems, and autonomous investigation and containment becomes critical to mitigating the risk of AI systems.  

How Darktrace secures AI and shadow AI

Attackers are using AI to move faster, scale tactics, and make threats more adaptive and convincing. Internally, organizations are grappling with new forms of risk created by generative AI, autonomous agents, shadow AI, and increasingly complex digital environments.

Darktrace helps organizations protect both people and AI in a world where AI is now central to how business gets done. Darktrace / SECURE AI helps organizations discover and control shadow AI by surfacing unsanctioned or unexpected AI activity where it appears – including MCP detections, distinguishing misuse of legitimate tools and unapproved services, and applying policy to contain data exposure while guiding users toward sanctioned options.

Stay up to date on AI security

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

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June 25, 2026

From Click to Command: Behavioral Detection of AppleScript-Led MacOS Intrusions

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Introduction

Darktrace’s Threat Research team is publishing this analysis to help defenders understand an active pattern of macOS tradecraft observed in multiple customer environments. This post summarizes the behaviors observed, how they were assessed, and what defenders can do now.

Across multiple environments, Darktrace observed a consistent MacOS intrusion pattern beginning with ClickFix-style user-assisted “update” execution and transitioning into AppleScript-driven post-compromise activity and sustained outbound signaling.

While individual indicators were low-confidence, the repeated convergence of weak behavioral signals — including HTTP POST beaconing, rare or IP-only destinations, SSL anomalies, and abnormal client characteristics — provided a defensible indication of command-and-control establishment Darktrace detection and response in these cases was driven by behavior over artifacts. In the highest-confidence instances, automated containment disrupted outbound signaling before sustained tasking could occur.

Background

ClickFix-style activity typically relies on user-assisted execution and plausible “update” pretexting, followed by post-execution use of native tools to keep the footprint light. In MacOS environments, AppleScript and other built-in scripting mechanisms enable flexible post-compromise workflows while minimizing stable file-based indicators.

Following execution, affected devices exhibited a consistent behavioral pattern. AppleScript or equivalent native scripting activity was observed initiating follow-on workflows, after which outbound communications began to establish a structured rhythm.

These communications were characterized by repeated HTTP POST requests to low-prevalence or IP-only endpoints, often combined with unusual SSL properties and client identifiers that diverged from baseline device behavior. Individually, these signals were weak. When correlated across time and devices, they formed a pattern consistent with control establishment rather than benign software activity.

In higher-confidence cases, Autonomous Response actions were able to reduce or halt outbound signaling, interrupting the attacker’s ability to maintain control.

Detection Timeline

In representative cases, the sequence unfolded as follows:

Stage 1 – Initial Execution

Initial activity began with suspicious or masqueraded execution on a MacOS endpoint, consistent with ClickFix-style user deception.

Stage 2 – Post-Execution Scripting

This was followed closely by native scripting activity, most commonly AppleScript, indicating the transition into post-execution workflow.

Stage 3 – Outbound Communications

Outbound communications then emerged, initially sporadic but quickly forming a consistent cadence of HTTP POST requests to rare external endpoints.

Stage 4 – Anomaly Convergence

As activity persisted, additional anomalies became visible — unusual SSL characteristics, abnormal user agents, and connections to infrastructure with no prior network prevalence.

Stage 5 – Autonomous Response

In the most mature stages of the activity, automated containment actions disrupted outbound communications on affected devices, limiting the attacker’s ability to continue tasking while investigations progressed.

Darktrace coverage and detections

The following use-case highlights systems likely affected by malicious macOS intrusion activity linked by Microsoft to the Democratic People’s Republic of Korea (DPRK) [1], with indications of suspicious behavior observed between March 1 and May 3, 2026. The activity overlaps with patterns described in recent reporting on DPRK-nexus MacOS intrusions [1], though attribution confidence in this case remains moderate and based on behavioral alignment rather than solely infrastructure linkage.

Analyst confidence emerged through the correlation of multiple weak signals across time and devices. This included model coverage for rare external communications, sustained beaconing patterns, repeated HTTP POSTs, and anomalous client characteristics. Where enabled, Autonomous Response actions disrupted the most active outbound paths to reduce the attacker’s ability to maintain control while Darktrace’s investigation continued.

Notably, this highly anomalous behavior included:

  • Outbound connections to the rare external endpoint, zoom[.]uswebob[.]us associated with IP address, 148.72.73[.]98 [2][3] over port 443
  • Outbound connections to the rare external endpoint, check02id[.]com associated with IP address, 83.136.210[.]180 [4] over port 7365
  • Outbound connections to the rare external endpoints, 104.145.210[.]107 [5] over port 8443 and 83.136.208[.]48 [6] over port 443
  • Outbound connections to the rare external endpoint, 83.136.208[.]246 [7] over port 6783 with observed URI `/api/daemon` and a PowerShell user agent

Darktrace’s detection initially highlighted a desktop device (running MacOS) engaging in anomalous behavior as early as March 12, 2026. Starting on March 12, the source device triggered a ‘Possible Doppelganger Attack’ alert including connectivity to the hostname "zoom[.]uswebob[.]us · 148.72.73[.]98" over port 443 (TCP, HTTPS, H2). This model highlights a device connecting to a location that is rare but masquerades as legitimate software, such as Zoom in this case, a commonly used technique to blend into expected traffic [2] [3].

 Initial connectivity observed to the rare external hostname, zoom[.]uswebob[.]us · 148.72.73[.]98, over port 443.
Figure 1: Initial connectivity observed to the rare external hostname, zoom[.]uswebob[.]us · 148.72.73[.]98, over port 443.

This was followed roughly seven later by a connection to 104.145.210[.]107 over port 8443, during which approximately 250 KiB of data of inbound data and 30 MiB of outbound data was observed, triggering the ‘Unusual Activity / Unusual External Data to New Endpoint’ in Darktrace.

Quickly after this connection, Darktrace’s Autonomous Response intervened, blocking the device’s access to the unusual external location and halting the data exfiltration attempt.

Figure 2: Darktrace’s detection of unusual data exfiltration, shortly followed by an Autonomous Response action to block it.

The device continued to consistently trigger model alerts relating to unusual external connectivity, including 'Posting HTTP to IP Without Hostname', 'Anomalous Connection / Rare External SSL Self-Signed' alerts, until well after 3 PM that day.

Figure 3: Additional external connectivity to new IP without a hostname, including connectivity to 83.136.208[.]246, alongside an anomalous ‘curl/8.7.1’ user agent and ‘/api/daemon’ URI.
Figure 4: Continued external SSL connectivity to IP 83.136.208[.]48, including connectivity to 83.136.208[.]246, alongside an anomalous ‘curl/8.7.1’ user agent and ‘/api/daemon’ URI.
Figure 5: Continued external HTTP connectivity to hostname, check02id[.]com · 83.136.210[.]180, alongside an anomalous ‘Go-http-client/1,1’ user agent.

From March 13 to March 28, the device continued exhibit unusual connectivity to various endpoints (e.g., 83.136.208[.]48, 83.136.208[.]246, check02id[.]com · 83.136.210[.]180), with the 'Multiple HTTP POSTs to Rare Hostname' model consistently triggering.

Windows OS Case

Pivoting over to an additional device, this time running Windows OS, anomalous behavior was also observed between March 30 and April 20. Notably, on March 30, the device was observed making a large number of suspicious external connection attempts to 83.136.208[.]246 over port 6783, all of which failed.

A further indicator was observed on April 1 with PowerShell connectivity to the same rare endpoint (83.136.208[.]246, port 6783), using the URI '/api/daemon' and the user agent 'Mozilla/5.0 (Windows NT; Windows NT 10.0; fr-FR) WindowsPowerShell/5.1.26100.7920'.  Additional alerts included 'New User Agent to IP Without Hostname' and 'Anomalous Github Download', alongside activity involving the same endpoint.

Figure 6 : ‘Anomalous Powershell to Rare External Destination’ and ‘Github Download’ model alerts. This behavior involved connectivity with the endpoints ‘83.136.208[.]246’ and ‘github[.]com’.

The device continued triggering 'Posting HTTP to IP Without Hostname' & 'PowerShell to External Rare' alerts between April 4 and April 20 across multiple related endpoints (i.e., 83.136.208[.]48, 83.136.208[.]246, check02id[.]com · 83.136.210[.]180).

Darktrace’s Autonomous Response capability was able to block suspicious PowerShell attempts to unusual external locations, as shown below in an example from April 20.

Figure 7:  Autonomous Response intervening to block an unusual PowerShell connection to an external destination.

Cyber AI Analyst investigations

In higher-confidence instances, Darktrace’s Cyber AI Analyst investigations helped connect otherwise separate model alerts into a single incident narrative, highlighting the attacker’s progression from post-execution scripting into sustained outbound signaling. This contextual stitching is particularly valuable in macOS scenarios where static artefacts are limited, and behavioral sequencing defines the intrusion.

Cyber AI Analyst investigations highlighted alerts on March 12, including unusual repeated connections and possible SSL command-and-control (C2) to multiple endpoints:

Figure 8: Cyber AI Analyst investigation linking events into a unified incident.

Autonomous Response

In addition to the containment actions detailed earlier, Autonomous Response implemented multiple additional measures to contain suspicious activity throughout the course of this attack. Whenever unusual external connectivity was detected, Darktrace blocked it, closing down potential C2 channels. Likewise, when data exfiltration attempts were identified, these connections were stopped to prevent the potential loss of sensitive data.

Figure 9: Autonomous Response actions implemented by Darktrace in response to suspicious connectivity in mid-March.

Furthermore, in cases where a device was deemed to have carried out a significant number of anomalous activities, Darktrace enforced a “pattern of life” on the device, preventing it from deviating from its expected behavior while allowing legitimate business operations to continue uninterrupted.

Figure 10: Autonomous Response actions implemented by Darktrace in response to suspicious connectivity in April, including the “Enforce Pattern of Life” action.

Conclusion

macOS intrusion tradecraft continues to shift toward native tooling and lightweight control channels designed to evade signature-led controls.

The repeated convergence of rare destinations, POST-based signaling, and anomalous client behavior — observed across time and across devices — provided sufficient evidence to act early and with confidence.

As macOS tradecraft continues to evolve, the defender advantage increasingly lies not in signatures, but in the ability to reason from behavior.

Credit to Justin Torres (Senior Cyber Analyst), Nathaniel Jones (VP, Security & AI Strategy, FCISO)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Alert Coverage:

/ NETWORK-based model alerts:

·       Anomalous Connection::Multiple HTTP POSTs to Rare Hostname

·       Anomalous Connection::Rare External SSL Self-Signed

·       Anomalous Connection::Powershell to Rare External

·       Anomalous Connection::New User Agent to IP Without Hostname

·       Anomalous Connection::Posting HTTP to IP Without Hostname

·       Compromise::Fast Beaconing to DGA

·       Compromise::Large Number of Suspicious Failed Connections

·       Device::Anomalous Github Download

·       Device::New PowerShell User Agent

·       Unusual Activity::Unusual External Data to New Endpoint

/ NETWORK-based Autonomous Response model alerts:

·       Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block

·       Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach

·       Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block

Indicators of Compromise (IoCs)

IP/Hostname:

·       zoom[.]uswebob[.]us · 148.72.73[.]98

·       83.136.208[.]246

·       check02id[.]com · 83.136.210[.]180

·       83.136.208[.]48

·       104.145.210[.]107

URIs:

·       /api/daemon

Destination Port Usage:

·       6783

·       5202

·       443

·       7365

·       8443

ASN:

·       AS400897 PETROSKY

·       AS398256 AS-ULTAHOST

User agents:

·       Mozilla/5.0 (Windows NT; Windows NT 10.0; fr-FR) WindowsPowerShell/5.1.26100.7920

·       Go-http-client/1.1

·       curl/8.7.1

MITRE ATT&CK Mapping

(Technique Name - Tactic - ID - Sub-Technique of)

·       Browser Session Hijacking - COLLECTION - T1185

·       Web Protocols - COMMAND AND CONTROL - T1071.001 - T1071

·       Install Digital Certificate - RESOURCE DEVELOPMENT - T1608.003 - T1608

·       PowerShell - EXECUTION - T1059.001 - T1059

·       Domain Generation Algorithms - COMMAND AND CONTROL - T1568.002 - T1568

·       Non-Standard Port - COMMAND AND CONTROL - T1571

·       Malware - RESOURCE DEVELOPMENT - T1588.001 - T1588

·       Web Service - COMMAND AND CONTROL - T1102

·       Code Repositories - COLLECTION - T1213.003 - T1213

·       Exploitation of Remote Services - LATERAL MOVEMENT - T1210

·       Exfiltration Over C2 Channel - EXFILTRATION - T1041

·       Exfiltration to Cloud Storage - EXFILTRATION - T1567.002 - T1567

References:

[1] https://www.microsoft.com/en-us/security/blog/2026/04/16/dissecting-sapphire-sleets-macos-intrusion-from-lure-to-compromise/

[2] https://radar.securityalliance.org/advisory-on-dprk-unc1069-fake-microsoft-teams-and-zoom-calls/

[3] https://www.virustotal.com/gui/domain/uswebob.us

[4] https://www.virustotal.com/gui/ip-address/83.136.210.180/community

[5] https://www.virustotal.com/gui/ip-address/104.145.210.107/community

[6] https://www.virustotal.com/gui/ip-address/83.136.208.48/community

[7] https://www.virustotal.com/gui/ip-address/83.136.208.246/community

[8] https://www.darktrace.com/blog/applescript-abuse-unpacking-a-macos-phishing-campaign

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About the author
Justin Torres
Cyber Analyst
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