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October 18, 2022

Kill Chain Insights: Detecting AutoIT Malware Compromise

Discover how AutoIt malware operates and learn strategies to combat this emerging threat in our latest blog post.
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
Joel Davidson
Cyber Analyst
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18
Oct 2022

Introduction 

Good defence is like an onion, it has layers. Each part of a security implementation should have checks built in so that if one wall is breached, there are further contingencies. Security aficionados call this ‘defence in depth’, a military concept introduced to the cyber-sphere in 2009 [1]. Since then, it has remained a central tenet when designing secure systems, digital or otherwise [2]. Despite this, the attacker’s advantage is ever-present with continued development of malware and zero-day exploits. No matter how many layers a security platform has, how can organisations be expected to protect against a threat they do not know or even understand? 

Take the case of one Darktrace customer, a government-contracted manufacturing company located in the Americas. This company possesses a modern OT and IT network comprised of several thousand devices. They have dozens of servers, a few of which host Microsoft Exchange. Every week, these few mail servers receive hundreds of malicious payloads which will ultimately attempt to make their way into over a thousand different inboxes while dodging different security gateways. Had the RESPOND portion of Darktrace for Email been properly enabled, this is where the story would have ended. However, in June 2022 an employee made an instinctual decision that could have potentially cost the company its time, money, and reputation as a government contractor. Their crime: opening an unknown html file attached to a compelling phishing email. 

Following this misstep, a download was initiated which resulted in compromise of the system via vulnerable Microsoft admin tools from endpoints largely unknown to conventional OSINT sources. Using these tools, further malicious connectivity was accomplished before finally petering out. Fortunately, their existing Microsoft security gateway was up to date on the command and control (C2) domains observed in this breach and refused the connections.

Darktrace detected this activity at every turn, from the initial email to the download and subsequent attempted C2. Cyber AI Analyst stitched the events together for easy understanding and detected Indicators of Compromise (IOCs) that were not yet flagged in the greater intelligence community and, critically, did this all at machine speed. 

So how did the attacker evade action for so long? The answer is product misconfiguration - they did not refine their ‘layers’.  

Attack Details

On the night of June 8th an employee received a malicious email. Darktrace detected that this email contained a html attachment which itself contained links to endpoints 100% rare to the network. This email also originated from a never-before-seen sender. Although it would usually have been withheld based on these factors, the customer’s Darktrace/Email deployment was set to Advisory Mode meaning it continued through to the inbox. Late the next day, this user opened the attachment which then routed them to the 100% rare endpoint ‘xberxkiw[.]club’, a probable landing page for malware that did not register on OSINT available at the time.

Figure 1- Popular OSINT VirusTotal showing zero hits against the rare endpoint 

Only seconds after reaching the endpoint, Darktrace detected the Microsoft BITS user agent reaching out to another 100% rare endpoint ‘yrioer[.]mikigertxyss[.]com’, which generated a DETECT/Network model breach, ‘Unusual BITS Activity’. This was immediately suspicious since BITS is a deprecated and insecure windows admin tool which has been known to facilitate the movement of malicious payloads into and around a network. Upon successfully establishing a connection, the affected device began downloading a self-professed .zip file. However, Darktrace detected this file to be an extension-swapped .exe file. A PCAP of this activity can be seen below in Figure 2.

Figure 2- PCAP highlighting BITs service connections and false .zip (.exe) download

This activity also triggered a correlating breach of the ‘Masqueraded File Transfer’ model and pushed a high-fidelity alert to the Darktrace Proactive Threat Notification (PTN) service. This ensured both Darktrace and the customer’s SOC team were alerted to the anomalous activity.

At this stage the local SOC were likely beginning their triage. However further connections were being made to extend the compromise on the employee’s device and the network. The file they downloaded was later revealed to be ‘AutoIT3.exe’, a default filename given to any AutoIt script. AutoIt scripts do have legitimate use cases but are often associated with malicious activity for their ability to interact with the Windows GUI and bypass client protections. After opening, these scripts would launch on the host device and probe for other weaknesses. In this case, the script may have attempted to hunt passwords/default credentials, scan the local directory for common sensitive files, or scout local antivirus software on the device. It would then share any information gathered via established C2 channels.  

After the successful download of this mismatched MIME type, the device began attempting to further establish C2 to the endpoint ‘dirirxhitoq[.]kialsoyert[.]tk’. Even though OSINT still did not flag this endpoint, Darktrace detected this outreach as suspicious and initiated its first Cyber AI Analyst investigation into the beaconing activity. Following the sixth connection made to this endpoint on the 10th of June, the infected device breached C2 models, such as ‘Agent Beacon (Long Period)’ and ‘HTTP Beaconing to Rare Destination’. 

As the beaconing continued, it was clear that internal reconnaissance from AutoIt was not widely achieved, although similar IOCs could be detected on at least two other internal devices. This may represent other users opening the same malicious email, or successful lateral movement and infection propagation from the initial user/device. However comparatively, these devices did not experience the same level of infection as the first employee’s machine and never downloaded any malicious executables. AutoIt has a history of being used to deliver information stealers, which suggests a possible motivation had wider network compromise been successful [3].

Thankfully, after the 10th of June no further exploitation was observed. This was likely due to the combined awareness and action brought by the PTN alerting, static security gateways and action from the local security team. The company were protected thanks to defence in depth.  

Darktrace Coverage

Despite this, the role of Darktrace itself cannot be understated. Darktrace/Email was integral to the early detection process and provided insight into the vector and delivery methods used by this attacker. Post-compromise, Darktrace/Network also observed the full range of suspicious activity brought about by this incursion. In particular, the AI analyst feature played a major role in reducing the time for the SOC team to triage by detecting and flagging key information regarding some of the earliest IOCs.

Figure 3- Sample information pulled by AI analyst about one of the involved endpoints

Alongside the early detection, there were several instances where RESPOND/Network would have intervened however autonomous actions were limited to a small test group and not enabled widely throughout the customer’s deployment. As such, this activity continued unimpeded- a weak layer. Figure 4 highlights the first Darktrace RESPOND action which would have been taken.

Figure 4- Upon detecting the download of a mismatched mime from a rare endpoint, Darktrace RESPOND would have blocked all connections to the rare endpoint on the relevant port in a targeted manner

This Darktrace RESPOND action provides a precise and limited response by blocking the anomalous file download. However, after continued anomalous activity, RESPOND would have strengthened its posture and enforced stronger curbs across the wider anomalous activity. This stronger enforcement is a measure designed to relegate a device to its established norm. The breach which would generate this response can be seen below:

Figure 5- After a prolonged period of anomalous activity, Darktrace RESPOND would have stepped in to enforce the typical pattern of life observed on this device

Although Darktrace RESPOND was not fully enabled, this company had an extra layer of security in the PTN service, which alerted them just minutes after the initial file download was detected, alongside details relevant to the investigation. This ensured both Darktrace analysts and their own could review the activity and begin to isolate and remediate the threat. 

Concluding Insights

Thankfully, with multiple layers in their security, the customer managed to escape this incident largely unscathed. Quick and comprehensive email and network detection, customer alerting and local gateway blocking C2 connections ensured that the infection did not have leeway to propagate laterally throughout the network. However, even though this infection did not lead to catastrophe, the fact that it happened in the first place should be a learning point. 

Had RESPOND/Email been properly configured, this threat would have been stopped before reaching its intended recipients, removing the need to rely on end-users as a security measure. Furthermore, had RESPOND/Network been utilized beyond a limited test group, this activity would have been blocked at every other step of the network-level kill chain. From the anomalous MIME download to the establishment of C2, Darktrace RESPOND would have been able to effectively isolate and quarantine this activity to the host device, without any reliance on slow-to-update OSINT sources. RESPOND allows for the automation of time-sensitive security decisions and adds a powerful layer of defence that conventional security solutions cannot provide. Although it can be difficult to relinquish human ownership of these decisions, doing so is necessary to prevent unknown attackers from infiltrating using unknown vectors to achieve unknown ends.  

In conclusion, this incident demonstrates an effective case study around detecting a threat with novel IOCs. However, it is also a reminder that a company’s security makeup can always be improved. Overall, when building security layers in a company’s ‘onion’, it is great to have the best tools, but it is even greater to use them in the best way. Only with continued refining can organisations guarantee defence in depth. 

Thanks to Connor Mooney and Stefan Rowe for their contributions.

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location 

·      Compromise / Agent Beacon (Long Period) 

·      Compromise / HTTP Beaconing to Rare Destination 

·      Device / Large Number of Model Breaches 

·      Device / Suspicious Domain 

·      Device / Unusual BITS Activity 

·      Enhanced Monitoring: Anomalous File / Masqueraded File Transfer 

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
Joel Davidson
Cyber Analyst

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May 20, 2026

State of AI Cybersecurity 2026: 77% of security stacks include AI, but trust is lagging

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Findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

AI is a contributing member of nearly every modern cybersecurity team. As we discussed earlier in this blog series, rapid AI adoption is expanding the attack surface in ways that security professionals have never before experienced while also empowering attackers to operate at unprecedented speed and scale. It’s only logical that defenders are harnessing the power of AI to fight back.

After all, AI can help cybersecurity teams spot the subtle signs of novel threats before humans can, investigate events more quickly and thoroughly, and automate response. But although AI has been widely adopted, this technology is also frequently misunderstood, and occasionally viewed with suspicion.

For CISOs, the cybersecurity marketplace can be noisy. Making sense of competing vendors’ claims to distinguish the solutions that truly deliver on AI’s full potential from those that do not isn’t always easy. Without a nuanced understanding of the different types of AI used across the cybersecurity stack, it is difficult to make informed decisions about which vendors to work with or how to gain the most value from their solutions. Many security leaders are turning to Managed Security Service Providers (MSSPs) for guidance and support.

The right kinds of AI in the right places?

Back in 2024, when we first conducted this annual survey, more than a quarter of respondents were only vaguely familiar with generative AI or hadn’t heard of it at all. Today, GenAI plays a role in 77% of security stacks. This percentage marks a rapid increase in both awareness and adoption over a relatively short period of time.

According to security professionals, different types of AI are widely integrated into cybersecurity tooling:

  • 67% report that their organization’s security stack uses supervised machine learning
  • 67% report that theirs uses agentic AI
  • 58% report that theirs uses natural language processing (NLP)
  • 35% report that theirs uses unsupervised machine learning

But their responses suggest that organizations aren’t always using the most valuable types of AI for the most relevant use cases.

Despite all the recent attention AI has gotten, supervised machine learning isn’t new. Cybersecurity vendors have been experimenting with models trained on hand-labeled datasets for over a decade. These systems are fed large numbers of examples of malicious activity – for instance, strains of ransomware – and use these examples to generalize common indicators of maliciousness – such as the TTPs of multiple known ransomware strains – so that the models can identify similar attacks in the future. This approach is more effective than signature-based detection, since it isn’t tied to an individual byte sequence or file hash. However, supervised machine learning models can miss patterns or features outside the training data set. When adversarial behavior shifts, these systems can’t easily pivot.

Unsupervised machine learning, by contrast, can identify key patterns and trends in unlabeled data without human input. This enables it to classify information independently and detect anomalies without needing to be taught about past threats. Unsupervised learning can continuously learn about an environment and adapt in real time.

One key distinction between supervised and unsupervised machine learning is that supervised learning algorithms require periodic updating and re-training, whereas unsupervised machine learning trains itself while it works.

The question of trust

Even as AI moves into the mainstream, security professionals are eyeing it with a mix of enthusiasm and caution. Although 89% say they have good visibility into the reasoning behind AI-generated outputs, 74% are limiting AI’s ability to take autonomous action in their SOC until explainability improves. 86% do not allow AI to take even small remediation actions without human oversight.

This model, commonly known as “human in the loop,” is currently the norm across the industry. It seems like a best-of-both-worlds approach that allows teams to experience the benefits of AI-accelerated response without relinquishing control – or needing to trust an AI system.

Keeping humans somewhat in the loop is essential for getting the best out of AI. Analysts will always need to review alerts, make judgement calls, and set guardrails for AI's behavior. Their input helps AI models better understand what “normal” looks like, improving their accuracy over time.

However, relying on human confirmation has real costs – it delays response, increases the cognitive burden analysts must bear, and creates potential coverage gaps when security teams are overwhelmed or unavailable. The traditional model, in which humans monitor and act on every alert, is no longer workable at scale.

If organizations depend too heavily on in-the-loop humans, they risk recreating the very problem AI is meant to solve: backlogs of alerts waiting for analyst review. Removing the human from the loop can buy back valuable time, which analysts can then invest in building a proactive security posture. They can also focus more closely on the most critical incidents, where human attention is truly needed.

Allowing AI to operate autonomously requires trust in its decision-making. This trust can be built gradually over time, with autonomous operations expanding as trust grows. But it also requires knowledge and understanding of AI — what it is, how it works, and how best to deploy it at enterprise scale.

Looking for help in all the right places

To gain access to these capabilities in a way that’s efficient and scalable, growing numbers of security leaders are looking for outsourced support. In fact, 85% of security professionals prefer to obtain new SOC capabilities in the form of a managed service.

This makes sense: Managed Security Service Providers (MSSPs) can deliver deep, continuously available expertise without the cost and complexity of building an in-house team. Outsourcing also allows organizations to scale security coverage up or down as needs change, stay current with evolving threats and regulatory requirements, and leverage AI-native detection and response without needing to manage the AI tools themselves.

Preferences for MSSP-delivered security operations are particularly strong in the education, energy (87%), and healthcare sectors. This makes sense: all are high-value targets for threat actors, and all tend to have limited cybersecurity budgets, so the need for a partner who can deliver affordable access to expertise at scale is strong. Retailers also voiced a strong preference for MSSP-delivered services. These companies are tasked with managing large volumes of consumer personal and financial data, and with transforming an industry traditionally thought of as a late adopter to a vanguard of cyber defense. Technology companies, too, have a marked preference for SOC capabilities delivered by MSSPs. This may simply be because they understand the complexity of the threat landscape – and the advantages of specialized expertise — so well.

In order to help as many organizations as possible – from major enterprises to small and midmarket companies – benefit from enterprise-grade, AI-native security, Darktrace is making it easier for MSSPs to deliver its technology. The ActiveAI Security Portal introduces an alert dashboard designed to increase the speed and efficiency of alert triage, while a new AI-powered managed email security solution is giving MSSPs an edge in the never-ending fight against advanced phishing attacks – helping partners as well as organizations succeed on the frontlines of cyber defense.

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

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May 20, 2026

When Open Source Is Weaponized: Analysis of a Trojanized 7 Zip Installer

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Background of the malicious 7-Zip installer, and assessing its Impact

Early in 2026, external researchers disclosed a malicious distribution campaign leveraging a trojanized installer masquerading itself as a legitimate 7‑Zip utility. Evidence suggests the campaign was active as of January 2026, during which victims were served a fake installer from 7zip[.]com, a highly convincing typo-squatted domain impersonating the official 7‑Zip distribution site (7-zip[.]org).

Initial access is typically achieved through social engineering and search‑engine abuse, including YouTube tutorial content that explicitly referenced the impersonated domain as the download source. Notably, several reports observed the installer delivered a modified but functional build of 7‑Zip (7zfm.exe) to reduce suspicion and preserve expected user behavior.

However, the installer also dropped additional payloads, such as Uphero.exe, hero.exe, and hero.dll, which are not part of the legitimate 7‑Zip software package. Once installed and executed, these payloads allow the attacker to establish persistence and configure the infected host as a proxy node under their control. This facilitates malicious activities such as traffic relaying, anonymizing infrastructure, and the delivery of secondary payloads [1] [2].

Overall, this attack illustrates a proxyware-style attack that abuses implicit trust in widely deployed third‑party tools while exploiting unconventional delivery vectors such as instructional media. By closely imitating legitimate software behavior and branding, the threat actors significantly reduced user suspicion and increased the likelihood of widespread, undetected compromise.

Threat overview

Darktrace observed multiple customers affected by the malicious 7‑Zip installer between January 12 and January 22, impacting organizations across the Americas (AMS), Asia‑Pacific & Japan (APJ), and Europe, the Middle East, and Africa (EMEA) regions. The activity targeted customers across various sectors, including Human health and social work activities, Manufacturing, Education, and Information and communication.

The following use case highlights a device on one customer network making external connections associated with malicious 7-Zip update activity observed between  January 7 and January 18, 2026.  This behavior included connectivity to the malicious domain 7zip[.]com, followed by command-and control (C2) activity involving "smshero"-themed domains, as well as outbound proxy connections over ports 1000 and 1002.  

Initial Connectivity to 'update[.]7zip[.]com':

Initial Beaconing to Young Endpoint alert behavior, involving the known tunnel/proxy endpoint ‘79.127.221[.]47’.
Figure 1: Initial Beaconing to Young Endpoint alert behavior, involving the known tunnel/proxy endpoint ‘79.127.221[.]47’.

Starting on January 7, Darktrace / NETWORK detected the device making repeated beaconing connections to the endpoint 79.127.221[.]47 over the destination port 1000. The use of this port aligns with open-source intelligence (OSINT) reporting that hero[.]exe establishes outbound proxy connections via non-standard ports such as 1000 and 1002 [1].

Darktrace observed TLS beaconing alerts to the known trojanized installer, update[.]7zip[.]com · 98.96.229[.]19, over port 443 on January 7th.
Figure 2: Darktrace observed TLS beaconing alerts to the known trojanized installer, update[.]7zip[.]com · 98.96.229[.]19, over port 443 on January 7th.

Later the same day, the device initiated TLS beaconing to the endpoint update.7zip[.]com. This is more than likely a common source of compromise, where victims unknowingly installed a modified build of the tool alongside additional malicious components. The campaign then progressed into the next attack phase, marked by established connectivity to various C2 domains.

Beaconing Activity to "smshero"-themed domains

Darktrace subsequently observed the same infected device connecting to various C2 domains used to retrieve configuration data. As such, these external hostnames were themed around the string “smshero”, for example ‘smshero[.]co’.

On January 8th, Darktrace observed SSL beaconing to a rare destination which was attributed to a known ‘config/control domain’, nova[.]smshero[.]ai.
Figure 3: On January 8th, Darktrace observed SSL beaconing to a rare destination which was attributed to a known ‘config/control domain’, nova[.]smshero[.]ai.

The following day, on January 8, the device exhibited its first connectivity to a "smshero"-themed endpoint, which has since been identified as being associated with rotating C2 servers [1] [3]. Similar beaconing activity continued over the following days, with Darktrace identifying C2 connectivity to update[.]7zip[.]com over port 443, alongside additional connections to “smshero”‑themed endpoints such as zest.hero-sms[.]ai, flux.smshero[.]cc, and glide.smshero[.]cc between January 9 and January 15.

Darktrace later observed continued beaconing alerts over a 4-day interval to additional rare destinations attributed to a known ‘config/control domain’, zest[.]hero-sms[.]ai & glide[.]smshero[.]cc.
Figure 4: Darktrace later observed continued beaconing alerts over a 4-day interval to additional rare destinations attributed to a known ‘config/control domain’, zest[.]hero-sms[.]ai & glide[.]smshero[.]cc.

Proxied connectivity over destination ports

The primary objective of this campaign is believed to be proxyware, whereby third-party traffic is routed through victim devices to potentially obfuscate malicious activity. Devices were also observed communicating with rare external IPs hosted on Cloudflare and DataCamp Limited ASNs, establishing outbound proxy connections over the non-standard ports 1000 and 1002 [1].

OSINT sources also indicate that connections over these ports leveraged an XOR-encoded protocol (key 0x70) designed to obscure control messages. While the end goal of the campaign remains unclear, residential proxy networks can be abused to evade security rules and facilitate further unauthorized activities, including phishing and malware distribution [1][3].

Specifically, on January 8, Darktrace observed the device engaging in low-and-slow data exfiltration to the IP 79.127.221[.]47, which had first been observed the previous day, over port 1000. Proxyware typically installs an agent that routes third‑party traffic through an end-user’s device, effectively  turning it into a residential proxy exit node. This activity likely represents the system actively communicating outbound data to an entity that controls its behavior.

Figure 5: Darktrace later observed a ‘Low and Slow Exfiltration to IP’ alert, involving the known tunnel/proxy endpoint ‘79.127.221[.]47’.

Similar activity continued between January 10 and January 18, with Darktrace detecting threat actors attempting to exfiltrate significant volumes of data to 79.127.221[.]47 over destination port 1000.

Throughout the course of this incident, Darktrace’s Cyber AI Analyst launched several autonomous investigations, analyzing each anomalous event and ultimately painting a detailed picture of the attack timeline. These investigations correlated multiple incidents based on Darktrace detections observed between January 7 and January 19. Cyber AI Analyst identified anomalous variables such as repeated connections to unusual endpoints involving data uploads and downloads, with particular emphasis on HTTP and SSL connectivity.

Darktrace AI Analyst Coverage, showcasing multiple incident events that occurred on January 7th & 8th, highlighting associated malicious 7-zip behaviors.
Figure 6: Darktrace AI Analyst Coverage, showcasing multiple incident events that occurred on January 7th & 8th, highlighting associated malicious 7-zip behaviors.
Darktrace AI Analyst Endpoint Details from the given ‘Unusual Repeated Connections’ Incident Event, including the known tunnel/proxy endpoint.
Figure 7: Darktrace AI Analyst Endpoint Details from the given ‘Unusual Repeated Connections’ Incident Event, including the known tunnel/proxy endpoint.
 Darktrace AI Analyst Coverage, showcasing additional incident events that occurred on January 12th through 18th, highlighting malicious 7-zip behaviors and SSL connectivity.
Figure 8: Darktrace AI Analyst Coverage, showcasing additional incident events that occurred on January 12th through 18th, highlighting malicious 7-zip behaviors and SSL connectivity.

Darktrace’s Autonomous Response

At several stages throughout the attack, Darktrace implemented Autonomous Response actions to help contain the suspicious activity as soon as it was identified, providing the customer’s security team with additional time to investigate and remediate. Between January 7 and January 18, Darktrace blocked a wide range of malicious activity, including beaconing connections to unusual endpoints, small data exfiltration attempts, and larger egress efforts, ultimately preventing the attacker from progressing through multiple stages of the attack or achieving their objectives.

Darktrace Autonomous Response Action Coverage showcasing connection block connection events including various endpoints that occurred on January 7th.
Figure 9: Darktrace Autonomous Response Action Coverage showcasing connection block connection events including various endpoints that occurred on January 7th.
Darktrace Antigena (Autonomous Response) Model Alert Coverage, showcasing a Antigena Suspicious Activity Block alert occurred on January 10th as a result of the Low and Slow Exfiltration to IP model alert.
Figure 10: Darktrace Antigena (Autonomous Response) Model Alert Coverage, showcasing a Antigena Suspicious Activity Block alert occurred on January 10th as a result of the Low and Slow Exfiltration to IP model alert.
Figure 11: Additional Darktrace Antigena (Autonomous Response) Model Alert Coverage, showcasing a Antigena Large Data Volume Outbound Block alert occurred on January 18th as a result of the Uncommon 1 GiB Outbound model alert.

Conclusion

The malicious 7‑Zip installer underscores how attackers continue to weaponize trust in widely used, legitimate software to gain initial access while evading user suspicion. By exploiting familiar and commonly installed services, this type of attack demonstrates that even routine actions, such as installing compression software, can become high‑risk events when defenses or user awareness are insufficient.

This campaign further emphasizes the urgent need for strict software validation and continuous network monitoring. Modern threats no longer rely solely on obscure tools or overtly malicious behavior. Instead, they increasingly blend seamlessly into everyday operations, making detection more challenging.

In this case, Darktrace / NETWORK was able to identify the anomalous activity and Autonomous Response actions in a timely manner, enabling the customer to be quickly notified and providing crucial additional time to investigate further.

In summary, the abuse of a trojanized 7‑Zip installer highlights a concerning shift in modern threat tactics, where trusted and widely deployed tools can serve as primary delivery mechanisms for system compromise. This reality reinforces that proactive detection, continuous monitoring, and strong security awareness are not optional but essential.

Credit to Justin Torres, Senior Cyber Analyst, David Moreira da Silva, Cyber Analyst, Emma Foulger, Global Threat Research Operations Lead.

Edited by Ryan Traill (Content Manager)

Appendices

References

1. https://www.malwarebytes.com/blog/threat-intel/2026/02/fake-7-zip-downloads-are-turning-home-pcs-into-proxy-nodes

2. https://www.tomshardware.com/tech-industry/cyber-security/unofficial-7-zip-com-website-served-up-malware-for-10-days-files-turned-pcs-into-a-proxy-botnet

3. https://blog.lukeacha.com/2026/01/beware-of-fake-7zip-installer-upstage.html

4. https://www.bleepingcomputer.com/news/security/malicious-7-zip-site-distributes-installer-laced-with-proxy-tool/

5. https://customerportal.darktrace.com/guides/antigena-network-model-actions

Darktrace Model Detections

·      Anomalous Connection / Data Sent to Rare Domain

·      Anomalous Connection / Low and Slow Exfiltration to IP

·      Anomalous Connection / Multiple Failed Connections to Rare Endpoint

·      Anomalous Connection / Uncommon 1 GiB Outbound

·      Anomalous Server Activity / Rare External from Server

·      Compromise / Agent Beacon (Long Period)

·      Compromise / Beacon for 4 Days

·      Compromise / Beacon to Young Endpoint

·      Compromise / Beaconing Activity To External Rare

·      Compromise / High Volume of Connections with Beacon Score

·      Compromise / Large Number of Suspicious Failed Connections

·      Compromise / Large Number of Suspicious Successful Connections

·      Compromise / Repeating Connections Over 4 Days

·      Compromise / SSL Beaconing to Rare Destination

·      Compromise / Suspicious TLS Beaconing To Rare External

·      Device / Large Number of Model Alerts

·      Unusual Activity / Unusual External Activity

Cyber AI Analyst Coverage

·      Unusual Repeated Connections

·      Unusual Repeated Connections to Multiple Endpoints

·      Possible HTTP Command and Control

·      Possible HTTP Command and Control to Multiple Endpoints

·      Suspicious Remote Service Control Activity

·      Possible SSL Command and Control to Multiple Endpoints

Indicators of Compromise

IoC - Type - Description + Confidence

·      7zip[.]com – Hostname – C2 Endpoint

·      flux[.]smshero[.]co - Hostname - C2 Endpoint

·      neo[.]herosms[.]co - Hostname - C2 Endpoint

·      nova[.]smshero[.]ai - Hostname - C2 Endpoint

·      zest[.]hero-sms[.]ai -  Hostname - C2 Endpoint

·      soc[.]hero-sms[.]co - Hostname - C2 Endpoint

·      pulse[.]herosms[.]cc - Hostname - C2 Endpoint

·      glide[.]smshero[.]cc - Hostname - C2 Endpoint

·      prime[.]herosms[.]vip - Hostname - C2 Endpoint

·      172.96.115[.]226 - IP Address - C2 Endpoint

·      79.127.221[.]47:1002 – IP Address/Port - Proxy Endpoint

·      84.17.37[.]1:1002 - IP Address/Port - Proxy Endpoint

MITRE ATT&CK Mapping

Technique Name - Tactic - ID - Sub-Technique of

·      Exfiltration Over C2 Channel - EXFILTRATION - T1041

·      Scheduled Transfer - EXFILTRATION - T1029

·      Automated Exfiltration - EXFILTRATION - T1020

·      Data Transfer Size Limits - EXFILTRATION - T1030

·      External Proxy - COMMAND AND CONTROL - T1090.002 - T1090

·      Non-Application Layer Protocol - COMMAND AND CONTROL - T1095

·      Non-Standard Port - COMMAND AND CONTROL - T1571

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

·      Exploit Public-Facing Application - INITIAL ACCESS - T1190

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

·      Application Layer Protocol - COMMAND AND CONTROL - T1071

·      Man in the Browser - COLLECTION - T1185

·      Browser Extensions - PERSISTENCE - T1176

·      Encrypted Channel - COMMAND AND CONTROL - T1573

·      Fallback Channels - COMMAND AND CONTROL - T1008

·      Multi-Stage Channels - COMMAND AND CONTROL - T1104

·      Supply Chain Compromise - INITIAL ACCESS ICS - T0862

·      Commonly Used Port - COMMAND AND CONTROL ICS - T0885

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Justin Torres
Cyber Analyst
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