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April 10, 2023

Detecting Malicious Email Activity & AI Impersonating

Discover how two different phishing attempts from some known and unknown senders used a payroll diversion and credential sealing box link to harm users.
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
Isabelle Cheong
Cyber Security Analyst
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10
Apr 2023

Social engineering has become widespread in the cyber threat landscape in recent years, and the near-universal use of social media today has allowed attackers to research and target victims more effectively. Social engineering involves manipulating users to carry out actions such as revealing sensitive information like login credentials or credit card details. It can also lead to user account compromises, causing huge disruption to an organization’s digital estate. 

As people use social media platforms not only for personal reasons, but also for business purposes, attackers gain information they can exploit in social engineering attacks. For example, a threat actor may attempt to impersonate a known individual or legitimate service to take advantage of a user’s established trust. This is a highly successful method of social engineering because mimicking known contacts makes it difficult for traditional security tools that rely on deny-lists to detect the attack.

In October 2022, Darktrace identified and responded to two separate malicious email campaigns in which threat actors attempted to impersonate known contacts in an effort to compromise customer devices. As it learns the normal behavior of every user in the email system, Darktrace was able to instantly detect these threats and mitigate them autonomously, preventing significant disruption to the customer networks.

Payroll Diversion Fraud Attempt Impersonating a Former Employee 

While a customer in the Canadian energy sector was trialing Darktrace in October 2022, Darktrace/Email™ identified a suspicious email seemingly sent from an employee within the organization. The email was sent to the Senior Director of Human Resources (HR) with a subject line of “Change in payroll Direct Deposit.” The email requested a change in bank account information for an employee. However, Darktrace recognized that the sender was using a free mail address that contained random letters, indicating it may have been algorithmically generated. Since this incident occurred during a trial, Darktrace/Email was not configured to take action. Otherwise, it would have prevented the email from landing in the inbox. In this case though, the email went through, bypassing all other security tools in place.

Although the email was from an unknown sender, the HR director believed the email could have been legitimate as the employee who appeared to be the sender had left the organization seven days prior and no longer had access to their corporate email account. However, after reviewing it in the Darktrace/Email dashboard, the customer grew suspicious and contacted the former employee directly to verify if the request was legitimate. The former employee validated the suspicions by confirming they had sent no such email.

Further investigation by the customer revealed that the former employee had been vocal about their departure on various social media platforms. This gave threat actors valuable information to believably impersonate the former employee and defraud the organization. 

Such attempts to target organizations’ HR departments and divert payroll are common tactics for cyber-criminals and are often identified by Darktrace/Email across the customer base. Darktrace/Email is able to instantly identify the indicators associated with these spoofing attempts and immediately bring them to the attention of the customer’s security team. 

Using Legitimate File Sharing Service to Share a Phishing Link 

On October 7, 2022, a customer in the Singaporean construction sector was targeted by a phishing campaign attempting to impersonate a law firm known to the organization. Almost 200 employees received an email with the subject line “Accepted: Valuation Agreement.” 

Figure 1: Sample of an UI view of the message held showing anomaly indicators, history, association, and validation.

Four days earlier, Darktrace observed communication between another email address associated with the law firm and an employee of the customer. Darktrace/Email noted that it was the first time this correspondent had sent emails to the customer. 

Figure 2: Metrics showing how well the sender’s domain is known within the digital environment.

The emails contained a highly unusual link to a file sharing service, (hxxps://ssvilvensstokes[.]app[.]box[.]com/notes), hidden behind the text “PREVIEW OR PRINT COPY OF DOCUMENT HERE.” Darktrace analysts investigated this event further and found that around 30 similar URLs had been identified as suspicious using OSINT security tools in October 2022, suggesting the customer was not the only target of this phishing campaign.

Figure 3: Preview of the phishing email’s body.
Figure 4: Darktrace’s evaluation of the link contained in the phishing email.

Additional OSINT work revealed that the link directed to a website which appeared to host a PDF file named “Valuation Agreement.” The recipient would then be prompted to follow another link (hulking-citrine-krypton[.]glitch[.]me), again hidden behind the text “OPEN OR ACCESS DOCUMENT HERE” to view the file. Subsequently, the user would be prompted to enter their Microsoft 365 credentials. 

Figure 5: The page displayed when the phishing link was clicked, viewed in a sandbox environment.
Figure 6: Example of a page shown when recipient clicks the second link, accessing “hulking-citrine-krypton[.]glitch[.]me”. 

This page contained the text “This document has been scanned for viruses by Norton Antivirus Security.” This is another example of threat actors’ employing social engineering techniques by impersonating well-known brands, such as established security vendors, to gain the trust of users and increase their likelihood of success.

It is highly probable that a real employee of the law firm had their account hijacked and that a malicious actor was exploiting it to send out these phishing emails en masse as part of a supply chain attack. In such cases, malicious actors rely on their targets’ trust of known contacts to not question departures from their normal conversations. 

Darktrace was able to instantly detect multiple anomalies in these emails, despite the fact that they were seemingly sent by known correspondents. The activity detected automatically triggered model breaches associated with unexpected and visually prominent links. As a result, Darktrace/Email responded by locking the link, stopping users from being able to click it.

Darktrace subsequently identified additional emails from this sender attempting to target other recipients within the company, triggering the model breaches associated with a surge in email sending indicative of a phishing campaign. In response, Darktrace/Email autonomously acted and filed these emails as junk. As more emails were detected across the customer’s environment, the anomaly score of the sender increased and Darktrace ultimately held back over 160 malicious emails, safeguarding recipients from potential account compromise.           

The following Darktrace/Email models were breached throughout the course of this phishing campaign:

  • Unusual/Sender Surge 
  • Unusual/Undisclosed Recipients 
  • Antigena Anomaly 
  • Association/Unlikely Recipient Association 
  • Link/Low Link Association 
  • Link/Visually Prominent Link 
  • Link/Visually Prominent Link Unexpected For Sender 
  • Unusual/New Sender Wide Distribution
  • Unusual/Undisclosed Recipients + New Address Known Domain

Conclusion

Social engineering plays a role in many of the major threats challenging current email cyber security, as attackers can use it to manipulate users into transferring money, revealing credentials, clicking malicious links, and more. 

The above threat stories happened before language generating AI became mainstream with the release of ChatGPT in December 2022. Now, it is even easier for malicious actors to generate sophisticated social engineering emails. By using social media posts as input, social engineering emails written by generative AI can be highly targeted and produced at scale. They often avoid the flags users are trained to look for, like poor grammar and spelling mistakes, and can hide payloads or forgo them entirely.

To mitigate the risk of possible social engineering attempts, it is recommended that organizations implement social media policies that advise employees to be cautious of what they post online and enact procedures to verify if fund transfer requests are legitimate.

Yet these policies are not enough on their own. Darktrace/Email can identify suspicious email traits, whether an email is sent from a known correspondent or an unknown sender. With Self-Learning AI, it knows an organization’s users better than any impersonator could. In this way, Darktrace/Email detects anomalies within emails and neutralizes malicious components at machine-speed, stopping attacks at their earliest stages, before employees fall victim. 

Appendices

List of Indicators of Compromise (IoCs)

Domain:

hxxps://ssvilvensstokes[.]app[.]box[.]com/notes/*?s=* - 1st external link (seen in email)

hxxps://hulking-citrine-krypton[.]glitch[.]me/flk.html - 2nd external link, masked behind “OPEN OR ACCESS DOCUMENT HERE”

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
Isabelle Cheong
Cyber Security Analyst

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June 22, 2026

Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations

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Darktrace joins the OpenAI Daybreak Cyber Partner Program

Today, we announced that Darktrace is joining the OpenAI Daybreak Cyber Partner Program. We’ll be partnering with OpenAI to explore how their cyber capabilities can be integrated within Darktrace products and services to bring new capabilities to our customers.

This partnership is an exciting opportunity to bring together Darktrace’s behavioral AI modelling of the organization with OpenAI’s advanced contextual capabilities to create a new level of understanding for security teams. To understand the impact, it’s helpful to start with how we think about the problem.  

At Darktrace, we built our AI in support of the core belief that cybersecurity needs to understand the business it is defending. That's why our Self-Learning AI is designed to help organizations understand normal and abnormal behavior for each organization across their digital environment, including users and identities, networks and cloud, email and collaboration tools, and now AI systems and agents with the rollout of Darktrace / SECURE AI™.  

Our goal was never simply to spot known attacks faster. It was to help defenders understand how their organization behaves, potential risks and impact, and where disruption could take hold so they could prepare for the unknown threats that they may not have seen or even imagined before.  

That’s exactly what is happening across the threat landscape today. Attacks keep changing; techniques shift, infrastructure evolves, and attackers move with more speed, precision, and context. And now they have even more AI and automation on their side. Attackers are exploiting identities, trusted services, SaaS applications, and business workflows. They are not always breaking in; often, the threat may come from within the organization in the form of insider threat or even rogue agents.  

In this reality, defenders need a combination of deep AI modelling of the organization and AI that can connect identified threats to concrete business context, translating this information into real world value, and allow action before risk becomes disruption.

That is the opportunity we see in partnering with OpenAI.  

What is the OpenAI Daybreak Cyber Partner Program and why is Darktrace joining

The OpenAI Daybreak Cyber Partner Program is focused on advancing the safe use of AI for cybersecurity. As part of the program’s next phase, OpenAI is working with a select group of trusted partners including Darktrace on scoped product integrations, managed services, and partner-delivered defensive capabilities. We’ll be exploring how OpenAI’s advanced frontier AI capabilities can support defenders in the tools and workflows they already use each day.

For Darktrace, this is a natural extension of our expertise and the work we have been doing for a decade: safely and securely applying the most effective AI techniques in combination to understand organizations, detecting malicious activity at the earliest indicators, and helping cyber defenders act faster.  

By using the advanced models and more precise safeguards available in the OpenAI Daybreak Cyber Partner Program, Darktrace and OpenAI will combine Darktrace’s real-time behavioral understanding of an organization's digital estate with OpenAI's ability to interpret wider business context.  

This is a unique and powerful combination of insights that could give organizations deeper context on technical risk and help them prioritize workloads and investigations based on potential impact to revenue, operations, and resilience. It can also provide security teams and executives with intelligence into which events matter most to the business, why they matter, and what action to take. Not just finding, for instance, that an agent is compromised, but highlighting that the compromised agent could shut down order fulfilment within the next three hours.  

Why the Darktrace and OpenAI partnership matters for defenders

Security teams today have more attack surface, more complex environments to protect, and an increasing volume of threats. The ability to act quickly is critical, but they also need to be able to focus on the risks that could have the greatest business impact.

That is especially important as attackers use AI to scale phishing, automate reconnaissance, find weaknesses, and blend into normal business activity. At the same time, organizations and their employees are using AI to innovate, which introduces an even broader attack surface and new set of risks. Defenders need AI that can operate across the same complexity, but safely, transparently, and in service of building more resilience. And they need a way to safely adopt, govern, and defend AI across their organizations.

Joining the OpenAI Daybreak Cyber Partner Program is another step in that direction. We are still early in this work, and we will take a careful, disciplined approach. But the direction is clear: protecting organizations requires AI that understands the business, not just the attack.

At Darktrace, that is exactly where we remain focused and why we are so excited about this partnership with OpenAI.  

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June 16, 2026

Hola VPN Abuse: From Proxy Traffic to Malware and Cryptomining

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Introduction

In enterprise environments, non-compliant software traffic can introduce unexpected exposure by creating unmanaged paths for outbound connectivity. Hola VPN is a notable example because of its peer-to-peer design, which can effectively turn user devices into routing or exit nodes for other parties’ traffic, shifting the risk profile from that of a traditional virtual private network (VPN) to something closer to a distributed proxy.

As a result, the appearance of Hola-related activity, whether from prior installation or unintended background connections, should be treated with caution.  Such activity may provide a foothold for malicious behavior, including lateral movement or command-and-control communication.

This blog explores how Hola-associated activity appeared as part of broader patterns of suspicious behavior observed across the Darktrace customer base.

The campaign

In February and March 2026, Darktrace observed similar anomalous activity across multiple customer environments, with affected devices showing consistent behavioral patterns. These included connections to multiple *.hola[.]org endpoints using Hola-related user agents, suggesting interaction with Hola infrastructure rather than isolated or incidental traffic.

Following these connections, affected customer environments showed downloads of suspicious executable files from rare external endpoints 188.241.219[.]55 and 184.241.218[.]111. Both endpoints have been flagged as potentially malicious by open-source intelligence (OSINT) [1][2].

These downloads were conducted using consistent user agents across impacted customers, specifically ‘Hola svc_js_win32/1.249.408’ and ‘Hola svc_js_win32/1.251.389’, suggesting a possible association with Hola-related activity.

Notably, this pattern aligns with recent reporting that, in some cases, Hola distributed an undeclared executable component, me[.]exe, which was later assessed to be a likely Monero-mining binary introduced via a compromised delivery pipeline [3].

Case Study 1

Darktrace first observed a new device on January 19, 2026, within a customer environment based in the Europe, Middle East, and Africa (EMEA) region. On the same day it appeared on the network, the device communicated with multiple pieces of Hola VPN-linked infrastructure before downloading a binary from a hola[.]org subdomain.

Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.
Figure 1: Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.

Subsequent Darktrace telemetry revealed a recurring pattern of activity from the day the device was first observed through to March 4, 2026. During this period, the device repeatedly issued HTTP GET requests to the URI /bwfile?size=1048576, each returning a 200 OK response, indicating successful file retrieval.

This behavior was accompanied by a POST request to /bwfile, followed by an additional GET request for a significantly larger file at /bwfile?size=26214400, suggesting a deliberate and structured file transfer pattern.

Notably, the binary download activity was not tied to a single static host. Instead, it was observed across multiple URLs that changed over time while remaining within the same hola[.]org domain. This pattern suggests the use of rotating or distributed delivery infrastructure rather than a fixed endpoint.

Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.
Figure 2: Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.

Across these events, the activity was consistently associated with the user agent Hola svc_js_win32/1.249.408, further linking the traffic to Hola-related service components. Amid these persistent and unusual connections, on February 22, Darktrace observed the device connecting to 188.241.219[.]55/proxy-peer-windows-amd64[.]exe, resulting in the download of an executable file.

 File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.
Figure 3: File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.

Based on its file hash, the downloaded file was assessed as a likely Trojan downloader [4], with import hash (imphash) values showing similarities to samples linked to Vidar, Rhadamanthys, and Stealc according to OSINT [5]. Overall, this sequence of activity suggests that Hola-related connectivity may have been leveraged as part of a broader malware delivery chain.

Darktrace’s Autonomous Response

Due to the highly unusual activity observed, Darktrace Autonomous Response was triggered by the device’s behavior. However, as the customer deployment was configured in “Human Confirmation” mode, manual approval was required before any action could be taken.

Had the deployment been set to “Fully Autonomous” mode, Darktrace would have automatically:

  1. Blocked connections to the associated ports and external endpoints
  2. Prevented all outgoing network connections from the device
  3. Enforced the device’s established ‘pattern of life’, allowing normal activity to continue while restricting any anomalous behavior
Figure 4: Example of a Darktrace Autonomous Response model highlighting the action that would have been taken, demonstrating how the system identifies anomalous behavior and applies targeted containment measures to restrict suspicious network activity.

Case Study 2

While the first case focused on anomalous activity from a newly observed device, Darktrace also identified cases in which devices had already been communicating with Hola-related endpoints prior to the suspected campaign. This may suggest pre-existing Hola usage within the environment, potentially increasing exposure and creating an avenue for subsequent suspicious activity.

One case involved three devices within a customer network based in the Americas (AMS). In this instance, a different payload was identified: me[.]exe, a potentially malicious cryptocurrency miner also referred to as HolaMonitorService[.]exe [6][7]. The downloads were observed from infrastructure similar to that seen in Case 1, including an IP address within the same 188.241.0.0/16 subnet.

Connections to *.hola[.]org, alongside the use of potential Hola-related user agents consistent with those in Case 1, were also identified, further suggesting a link between the observed activity and Hola-associated infrastructure.

Darktrace observed activity indicative of unusual VPN usage on the first affected device on February 2, followed by telemetry suggesting potential Tor usage. This was later followed by the download of me[.]exe on March 10 from 188.241.218[.]111. Notably, this device was the earliest among the three within the deployment to exhibit the presence of the suspicious executable.

Figure 5: Cyber AI Analyst detection highlighting the download of a suspicious executable from a similar external endpoint in a separate deployment.

On March 5, 2026, the second affected device exhibited a slightly different progression, initiating connections to http-test1[.]hola[.]org using the user agent ‘hola_get’. This activity was followed by the download of me[.]exe from the same endpoint on March 13, consistent with the broader pattern of Hola-related downloads observed across the environment.

 Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.
Figure 6: Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.

The final affected device within this customer’s network demonstrated a more limited but related pattern, also downloading me[.]exe on March 17 using the same ‘hola_get’ user agent.

While the earlier Hola VPN usage observed across the deployment may not have been directly related to the suspected malware campaign, it may nonetheless have contributed to reduced visibility. The presence of pre-existing Hola-related traffic could have obscured malicious activity, making it more difficult to distinguish legitimate usage from attacker-driven behavior and, in turn, hindering the timely identification of the emerging compromise.

Darktrace’s Autonomous Response

For this deployment, the customer had their Autonomous Response capability configured in “Fully Autonomous” mode, allowing Darktrace to take action without human intervention. As a result, the system was able to autonomously disrupt the activity as soon as relevant events were identified through model detections.

Figure 7: Darktrace Autonomous Response actions taken against suspicious activity linked to Hola VPN.

Suspected cryptomining activity

As previously noted, some of the observed executable payloads appear to be linked to cryptomining malware. Across a subset of affected customer environments, this assessment was further supported by subsequent device activity consistent with Monero mining. Affected devices established follow-on connections to multiple external endpoints aligned with known mining infrastructure, indicating post-download execution.

Considering the broader sequence of activity, this pattern may point to a wider form of abuse in which legitimate VPN-related traffic is used to mask or facilitate malicious behavior following compromise.

On several devices, the download of executable files, including a newly observed peer[.]exe, was followed by alerts indicative of cryptocurrency mining activity. Mining-related credentials such as ‘x’ were observed using the Minergate protocol to communicate with endpoints within the 89.125.255.0/24 subnet and 188.241.218[.]111, the same endpoint involved in earlier download activity. Additional credentials appeared to reflect device-specific CPU identifiers, for example ‘12th Gen Intel(R) Core (TM) i5-1235U’.

Observed mining methods included login, submit, and job, consistent with active participation in a pool-based mining workflow rather than passive or incidental contact. The login method indicates that the host authenticated to the mining service as a worker, job reflects the assignment of computational tasks, and submit shows completed work being returned to the pool [8]. This sequence suggests that affected devices were actively contributing processing resources as part of an unauthorized distributed mining operation.

The presence of unauthorized cryptominers can lead to degraded system performance and reduced device stability. Beyond the immediate resource impact, such activity often serves as an indicator of a broader compromise rather than an isolated issue. This may increase the risk of further malware deployment, persistence mechanisms, and lateral movement, particularly in environments where the initial intrusion has not been fully contained.

Conclusion

Across affected environments, detections such as unusual VPN usage, connections to Hola infrastructure, anomalous HTTP activity, suspicious file downloads, and subsequent cryptomining behavior were linked into a single, evolving incident narrative. This aggregation provided a clearer view of attack progression, enabling security teams to understand not just isolated alerts, but the full sequence of compromise from initial contact through to post-exploitation.

Ultimately, these activities show that the risk posed by non-compliant software such as Hola VPN can extend far beyond simple policy violations. What began as traffic to Hola-related infrastructure was, in multiple cases, followed by behavior suggesting deliberate misuse, including suspicious executable downloads using Hola-related user agents and, in some instances, evidence of active cryptomining. These were not isolated anomalies, but elements of a broader pattern in which seemingly benign proxy or VPN-related communications may have created a pathway for malicious delivery and unauthorized resource exploitation.

The significance of this activity lies not only in the downloads or mining, but in what it reveals about an attacker’s ability to blend malicious operations into traffic associated with software that may already have a foothold in the environment. When unapproved software operates within an enterprise, it can reduce visibility, blur the distinction between legitimate and malicious traffic, and create opportunities to extend compromise in ways that are persistent and difficult to detect. Darktrace’s anomaly-based approach enables these behavioral distinctions to be identified, regardless of whether the device is new or long established within the network.

Credit to Min Kim (Associate Principal Analyst), Priya Thapa (Senior Cyber Analyst)
Edited by Ryan Traill (Content Manager)

Appendices

References

[1] https://www.virustotal.com/gui/ip-address/188.241.219.55

[2]  https://www.virustotal.com/gui/ip-address/188.241.218.111

[3] https://www.sophos.com/en-us/blog/you-do-surprise-me-exe-an-unexpected-executable-in-hola-browser

[4] https://www.virustotal.com/gui/file/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/detection

[5] https://bazaar.abuse.ch/sample/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/

[6] https://any.run/report/4cdeb5df217764a8b6a20d518b76ccb30cbe623365a13d9dcd40900950f1ed99/de3a756a-3101-4369-8922-52c586c939fb

[7] https://www.virustotal.com/gui/file/e3541caf708c075f0bb22fc68b03acd8457fea7cf0732ea935b1eb016d1c7721/community

[8] https://bitcoinwiki.org/wiki/stratum

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Compromise / Crypto Currency Mining Activity

·      Compromise / High Priority Crypto Currency Mining (EM)

·      Device / New User Agent

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·      Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·      Antigena / Network / External Threat / Antigena Tor Block

·      Antigena / Network / External Threat / Antigena File then New Outbound Block

·      Antigena / Network / External Threat / Antigena Suspicious Activity Block

·      Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

·      Antigena / Network / External threat / Antigena Suspicious File Block

Indicators of Compromise (IoCs)

IoC –Type -Description + Confidence

188.241.219[.]55 - IP Address - Malware distribution source

188.241.218[.]111 - IP Address -Malware distribution source

hxxp://188.241.218[.]111:8080/me[.]exe - URI - Malicious payload

hxxp://188.241.219[.]55:9000/proxy-peer-windows-amd64[.]exe - URI - Malicious payload

hxxp://188.241.219[.]55:9000/peer[.]exe - URI - Malicious payload

C8088f3c8bc3542eb1ad78a7cc5306d866c8ac81 - SHA1 - Malicious payload, me[.]exe

b595a6de0f6a18975b29e6f8ebe604956a173478 - SHA1 - Malicious payload, me[.]exe

e9139a2e0839e8b9e5c9787ea936347ae56e5460 - SHA1 - Possible malicious payload

c2e80073e4cafe757d5643bd8fd45f28ad89bff9 - SHA1 - Possible malicious payload

695355eceedcdd337d8fcbd35e6a531cda75b847 - SHA1 - Possible malicious payload

f0b0d8068a1b9ab5d68a8a46842d72b870b292e7 - SHA1 - Possible malicious payload

a21c8b8cabc7670ea45bc175e185a0f9bfcf4733 - SHA1 - Malicious payload, me[.]exe

0353ca44b9f397d8f492db0b2f7a1d00a9e4406a - SHA1 - Possible malicious payload

56824c8a110e35ab303dc27a6c758cd50c36174c - SHA1 - Malicious payload, peer[.]exe

c141fa0fa505fe7f9ad5dd21d9d4d6d411739682 - SHA1 - Malicious payload, peer[.]exe

0417ec988b16f1267065185a6eea98f0bd2e17cd - SHA1 - Possible malicious payload

c54f7eaaeb3e0b528cd2584bdcb3a4b13cc0f8a2 - SHA1 - Malicious payload, peer[.]exe

11c78f15fafd53f8cc5a52b828d7cbf2a99e0b09 - SHA1 - Malicious payload, peer[.]exe

0258bf7dbb0123247db29e8799991140bbdbd9bb - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

b46043a06dd9bbd63e4214d5fbc7fd56e1ff0618 - SHA1 - Possible malicious payload

753afdecd9f5402d004e8e5f768170ae9a468ca5 - SHA1 - Possible malicious payload

8f533c7cb1524b00f7b0311c2ea8603298d6b2ca - SHA1 - Possible malicious payload

3a3bc6a5b4db1a4e961abcb002d26fe9d5e5c349 - SHA1 - Possible malicious payload

897f70eb41d302b045fcb05ed0693675e778ce57 - SHA1 - Possible malicious payload

6ddd5644809606e3dc1e2cc06059c3f5e6176f85 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

68a94f7cdcaf8853ea99251c1ecc67ae9b32eba8 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

MITRE ATT&CK Mapping

T1659 -Initial Access, Command and Control -Content Injection

T1588.001 -Resource Development -Malware

T1189 -Initial Access -Drive-by Compromise

T1105 -Command and Control -Ingress Tool Transfer

T1657 -Impact -Financial Theft

T1497.001 -Impact -Compute Hijacking

T1496 -Impact -Resource Hijacking

T1210 -Lateral Movement -Exploitation of Remote Services

T1036.012 -Stealth -Browser Fingerprint

T1071.001 -Command and Control -Web Protocols

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About the author
Min Kim
Cyber Security Analyst
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