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August 9, 2022

Cyber Tactics in the Russo-Ukrainian Conflict

The conflict between Russia and Ukraine has led to fears of a full-scale cyberwar. Learn the cyber attack tactics used, hacking groups involved, and more!
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
Rosa Jong
OSINT Analyst
Written by
Taisiia Garkava
Security Analyst
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09
Aug 2022

Introduction

Since the beginning of the Russian invasion of Ukraine in February 2022, cyber communities around the world have been witnessing what can be called a ‘renaissance of cyberwarfare' [1]. Rather than being financially motivated, threat actors are being guided by political convictions to defend allies or attack their enemies. This blog reviews some of the main threat actors involved in this conflict and their ongoing tactics, and advises on how organizations can best protect themselves. Darktrace’s preliminary assessments predicted that attacks would be observed globally with a focus on pro-Ukrainian nations such as North Atlantic Treaty Organization (NATO) members and that identified Advanced Persistent Threat (APT) groups would develop new and complex malware deployed through increasingly sophisticated attack vectors. This blog will show that many of these assessments had unexpected outcomes.

Context for Conflict 

Cyber confrontation between Russia and Ukraine dates back to 2013, when Viktor Yanukovych, (former President of Ukraine) rejected an EU trade pact in favour of an agreement with Russia. This sparked mass protests leading to his overthrow, and shortly after, Russian troops annexed Crimea and initiated the beginning of Russian-Ukrainian ground and cyber warfare. Since then, Russian threat actors have been periodically targeting Ukrainian infrastructure. One of the most notable examples of this, an attack against their national power grid in December 2015, resulted in power outages for approximately 255,000 people in Ukraine and was later attributed to the Russian hacking group Sandworm [2 & 3]. 

Another well-known attack in June 2017 overwhelmed the websites of hundreds of Ukrainian organizations using the infamous NotPetya malware. This attack is still considered the most damaging cyberattack in history, with more than €10 billion euros in financial damage [4]. In February 2022, countries witnessed the next stage of cyberwar against Ukraine with both new and familiar actors deploying various techniques to target their rival’s critical infrastructure. 

Tactic 1: Ransomware

Although some sources suggest US ransomware incidents and expectations of ransom may have declined during the conflict, ransomware still remained a significant tactic deployed globally across this period [5] [6] [7]. A Ukrainian hacking group, Network Battalion 65 (NB65), used ransomware to attack the Russian state-owned television and radio broadcasting network VGTRK. NB65 managed to steal 900,000 emails and 4000 files, and later demanded a ransom which they promised to donate to the Ukrainian army. This attack was unique because the group used the previously leaked source code of Conti, another infamous hacker group that had pledged its support to the Russian government earlier in the conflict. NB65 modified the leaked code to make unique ransomware for each of its targets [5]. 

Against expectations, Darktrace’s customer base appeared to deviate from these ransom trends. Analysts have seen relatively unsophisticated ransomware attacks during the conflict period, with limited evidence to suggest they were connected to any APT activity. Between November 2021 and June 2022, there were 51 confirmed ransomware compromises across the Darktrace customer base. This represents an increase of 43.16% compared to the same period the year before, accounting for relative customer growth. Whilst this suggests an overall growth in ransom cases, many of these confirmed incidents were unattributed and did not appear to be targeting any particular verticals or regions. While there was an increase in the energy sector, this could not be explicitly linked to the conflict. 

The Darktrace DETECT family has a variety of models related to ransomware visibility:

Darktrace Detections for T1486 (Data Encrypted for Impact):

- Compromise / Ransomware / Ransom or Offensive Words Written to SMB

- Compromise / Ransomware / Suspicious SMB Activity

- Anomalous Connection / Sustained MIME Type Conversion

- Unusual Activity / Sustained Anomalous SMB Activity

- Compromise / Ransomware / Suspicious SMB File Extension

- Unusual Activity / Anomalous SMB Read & Write

- Unusual Activity / Anomalous SMB Read & Write from New Device

- SaaS / Resource / SaaS Resources with Additional Extensions

- Compromise / Ransomware / Possible Ransom Note Read

- [If RESPOND is enabled] Antigena / Network / External Threat / Antigena Ransomware Block

Tactic 2: Wipers

One of the largest groups of executables seen during the conflict were wipers. On the eve of the invasion, Ukrainian organizations were targeted by a new wiper malware given the name “HermeticWiper”. Hermetic refers to the name of the Cyprian company “Hermetica Digital Ltd.” which was used by attackers to request a code signing certificate [6]. Such a digital certificate is used to verify the ownership of the code and that it has not been altered. The 24-year-old owner of Hermetica Digital says he had no idea that his company was abused to retrieve a code signing certificate [7]. 

HermeticWiper consists of three components: a worm, decoy ransomware and the wiper malware. The custom worm designed for HermeticWiper was used to spread the malware across the network of its infected machines. ESET researchers discovered that the decoy ransomware and the wiper were released at the same time [8]. The decoy ransomware was used to make it look like the machine was hit by ransomware, when in reality the wiper was already permanently wiping data from the machines. In the attack’s initial stage, it bypasses Windows security features designed to prevent overwriting boot records by installing a separate driver. After wiping data from the machine, HermeticWiper prevents that data from being re-fragmented and overwrites the files to fragment it further. This is done to make it more challenging to reconstruct data for post-compromise forensics [9]. Overall, the function and purpose of HermeticWiper seems similar to that of NotPetya ransomware. 

HermeticWiper is not the only conflict-associated wiper malware which has been observed. In January 2022, Microsoft warned Ukrainian customers that they detected wiper intrusion activity against several European organizations. One example of this was the MBR (Master Boot Record) wiper. This type of wiper overwrites the MBR, the disk sector that instructs a computer on how to load its operating system, with a ransomware note. In reality, the note is a misdirection and the malware destroys the MBR and targeted files [10].  

One of the most notable groups that used wiper malware was Sandworm. Sandworm is an APT attributed to Russia’s foreign military intelligence agency, GRU. The group has been active since 2009 and has used a variety of TTPs within their attacks. They have a history of targeting Ukraine including attacks in 2015 on Ukraine’s energy distribution companies and in 2017 when they used the aforementioned NotPetya malware against several Ukrainian organizations [11]. Another Russian (or pro-Russian) group using wiper malware to target Ukraine is DEV-0586. This group targeted various Ukrainian organizations in January 2022 with Whispergate wiper malware. This type of wiper malware presents itself as ransomware by displaying a file instructing the victim to pay Bitcoin to have their files decrypted [12].  

Darktrace did not observe any confirmed cases of HermeticWiper nor other conflict-associated wipers (e.g IsaacWiper and CaddyWiper) within the customer base over this period. Despite this, Darktrace DETECT has a variety of models related to wipers and data destruction:

Darktrace Detections for T1485 (Data Destruction)- this is the main technique exploited during wiper attacks

- Unusual Activity / Anomalous SMB Delete Volume

- IaaS / Unusual Activity / Anomalous AWS Resources Deleted

- IaaS / Storage / S3 Bucket Delete

- SaaS / Resource / Mass Email Deletes from Rare Location

- SaaS / Resource / Anomalous SaaS Resources Deleted

- SaaS / Resource / Resource Permanent Delete

- [If RESPOND is enabled] Antigena / Network / Manual / Enforce Pattern of Life

- [If RESPOND is enabled] Antigena / SaaS / Antigena Unusual Activity Block

Tactic 3: Spear-Phishing

Another strategy that some threat actors employ is spear-phishing. Targeting can be done using email, social media, messaging, or other platforms.

The hacking group Armageddon (also known as Gamaredon) has been responsible for several spear-phishing attacks during the crisis, primarily targeting individuals involved in the Ukrainian Government [13]. Since the beginning of the war, the group has been sending out a large volume of emails containing an HTML file which, if opened, downloads and launches a RAR payload. Those who click the attached link download an HTA with a PowerShell script which obtains the final Armageddon payload. Using the same strategy, the group is also targeting governmental agencies in the European Union [14]. With high-value targets, the need to improve teaching around phishing identification to minimize the chance of being caught in an attacker's net is higher than ever. 

In comparison to the wider trends, Darktrace analysts again saw little-to-no evidence of conflict-associated phishing campaigns affecting customers. Those phishing attempts which did target customers were largely not conflict-related. In some cases, the conflict was used opportunistically, such as when one customer was targeted with a phishing email referencing Russian bank exclusions from the SWIFT payment system (Figures 1 and 2). The email was identified by Darktrace/Email as a probable attempt at financial extortion and inducement - in this case the company received a spoofed email from a major bank’s remittance department.  

Figure 1- Screencap of targeted phishing email sent to Darktrace customer
Figure 2- Attached file contains soliciting reference to SWIFT, a money payment system which select Russian banks were removed from because of the conflict [15]

 Although the conflict was used as a reference in some examples, in most of Darktrace’s observed phishing cases during the conflict period there was little-to-no evidence to suggest that the company being targeted nor the threat actor behind the phishing attempt was associated with or attributable to the Russia-Ukraine conflict.

However, Darktrace/Email has several model categories which pick up phishing related threats:

Sample of Darktrace for Email Detections for T1566 (Phishing)- this is the overarching technique exploited during spear-phishing events

Model Categories:

- Inducement

- Internal / External User Spoofing

- Internal / External Domain Spoofing

- Fake Support

- Link to Rare Domains

- Link to File Storage

- Redirect Links

- Anomalous / Malicious Attachments

- Compromised Known Sender

Specific models can be located on the Email Console

 

Tactic 4: Distributed-Denial-of-Service (DDoS)

Another tactic employed by both pro-Russian and pro-Ukrainian threat actors was DDoS (Distributed Denial of Service) attacks. Both pro-Russia and pro-Ukraine actors were seen targeting critical infrastructure, information resources, and governmental platforms with mass DDoS attacks. The Ukrainian Minister of Digital Transformation, Mykhailo Fedorov, called on an IT Army of underground Ukrainian hackers and volunteers to protect Ukraine's critical infrastructure and conduct DDoS attacks against Russia [16]. As of 1 August 2022, more than two hundred thousand people are subscribed to the group's official Telegram channel, where potential DDoS targets are announced [17].

Darktrace observed similar pro-Ukraine DDoS behaviors within a variety of customer environments. These DDoS campaigns appeared to involve low-volume individual support combined with crowd-sourced DDoS activity. They were hosted on a range of public-sourced DDoS sites and seemed to share sentiments of groups such as the IT Army of Ukraine (Figure 3).

Figure 3- Example DDoS outsource domain with unusual TLD 

From the Russian side, one of the prominent newly emerged groups, Killnet, is striking back, launching several massive DDoS attacks against the critical infrastructure of countries that provide weaponry to Ukraine [18 & 19]. Today, the number of supporters of Killnet has grown to eighty-four thousand on their Telegram channel. The group has already launched a number of mass attacks on several NATO states, including Germany, Poland, Italy, Lithuania and Norway. This shows the conflict has attracted new and fast-growing groups with large backing and the capacity to undertake widespread attacks. 

DETECT has several models to identify anomalous DoS/DDoS activity:

Darktrace Detection for T1498 (Network Denial of Service)- this is the main technique exploited during DDoS attacks

- Device / Anomaly Indicators / Denial of Service Activity Indicator

- Anomalous Server Activity / Possible Denial of Service Activity

- [If RESPOND is enabled] Antigena / Network / External Threat / Antigena Suspicious Activity Block

What did Darktrace observe?

Darktrace’s cross-fleet detections were largely contrary to expectations. Analysts did not see large-scale complex conflict-linked attacks utilizing either conflict-associated ransomware, malware, or other TTPs. Instead, cyber incidents observed were largely opportunistic, using malware that could be purchased through Malware-as-a-Service models and other widely available toolkits, (rather than APT or conflict-attributable attacks). Overall, this is not to say there have been no repercussions from the conflict or that opportunistic attacks will cease, but evidence suggests that there were fewer wider cyber consequences beyond the initial APT-based attacks seen in the public forum. 

Another trend expected since the beginning of the conflict was targeted responses to sanction announcements focusing on NATO businesses and governments. Analysts, however, saw the limited reactive actions, with little-to-no direct impact from sanction announcements. Although cyber-attacks on some NATO organizations did take place, they were not as widespread or impactful as expected. Lastly, it was thought that exposure to new and sophisticated exploits would increase and be used to weaken NATO nations - especially corporations in critical industries. However, analysts observed relatively common exploits deployed indiscriminately and opportunistically. Overall, with the wider industry expecting chaos, Darktrace analysts did not see the crisis taken advantage of to target wider businesses outside of Ukraine. Based on this comparison between expectations and reality, the conflict has demonstrated the danger of  falling prey to confirmation bias and the need to remain vigilant and expect the unexpected. It may be possible to say that cyberwar is ‘cold’ right now, however the element of surprise is always present, and it is better to be prepared to protect yourself and your organization.    

What to Expect from the Future

As cyberattacks continue to become less monetarily and physically costly, it is to be expected that they will increase in frequency. Even after a political ceasefire is established, hacking groups can harbour resentment and continue their attacks, though possibly on a smaller scale.  

Additionally, the longer this conflict continues, the more sophisticated hacking groups’s attacks may become. In one of their publications, Killnet shared with subscribers that they had created ‘network weaponry’ powerful enough to simultaneously take down five European countries (Figure 4) [20]. Whether or not this claim is true, it is vital to be prepared. The European Union and the United States have supported Ukraine since the start of the invasion, and the EU has also stated that it is considering providing further assistance to help Ukraine in cyberspace [21].

Figure 4- Snapshot of Killnet Telegram announcement

How to Protect Against these Attacks

In the face of wider conflict and cybersecurity tensions, it is crucial that organizations evaluate their security stack and practise the following: 

·       Know what your critical assets are and what software is running on them. 

·       Keep your software up to date. Prioritize patching critical and high vulnerabilities that allow remote code execution. 

·       Enforce Multifactor Authentication (MFA) to the greatest extent possible. 

·       Require the use of a password manager to generate strong and unique passwords for each separate account. 

·       Backup all the essential files on the cloud and external drives and regularly maintain them. 

·       Train your employees to recognize phishing emails, suspicious websites, infected links or other abnormalities to prevent successful compromise of email accounts. 

In order to prevent an organization from suffering damage due to one of the attacks mentioned above, a full-circle approach is needed. This defence starts with a thorough understanding of the attack surface to provide timely mitigation. This can be supported by Darktrace products: 

·       As shown throughout this blog, Darktrace DETECT and Darktrace/Email have several models relating to conflict-associated TTPs and attacks. These help to quickly alert security teams and provide visibility of anomalous behaviors.

·       Darktrace PREVENT/ASM helps to identify vulnerable external-facing assets. By patching and securing these devices, the risk of exploit is drastically reduced.

·       Darktrace RESPOND and RESPOND/Email can make targeted actions to a range of threats such as blocking incoming DDoS connections or locking malicious email links.

Thanks to the Darktrace Threat Intelligence Unit for their contributions to this blog.

Appendices 

Reference List

[1] https://www.atlanticcouncil.org/blogs/ukrainealert/vladimir-putins-ukraine-invasion-is-the-worlds-first-full-scale-cyberwar/ 

[2] https://www.reuters.com/article/us-ukraine-cybersecurity-idUSKCN0VY30K

[3] https://www.reuters.com/article/us-ukraine-cybersecurity-sandworm-idUSKBN0UM00N20160108

[4 & 11] https://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/ 

[5] https://www.scmagazine.com/analysis/ransomware/despite-hopes-for-decline-ransomware-attacks-increased-during-russia-ukraine-conflict

[6] https://ransomware.org/blog/has-the-ukraine-conflict-disrupted-ransomware-attacks/

[7] https://www.cfr.org/blog/financial-incentives-may-explain-perceived-lack-ransomware-russias-latest-assault-ukraine

[8] https://www.bleepingcomputer.com/news/security/hackers-use-contis-leaked-ransomware-to-attack-russian-companies/ 

[9] https://voi.id/en/technology/138937/hermetica-owner-from-cyprus-didnt-know-his-server-was-used-in-malicious-malware-attack-in-ukraine 

[10] https://www.reuters.com/article/ukraine-crisis-cyber-cyprus-idCAKBN2KT2QI 

[11] https://www.eset.com/int/about/newsroom/press-releases/research/eset-research-ukraine-hit-by-destructive-attacks-before-and-during-the-russian-invasion-with-hermet/ 

[12] https://blog.malwarebytes.com/threat-intelligence/2022/03/hermeticwiper-a-detailed-analysis-of-the-destructive-malware-that-targeted-ukraine/ 

[13] https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ 

[15] https://www.cisa.gov/uscert/ncas/alerts/aa22-057a 

[16] https://attack.mitre.org/groups/G0047/ 

[17] https://cyware.com/news/ukraine-cert-warns-of-increasing-attacks-by-armageddon-group-850081f8 

[18] https://www.bbc.co.uk/news/business-60521822

[19] https://foreignpolicy.com/2022/04/11/russia-cyberwarfare-us-ukraine-volunteer-hackers-it-army/

[20] https://t.me/itarmyofukraine2022

[21] https://www.csoonline.com/article/3664859/russian-ddos-attack-on-lithuania-was-planned-on-telegram-flashpoint-says.html

[19 & 20] https://flashpoint.io/blog/killnet-kaliningrad-and-lithuanias-transport-standoff-with-russia/ 

[21] https://presidence-francaise.consilium.europa.eu/en/news/member-states-united-in-supporting-ukraine-and-strengthening-the-eu-s-telecommunications-and-cybersecurity-resilience/ 

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
Rosa Jong
OSINT Analyst
Written by
Taisiia Garkava
Security Analyst

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June 24, 2026

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

applescript-led mac os intrusionDefault blog imageDefault blog image

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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June 24, 2026

A New Security Challenge: The Curious Case of Prompt Language Analysis

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Why prompt analysis is emerging as a key AI security challenge

If securing AI has been one of the defining cybersecurity conversations of the past year, prompt analysis is quickly becoming one of its most interesting frontiers.

Security leaders are under pressure to understand how AI is being used across the business. In some organizations, that means governing employee use of chatbots. In others, it means overseeing copilots embedded into SaaS platforms, monitoring coding assistants, or assessing the growing footprint of autonomous agents. However different these use cases may appear on the surface, they share a common factor: humans and machines are usually interacting with enterprise systems through language.  

How prompt language differs from traditional security telemetry

For years, defenders have become used to working with familiar forms of telemetry: email traffic, network connections, API calls, endpoint processes, authentication events. Prompt language is different. It is not simply another log source. It is an expression of intent, instruction, curiosity, urgency, and sometimes manipulation. It reflects the end-goal of a user or agent, but not always with enough surrounding context to interpret the risk correctly.

Why existing security approaches only partially explain prompt risk

A growing number of vendors are approaching the task of securing AI from the angle they know best. Perimeter vendors are extending web or browser controls into AI usage. Identity vendors are emphasizing agent permissions and access governance. Data security and DLP providers are focusing on content inspection and exfiltration risk. All of these perspectives matter, but individually can’t fully explain the problem.

The challenge with securing AI is not just that a new application category has emerged. It is that language has become a new operating layer in the enterprise.

Employees now use prompts to summarize documents, generate code, analyze spreadsheets, query internal knowledge, and trigger multi-step actions through agents. In each case, prompt language acts as the interface between human intent and machine execution. That makes prompts incredibly valuable from a security perspective as they can hint at misuse, policy violations, data exposure, or attempts to circumvent controls. However, they can also be deeply ambiguous when viewed in isolation. That ambiguity is the heart of the issue.

Prompts as behavioral signals, not just text to classify

A prompt by itself tells you what was asked. It does not necessarily tell you whether the request is expected, risky, accidental, or entirely legitimate in context. Two nearly identical prompts can carry very different meanings depending on the role and function of who issued them, what systems they can access, and what actions followed. In other words, prompts are not just text to classify. They are behavioral signals to interpret.

Example: How context changes prompt risk entirely

Consider a common enterprise scenario. An employee is pulled into a new project with an aggressive deadline. Almost overnight, their use of AI tools spikes. They begin prompting more frequently, working across unfamiliar documents, querying new data sources, and interacting with more systems than usual to accelerate delivery. Viewed narrowly, this may look suspicious. Prompt volume increases, file access patterns change, API and SaaS activity rise. From some vantage points, it may resemble insider risk or unmanaged AI usage.

But now add context. Imagine that, earlier that day, the employee received instructions from a senior leader asking them to support a time-sensitive initiative. Their communication history shows that this leader is a legitimate reporting-line superior. Their recent collaboration patterns align with the new project team. Their subsequent activity, while unusual for that individual’s baseline, is consistent with the business task they were assigned.

What initially looked like a risk event may actually be a normal response to business pressure. Without the surrounding context of communication, organizational relationships, and broader behavioral patterns, prompt activity alone could generate more noise than insight.

The reverse is also true. A prompt may appear benign on the surface while the context around it suggests elevated risk. A request that seems routine could originate from a compromised user, a newly connected external agent, a shadow AI workflow, or a user acting outside their normal role. The language itself may not contain anything obviously malicious, but the surrounding conditions may tell a very different story.

What security teams need to analyze prompts effectively

The future of prompt analysis is not just about understanding language. It is about understanding language in context.

To do that well, security teams need more than prompt inspection. They need to understand:

  • Who is issuing the prompt, whether human or agent
  • How that identity normally behaves across the enterprise
  • What systems, data, and workflows are connected to the interaction
  • Which relationships and communications explain the surrounding activity
  • Whether the downstream actions align with expected business behavior

When those layers are absent, prompt analysis can become another isolated control surface: useful in theory, but limited in practice. Security teams may detect unusual wording but miss the operational function behind it, overreact to benign changes in behavior, or miss subtle misuse because the prompt itself did not appear dangerous.

How organizations should think about prompt analysis going forward

Security teams have seen this pattern before. In the cloud, posture without runtime context left important gaps. In identity, access control without behavioral understanding missed misuse that looked legitimate on paper. In data security, content inspection without business context often created friction without resolving risk. AI is exposing the same lesson again: controls are strongest when they are coordinated, not isolated. As organizations work to secure AI and identify gaps across their security operations, prompt analysis will become an increasingly important source of insight, but only as part of a broader strategy.

Prompt analysis will undoubtedly become more common, as prompts are one of the clearest windows into how people and agents are using AI systems. However, what matters most is not simply collecting prompts or filtering dangerous phrases, but being able to place that language inside a wider behavioral and operational picture.

Organizations that already have a broader understanding of how work gets done across the enterprise will be better positioned to make sense of prompt language as this category matures. They will be better able to distinguish urgency from abuse, experimentation from exfiltration, and productive AI adoption from hidden risk.

Figure 1: Darktrace / SECURE AI reconstructs the full sequence of events, showing every user and agent interaction in context, with risky prompts highlighted and categorized, including PII, sensitive data, and other policy violations.

At Darktrace, this is the key lesson emerging from the market: prompt language does matter, but it does not stand alone. It is most valuable when treated as a new behavioral input that can enrich understanding across the enterprise, not as a self-contained source of truth.

Why prompts become less useful when analyzed in isolation

The curious case of prompt language analysis, then, is this: the more important prompts become, the less useful they are in a vacuum.

The real opportunity is not just to see what was asked. It is to understand why it was asked, what it meant in that moment, and what happened next.

For a deeper look at how organizations are approaching this challenge from the strengths of prompt analysis to its limitations in isolation see Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches, which expands on the role prompt-level controls play within a broader, context-driven security strategy.

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About the author
Nabil Zoldjalali
VP, Field CISO
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