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August 22, 2023

Darktrace’s Detection of Unattributed Ransomware

Leveraging anomaly-based detection, we successfully identified an ongoing ransomware attack on the network of a customer and the activity that preceded it.
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
Natalia Sánchez Rocafort
Cyber Security Analyst
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Aug 2023

In the current threat landscape, much of the conversation around ransomware focusses on high-profile strains and notorious threat groups. While organizations and their security teams are justified in these concerns, it is important not to underestimate the danger posed by smaller scale, unattributed ransomware attacks.

Unlike attributed ransomware strains, there are often no playbooks or lists of previously observed indicators of compromise (IoCs) that security teams can consult to help them shore up their cyber defenses. As such, anomaly detection is critical to ensure that emerging threats can be detected based on their abnormality on the network, rather than relying heavily on threat intelligence.

In mid-March 2023, a Darktrace customer requested analytical support from the Darktrace Security Operations Center (SOC) after they had been hit by a ransomware attack a few hours earlier. Darktrace was able to uncover a myriad of malicious activity that preceded the eventual ransomware deployment, ultimately assisting the customer to identify compromised devices and contain the ransomware attack.

Attack Overview

While there were a small number of endpoints that had been flagged as malicious by open-source intelligence (OSINT), Darktrace DETECT™ focused on the unusualness of the activity surrounding this emerging ransomware attack. This provided unparalleled visibility over this ransomware attack at every stage of the cyber kill chain, whilst also revealing the potential origins of the compromise which came months area.

Initial Compromise

Initial investigation revealed that several devices that Darktrace were observed performing suspicious activity had previously engaged in anomalous behavior several months before the ransomware event, indicating this could be a part of a repeated compromise or the result of initial access brokers.

Most notably, in late January 2023 there was a spike in unusual activity when some of the affected devices were observed performing activity indicative of network and device scanning.

Darktrace DETECT identified some of the devices establishing unusually high volumes of internal failed connections via TCP and UDP, and the SMB protocol. Various key ports, such as 135, 139, and 445, were also scanned.

Due to the number of affected devices, the exact initial attack vector is unclear; however, one likely scenario is associated with an internet-facing DNS server. Towards the end of January 2023, the server began to receive unusual TCP DNS requests from the rare external endpoint, 103.203.59[.]3, which had been flagged as potentially malicious by OSINT [4]. Based on a portion of the hostname of the device, dc01, we can assume that this server served as a gateway to the domain controller. If a domain controller is compromised, a malicious actor would gain access to usernames and passwords within a network allowing attackers to obtain administrative-level access to an organization’s digital estate.

Around the same time as the unusual TCP DNS requests, Darktrace DETECT observed the domain controller engaging in further suspicious activity. As demonstrated in Figure 1, Darktrace recognized that this server was not responding to common requests from multiple internal devices, as it would be expected to. Following this, the device was observed carrying out new or uncommon Windows Management Instrumentation (WMI) activity. WMI is typically used by network administrators to manage remote and local Windows systems [3].

Figure 1: Device event log depicting the possible Initial attack vector.


Had Darktrace RESPOND™ been enabled in autonomous response mode, it would have to blocked connections originating from the compromised internal devices as soon as they were detected, while also limiting affected devices to their pre-established patterns of file to prevent them from carrying out any further malicious activity.

Darktrace subsequently observed multiple devices establishing various chains of connections that are indicative of lateral movement activity, such as unusual internal RDP and WMI requests. While there may be devices within an organization that do regularly partake these types of connections, Darktrace recognized that this activity was extremely unusual for these devices.

Darktrace’s Self-Learning AI allows for a deep understanding of customer networks and the devices within them. It’s anomaly-based threat detection capability enables it to recognize subtle deviations in a device’s normal patterns of behavior, without depending on known IoCs or signatures and rules to guide it.

Figure 2: Observed chain of possible lateral movement.


Persistence

Darktrace DETECT observed several affected devices communicating with rare external endpoints that had also been flagged as potentially malicious by OSINT tools. Multiple devices were observed performing activity indicative of NTLM brute-forcing activity, as seen in the Figure 3 which highlights the event log of the aforementioned domain controller. Said domain controller continuously engaged in anomalous behavior throughout the course of the attack. The same device was seen using a potentially compromise credential, ‘cvd’, which was observed via an SMB login event.

Figure 3: Continued unusual external connectivity.


Affected devices, including the domain controller, continued to engage in consistent communication with the endpoints prior to the actual ransomware attack. Darktrace identified that some of these malicious endpoints had likely been generated by Domain Generation Algorithms (DGA), a classic tactic utilized by threat actors. Subsequent OSINT investigation revealed that one such domain had been associated with malware such as TrojanDownloader:Win32/Upatre!rfn [5].

All external engagements were observed by Darktrace DETECT and would have been actioned on by Darktrace RESPOND, had it been configured in autonomous response mode. It would have blocked any suspicious outgoing connections originating from the compromised devices, thus preventing additional external engagement from taking place. Darktrace RESPOND works in tandem with DETECT to autonomously take action against suspicious activity based on its unusualness, rather than relying on static lists of ‘known-bads’ or malicious IoCs.

Reconnaissance

On March 14, 2023, a few days before the ransomware attack, Darktrace observed multiple internal devices failing to establish connections in a manner that suggests SMB, RDP and network scanning. Among these devices once more was the domain controller, which was seen performing potential SMB brute-forcing, representing yet another example of malicious activity carried out by this device.

Lateral Movement

Immediately prior to the attack, many compromised devices were observed mobilizing to conduct an array of high-severity lateral movement activity. Darktrace detected one device using two administrative credentials, namely ‘Administrator’ and ‘administrator’, while it also observed a notable spike in the volume of successful SMB connections from the device around the same time.

At this point, Darktrace DETECT was observing the progression of this attack along the cyber kill chain. What had started as internal recognisance, had escalated to exploitation and ensuing command-and-control activity. Following an SMB brute-force attempt, Darktrace DETECT identified a successful DCSync attack.

A DCSync attack occurs when a malicious actor impersonates a domain controller in an effort to gather sensitive information, such as user credentials and passwords hashes, by replicating directory services [1]. In this case, a device sent various successful DRSGetNCChanges operation requests to the DRSUAPI endpoint.

Data Exfiltration

Around the same time, Darktrace detected the compromised server transferring a high volume of data to rare external endpoints associated with Bublup, a third-party project management application used to save and share files. Although the actors attempted to avoid the detection of security tools by using a legitimate file storage service, Darktrace understood that this activity represented a deviation in this device’s expected pattern of life.

In one instance, around 8 GB of data was transferred, and in another, over 4 GB, indicating threat actors were employing a tactic known as ‘low and slow’ exfiltration whereby data is exfiltrated in small quantities via multiple connections, in an effort to mask their suspicious activity. While this tactic may have evaded the detection of traditional security measures, Darktrace’s anomaly-based detection allowed it to recognize that these two incidents represented a wider exfiltration event, rather than viewing the transfers in isolation.

Impact

Finally, Darktrace began to observe a large amount of suspicious SMB activity on the affected devices, most of which was SMB file encryption. DETECT observed the file extension ‘uw9nmvw’ being appended to many files across various internal shares and devices. In addition to this, a potential ransom note, ‘RECOVER-uw9nmvw-FILES.txt’, was detected on the network shortly after the start of the attack.

Figure 4: Depiction of the high-volume of suspicious SMB activity, including file encryption.


Conclusion

Ultimately, this incident show cases how Darktrace was able to successfully identify an emerging ransomware attack using its unrivalled anomaly-based detection capabilities, without having to rely on any previously established threat intelligence. Not only was Darktrace DETECT able to identify the ransomware at multiple stages of the kill chain, but it was also able to uncover the anomalous activity that took place in the buildup to the attack itself.

As the attack progressed along the cyber kill chain, escalating in severity at every juncture, DETECT was able to provide full visibility over the events. Through the successful identification of compromised devices, anomalous administrative credentials usage and encrypted files, Darktrace was able to greatly assist the customer, ensuring they were well-equipped to contain the incident and begin their incident management process.

Darktrace would have been able to aid the customer even further had they enabled its autonomous response technology on their network. Darktrace RESPOND would have taken targeted, mitigative action as soon as suspicious activity was detected, preventing the malicious actors from achieving their goals.

Credit to: Natalia Sánchez Rocafort, Cyber Security Analyst, Patrick Anjos, Senior Cyber Analyst.

MITRE Tactics/Techniques Mapping

RECONNAISSANCE

Scanning IP Blocks  (T1595.001)

RECONNAISSANCE

Vulnerability Scanning  (T1595.002)

IMPACT

Service Stop  (T1489)

LATERAL MOVEMENT

Taint Shared Content (T1080)

IMPACT

Data Encrypted for Impact (T1486)

INITIAL ACCESS

Replication Through Removable Media (T1200)

DEFENSE EVASION

Rogue Domain Controller (T1207)

COMMAND AND CONTROL

Domain Generation Algorithms (T1568.002)

EXECUTION

Windows Management Instrumentation (T1047)

INITIAL ACCESS

Phishing (T1190)

EXFILTRATION

Exfiltration Over C2 Channel (T1041)

IoC Table

IoC ----------- TYPE ------------- DESCRIPTION + PROBABILITY

CVD --------- credentials -------- Possible compromised credential

.UW9NMVW - File extension ----- Possible appended file extension

RECOVER-UW9NMVW-FILES.TXT - Ransom note - Possible ransom note observed

84.32.188[.]186 - IP address ------ C2 Endpoint

AS.EXECSVCT[.]COM - Hostname - C2 Endpoint

ZX.EXECSVCT[.]COM - Hostname - C2 Endpoint

QW.EXECSVCT[.]COM - Hostname - C2 Endpoint

EXECSVCT[.]COM - Hostname ------ C2 Endpoint

15.197.130[.]221 --- IP address ------ C2 Endpoint

AS59642 UAB CHERRY SERVERS - ASN - Possible ASN associated with C2 Endpoints

108.156.28[.]43

108.156.28[.]22

52.84.93[.]26

52.217.131[.]241

54.231.193[.]89 - IP addresses - Possible IP addresses associated with data exfiltration

103.203.59[.]3 -IP address ---- Possible IP address associated with initial attack vector

References:

[1] https://blog.netwrix.com/2021/11/30/what-is-dcsync-an-introduction/

[2] https://www.easeus.com/computer-instruction/delete-system32.html#:~:text=System32%20is%20a%20folder%20on,DLL%20files%2C%20and%20EXE%20files.

[3] https://www.techtarget.com/searchwindowsserver/definition/Windows-Management-Instrumentation#:~:text=WMI%20provides%20users%20with%20information,operational%20environments%2C%20including%20remote%20systems.

[4] https://www.virustotal.com/gui/ip-address/103.203.59[.]3

[5] https://otx.alienvault.com/indicator/ip/15.197.130[.]221

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
Natalia Sánchez Rocafort
Cyber Security 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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About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO

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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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