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June 23, 2023

How Darktrace Quickly Foiled An Information Stealer

Discover how Darktrace thwarted the CryptBot malware in just 2 seconds. Learn about this fast-moving threat and the defense strategies employed.
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
Alexandra Sentenac
Cyber Analyst
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23
Jun 2023

The recent trend of threat actors using information stealer malware, designed to gather and exfiltrate confidential data, shows no sign of slowing. With new or updated info-stealer strains appearing in the wild on a regular basis, it came as no surprise to see a surge in yet another prolific variant in late 2022, CryptBot.

What is CryptBot?

CryptBot is a Windows-based trojan malware that was first discovered in the wild in December 2019. It belongs to the prolific category of information stealers whose primary objective, as the name suggests, is to gather information from infected devices and send it to the threat actor.

ZeuS was reportedly the first info-stealer to be discovered, back in 2006. After its code was leaked, many other variants came to light and have been gaining popularity amongst cyber criminals [1] [2] [3]. Indeed, Inside the SOC has discussed multiple infections across its customer base associated with several types of stealers in the past months [4] [5] [6] [7]. 

The Darktrace Threat Research team investigated CryptBot infections on the digital environments of more than 40 different Darktrace customers between October 2022 and January 2023. Darktrace DETECT™ and its anomaly-based approach to threat detection allowed it to successfully identify the unusual activity surrounding these info-stealer infections on customer networks. Meanwhile, Darktrace RESPOND™, when enabled in autonomous response mode, was able to quickly intervene and prevent the exfiltration of sensitive company data.

Why is info-stealer malware popular?

It comes as no surprise that info-stealers have “become one of the most discussed malware types on the cybercriminal underground in 2022”, according to Accenture’s Cyber Threat Intelligence team [10]. This is likely in part due to the fact that:

More sensitive data on devices

Due to the digitization of many aspects of our lives, such as banking and social interactions, a trend accelerated by the COVID-19 pandemic.

Cost effective

Info-stealers provide a great return on investment (ROI) for threat actors looking to exfiltrate data without having to do the traditional internal reconnaissance and data transfer associated with data theft. Info-stealers are usually cheap to purchase and are available through Malware-as-a-Service (MaaS) offerings, allowing less technical and resourceful threat actors in on the stealing action. This makes them a prevalent threat in the malware landscape. 

How does CryptBot work?

The techniques employed by info-stealers to gather and exfiltrate data as well as the type of data targeted vary from malware to malware, but the data targeted typically includes login credentials for a variety of applications, financial information, cookies and global information about the infected computer [8]. Given its variety and sensitivity, threat actors can leverage the stolen data in several ways to make a profit. In the case of CryptBot, the data obtained is sold on forums or underground data marketplaces and can be later employed in higher profile attacks [9]. For example, stolen login information has previously been leveraged in credential-based attacks, which can successfully bypass authentication-based security measures, including multi-factor authentication (MFA). 

CryptBot functionalities

Like many information stealers, CryptBot is designed to steal a variety of sensitive personal and financial information such as browser credentials, cookies and history information and social media accounts login information, as well as cryptocurrency wallets and stored credit card information [11]. General information (e.g., OS, installed applications) about the infected computer is also retrieved. Browsers targeted by CryptBot include Chrome, Firefox, and Edge. In early 2022, CryptBot’s code was revamped in order to streamline its data extraction capabilities and improve its overall efficiency, an update that coincided with a rise in the number of infections [11] [12].

Some of CryptBot's functionalities were removed and its exfiltration process was streamlined, which resulted in a leaner payload, around half its original size and a quicker infection process [11]. Some of the features removed included sandbox detection and evasion functionalities, the collection of desktop text files and screen captures, which were deemed unnecessary. At the same time, the code was improved in order to include new Chrome versions released after CryptBot’s first appearance in 2019. Finally, its exfiltration process was simplified: prior to its 2022 update, the malware saved stolen data in two separate folders before sending it to two separate command and control (C2) domains. Post update, the data is only saved in one location and sent to one C2 domain, which is hardcoded in the C2 transmission function of the code. This makes the infection process much more streamlined, taking only a few minutes from start to finish. 

Aside from the update to its malware code, CryptBot regularly updates and refreshes its C2 domains and dropper websites, making it a highly fluctuating malware with constantly new indicators of compromise and distribution sites. 

Even though CryptBot is less known than other info-stealers, it was reportedly infecting thousands of devices daily in the first months of 2020 [13] and its continued prevalence resulted in Google taking legal action against its distribution infrastructure at the end of April 2023 [14].  

How is CryptBot obtained?

CryptBot is primarily distributed through malicious websites offering free and illegally modified software (i.e., cracked software) for common commercial programs (e.g., Microsoft Windows and Office, Adobe Photoshop, Google Chrome, Nitro PDF Pro) and video games. From these ‘malvertising’ pages, the user is redirected through multiple sites to the actual payload dropper page [15]. This distribution method has seen a gain in popularity amongst info-stealers in recent months and is also used by other malware families such as Raccoon Stealer and Vidar [16] [17].

A same network of cracked software websites can be used to download different malware strains, which can result in multiple simultaneous infections. Additionally, these networks often use search engine optimization (SEO) in order to make adverts for their malware distributing sites appear at the top of the Google search results page, thus increasing the chances of the malicious payloads being downloaded.

Furthermore, CryptBot leverages Pay-Per-Install (PPI) services such as 360Installer and PrivateLoader, a downloader malware family used to deliver payloads of multiple malware families operated by different threat actors [18] [19] [20]. The use of this distribution method for CryptBot payloads appears to have stemmed from its 2022 update. According to Google, 161 active domains were associated with 360Installer, of which 90 were associated with malware delivery activities and 29 with the delivery of CryptBot malware specifically. Google further identified hundreds of domains used by CryptBot as C2 sites, all of which appear to be hosted on the .top top-level domain [21].

This simple yet effective distribution tactic, combined with the MaaS model and the lucrative prospects of selling the stolen data resulted in numerous infections. Indeed, CryptBot was estimated to have infected over 670,000 computers in 2022 [14]. Even though the distribution method chosen means that most of the infected devices are likely to be personal computers, bring your own device (BYOD) policies and users’ tendency to reuse passwords means that corporate environments are also at risk. 

CryptBot Attack Overview

In some cases observed by Darktrace, after connecting to malvertising websites, devices were seen making encrypted SSL connections to file hosting services such as MediaFire or Mega, while in others devices were observed connecting to an endpoint associated with a content delivery network. This is likely the location from where the malware payload was downloaded alongside cracked software, which is executed by the unsuspecting user. As the user expects to run an executable file to install their desired software, the malware installation often happens without the user noticing.

Some of the malvertising sites observed by Darktrace on customer deployments were crackful[.]com, modcrack[.]net, windows-7-activator[.]com and office-activator[.]com. However, in many cases detected by Darktrace, CryptBot was propagated via websites offering trojanized KMSPico software (e.g., official-kmspico[.]com, kmspicoofficial[.]com). KMSPico is a popular Microsoft Windows and Office product activator that emulates a Windows Key Management Services (KMS) server to activate licenses fraudulently. 

Once it has been downloaded and executed, CryptBot will search the system for confidential information and create a folder with a seemingly randomly generated name, matching the regex [a-zA-Z]{10}, to store the gathered sensitive data, ready for exfiltration. 

Figure 1: Packet capture (PCAP) of an HTTP POST request showing the file with the stolen data being sent over the connection.
Figure 1: Packet capture (PCAP) of an HTTP POST request showing the file with the stolen data being sent over the connection.

This data is then sent to the C2 domain via HTTP POST requests on port 80 to the URI /gate.php. As previously stated, CryptBot C2 infrastructure is changed frequently and many of the domains seen by Darktrace had been registered within the previous 30 days. The domain names detected appeared to have been generated by an algorithm, following the regex patterns [a-z]{6}[0-9]{2,3}.top or [a-z]{6}[0-9]{2,3}.cfd. In several cases, the C2 domain had not been flagged as malicious by other security vendors or had just one detection. This is likely because of the frequent changes in the C2 infrastructure operated by the threat actors behind CryptBot, with new malicious domains being created periodically to avoid detection. This makes signature-based security solutions much less efficient to detect and block connections to malicious domains. Additionally, the fact that the stolen data is sent over regular HTTP POST requests, which are used daily as part of a multitude of legitimate processes such as file uploads or web form submissions, allows the exfiltration connections to blend in with normal and legitimate traffic making it difficult to isolate and detect as malicious activity. 

In this context, anomaly-based security detections such as Darktrace DETECT are the best way to pick out these anomalous connections amidst legitimate Internet traffic. In the case of CryptBot, two DETECT models were seen consistently breaching for CryptBot-related activity: ‘Device / Suspicious Domain’, breaching for connections to 100% rare C2 .top domains, and ‘Anomalous Connection / POST to PHP on New External Host’, breaching on the data exfiltration HTTP POST request. 

In deployments where Darktrace RESPOND was deployed, a RESPOND model breached within two seconds of the first HTTP POST request. If enabled in autonomous mode, RESPOND would block the data exfiltration connections, thus preventing the data safe from being sold in underground forums to other threat actors. In one of the cases investigated by Darktrace’s Threat Research team, DETECT was able to successfully identify and alert the customer about CryptBot-related malicious activity on a device that Darktrace had only begun to monitor one day before, showcasing how fast Darktrace’s Self-Learning AI learns every nuance of customer networks and the devices within it.

In most cases investigated by Darktrace, fewer than 5 minutes elapsed between the first connection to the endpoint offering free cracked software and the data being exfiltrated to the C2 domain. For example, in one of the attack chains observed in a university’s network, a device was seen connecting to the 100% rare endpoint official-kmspico[.]com at 16:53:47 (UTC).

Device Event Log showing SSL connections to the official-kmspico[.]com malvertising website.
Figure 2: Device Event Log showing SSL connections to the official-kmspico[.]com malvertising website.

One minute later, at 16:54:19 (UTC), the same device was seen connecting to two mega[.]co[.]nz subdomains and downloading around 13 MB of data from them. As mentioned previously, these connections likely represent the CryptBot payload and cracked software download.

Device Event Log showing SSL connections to mega[.]com endpoints following the connection to the malvertising site.
Figure 3: Device Event Log showing SSL connections to mega[.]com endpoints following the connection to the malvertising site.

At 16:56:01 (UTC), Darktrace detected the device making a first HTTP POST request to the 100% rare endpoint, avomyj24[.]top, which has been associated with CryptBot’s C2 infrastructure [22]. This initial HTTP POST connection likely represents the transfer of confidential data to the attacker’s infrastructure.

Device Event Log showing HTTP connections made by the infected device to the C2 domain. 
Figure 4: Device Event Log showing HTTP connections made by the infected device to the C2 domain. 

The full attack chain, from visiting the malvertising website to the malicious data egress, took less than three minutes to complete. In this circumstance, the machine-speed detection and response capabilities offered by Darktrace DETECT and RESPOND are paramount in order to stop CryptBot before it can successfully exfiltrates sensitive data. This is an incredibly quick infection timeline, with no lateral movement nor privilege escalation required to carry out the malware’s objective. 

Device Event Log showing the DETECT and RESPOND models breached during the attack. 
Figure 5: Device Event Log showing the DETECT and RESPOND models breached during the attack. 

Darktrace Cyber AI Analyst incidents were also generated as a result of this activity, displaying all relevant information in one panel for easy review by customer security teams.

Cyber AI Analyst event log showing the HTTP connections made by the breach device to the C2 endpoint.
Figure 6: Cyber AI Analyst event log showing the HTTP connections made by the breach device to the C2 endpoint.

Conclusion 

CryptBot info-stealer is fast, efficient, and apt at evading detection given its small size and swift process of data gathering and exfiltration via legitimate channels. Its constantly changing C2 infrastructure further makes it difficult for traditional security tools that really on rules and signatures or known indicators of compromise (IoCs) to detect these infections. 

In the face of such a threat, Darktrace’s anomaly-based detection allows it to recognize subtle deviations in a device’s pattern of behavior that may signal an evolving threat and instantly bring it to the attention of security teams. Darktrace DETECT is able to distinguish between benign activity and malicious behavior, even from newly monitored devices, while Darktrace RESPOND can move at machine-speed to prevent even the fastest moving threat actors from stealing confidential company data, as it demonstrated here by stopping CryptBot infections in as little as 2 seconds.

Credit to Alexandra Sentenac, Cyber Analyst, Roberto Romeu, Senior SOC Analyst

Darktrace Model Detections  

AI Analyst Coverage 

  • Possible HTTP Command and Control  

DETECT Model Breaches  

  • Device / Suspicious Domain 
  • Anomalous Connection / POST to PHP on New External Host 
  • Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 
  • Compromise / Multiple SSL to Rare DGA Domains

List of IOCs

Indicator Type Description
luaigz34[.]top Hostname CryptBot C2 endpoint
watibt04[.]top Hostname CryptBot C2 endpoint
avolsq14[.]top Hostname CryptBot C2 endpoint

MITRE ATT&CK Mapping

Category Technique Tactic
INITIAL ACCESS Drive-by Compromise - T1189 N/A
COMMAND AND CONTROL Web Protocols - T1071.001 N/A
COMMAND AND CONTROL Domain Generation Algorithm - T1568.002 N/A

References

[1] https://www.malwarebytes.com/blog/threats/info-stealers

[2] https://cybelangel.com/what-are-infostealers/

[3] https://ke-la.com/information-stealers-a-new-landscape/

[4] https://darktrace.com/blog/vidar-info-stealer-malware-distributed-via-malvertising-on-google

[5] https://darktrace.com/blog/a-surge-of-vidar-network-based-details-of-a-prolific-info-stealer 

[6] https://darktrace.com/blog/laplas-clipper-defending-against-crypto-currency-thieves-with-detect-respond

[7] https://darktrace.com/blog/amadey-info-stealer-exploiting-n-day-vulnerabilities 

[8] https://cybelangel.com/what-are-infostealers/

[9] https://webz.io/dwp/the-top-10-dark-web-marketplaces-in-2022/

[10] https://www.accenture.com/us-en/blogs/security/information-stealer-malware-on-dark-web

[11] https://www.bleepingcomputer.com/news/security/revamped-cryptbot-malware-spread-by-pirated-software-sites/

[12] https://blogs.blackberry.com/en/2022/03/threat-thursday-cryptbot-infostealer

[13] https://www.deepinstinct.com/blog/cryptbot-how-free-becomes-a-high-price-to-pay

[14] https://blog.google/technology/safety-security/continuing-our-work-to-hold-cybercriminal-ecosystems-accountable/

[15] https://asec.ahnlab.com/en/31802/

[16] https://darktrace.com/blog/the-last-of-its-kind-analysis-of-a-raccoon-stealer-v1-infection-part-1

[17] https://www.trendmicro.com/pt_br/research/21/c/websites-hosting-cracks-spread-malware-adware.html

[18] https://intel471.com/blog/privateloader-malware

[19] https://cyware.com/news/watch-out-pay-per-install-privateloader-malware-distribution-service-is-flourishing-888273be 

[20] https://regmedia.co.uk/2023/04/28/handout_google_cryptbot_complaint.pdf

[21] https://www.bankinfosecurity.com/google-wins-court-order-to-block-cryptbot-infrastructure-a-21905

[22] https://github.com/stamparm/maltrail/blob/master/trails/static/malware/cryptbot.txt

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
Alexandra Sentenac
Cyber Analyst

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May 18, 2026

AI Insider Threats: How Generative AI is Changing Insider Risk

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How generative AI changes insider behavior

AI systems, especially generative platforms such as chatbots, are designed for engagement with humans. They are equipped with extraordinary human-like responses that can both confirm, and inflate, human ideas and ideology; offering an appealing cognitive partnership between machine and human.  When considering this against the threat posed by insiders, the type of diverse engagement offered by AI can greatly increase the speed of an insider event, and can facilitate new attack platforms to carry out insider acts.  

This article offers analysis on how to consider this new paradigm of insider risk, and outlines key governance principles for CISOs, CSOs and SOC managers to manage the threats inherent with AI-powered insider risk.

What is an insider threat?

There are many industry or government definitions of what constitutes insider threat. At its heart, it relates to the harm created when trusted access to sensitive information, assets or personnel is abused bywith malicious intent, or through negligent activities.  

Traditional methodologies to manage insider threat have relied on two main concepts: assurance of individuals with access to sensitive assets, and a layered defense system to monitor for any breach of vulnerability. This is often done both before, and after access has been granted.  In the pre-access state, assurance is gained through security or recruitment checks. Once access is granted, controls such as privileged access, and zero-trust architecture offer defensive layers.

How does AI change the insider threat paradigm?

While these two concepts remain central to the management of insider threats, the introduction of AI offers three key new aspects that will re-shape the paradigm:.  

AI can act as a cognitive amplifier, influencing and affecting the motivations that can lead to insider-related activity. This is especially relevant for the deliberate insider - someone who is considering an act of insider harm. These individuals can now turn to AI systems to validate their thinking, provide unique insights, and, crucially, offer encouragement to act. With generative systems hard-wired to engage and agree with users, this can turn a helpful AI system into a dangerous AI hype machine for those with harmful insider intent.  

AI can act as an operational enabler. AI can now develop and increase the range of tools needed to carry out insider acts. New social engineering platforms such as vishing and deepfakes give adversaries a new edge to create insider harm. AI can generate solutions and operational platforms at increasing speeds; often without the need for human subject matter expertise to execute the activities. As one bar for advanced AI capabilities continues to be raised, the bar needed to make use of those platforms has become significantly lower.

AI can act as a semi-autonomous insider, particularly when agentic AI systems or non-human identities are provided broad levels of autonomy; creating a vector of insider acts with little-to-no human oversight or control. As AI agents assume many of the orchestration layers once reserved for humans, they do so without some of the restricted permissions that generally bind service accounts. With broad levels of accessibility and authority, these non-human identities (NHIs) can themselves become targets of insider intent.  Commonly, this refers to the increasing risks of prompt injection, poisoning, or other types of embedded bias. In many ways, this mirrors the risks of social engineering traditionally faced by humans. Even without deliberate or malicious efforts to corrupt them, AI systems and AI agents can carry out unintended actions; creating vulnerabilities and opportunities for insider harm.

How to defend against AI-powered insider threats

The increasing attack surfaces created or facilitated by AI is a growing concern.  In Darktrace’s own AI cybersecurity research, the risks introduced, and acknowledged, through the proliferation of AI tools and systems continues to outstrip traditional policies and governance guardrails. 22% of respondents in the survey cited ‘insider misuse aided by generative AI’ as a major threat concern.  And yet, in the same survey, only 37% of all respondents have formal policies in place to manage the safe and responsible use of AI.  This draws a significant and worrying delta between the known risks and threat concerns, and the ability (and resources) to mitigate them.

What can CISOs and SOC leaders do to protect their organization from AI insider threats?  

Given the rapid adaptation, adoption, and scale of AI systems, implementing the right levels of AI governance is non-negotiable. Getting the correct balance between AI-driven productivity gains and careful compliance will lead to long-term benefits. Adapting traditional insider threat structures to account for newer risks posed through the use of AI will be crucial. And understanding the value of AI systems that add to your cybersecurity resilience rather than imperil it will be essential.

For those responsible for the security and protection of their business assets and data holdings, the way AI has changed the paradigm of insider threats can seem daunting.  Adopting strong, and suitable AI governance can become difficult to introduce due to the volume and complexity of systems needed to be monitored. As well as traditional insider threat mitigations such as user monitoring, access controls and active management, the speed and autonomy of some AI systems need different, as well as additional layers of control.  

How Darktrace helps protect against AI-powered insider threats

Darktrace has demonstrated that, through platforms such as our proprietary Cyber AI Analyst, and our latest product Darktrace / SECURE AI, there are ways AI systems can be self-learning, self-critical and resilient to unpredictable AI behavior whilst still offering impressive returns; complementing traditional SOC and CISO strategies to combat insider threat.  

With / SECURE AI, some of the ephemeral risks drawn through AI use can be more easily governed.  Specifically, the ability to monitor conversational prompts (which can both affect AI outputs as well as highlight potential attempts at manipulation of AI; raising early flags of insider intent); the real-time observation of AI usage and development (highlighting potential blind-spots between AI development and deployment); shadow AI detection (surfacing unapproved tools and agents across your IT stack) and; the ability to know which identities (human or non-human) have permission access. All these features build on the existing foundations of strong insider threat management structures.  

How to take a defense-in-depth approach to AI-powered insider threats

Even without these tools, there are four key areas where robust, more effective controls can mitigate AI-powered insider threat.  Each of the below offers a defencce-in-depth approach: layering acknowledgement and understanding of an insider vector with controls that can bolster your defenses.  

Identity and access controls

Having a clear understanding of the entities that can access your sensitive information, assets and personnel is the first step in understanding the landscape in which insider harm can occur.  AI has shown that it is not just flesh and bone operators who can administer insider threats; Non-Human Identities (such as agentic AI systems) can operate with autonomy and freedom if they have the right credentials. By treating NHIs in the same way as human operators (rather than helpful machine-based tools), and adding similar mitigation and management controls, you can protect both your business, and your business-based identities from insider-related attention.

Visibility and shadow AI detection

Configuring AI systems carefully, as well as maintaining internal monitoring, can help identify ‘shadow AI’ usage; defined as the use of unsanctioned AI tools within the workplace1 (this topic was researched in Darktrace’s own paper on "How to secure AI in the enterprise". The adoption of shadow AI could be the result of deliberate preference, or ‘shortcutting’; where individuals use systems and models they are familiar with, even if unsanctioned. As well as some performance risks inherent with the use of shadow AI (such as data leakage and unwanted actions), it could also be a dangerous precursor for insider-related harm (either through deliberate attempts to subvert regular monitoring, or by opening vulnerabilities through unpatched or unaccredited tooling).

Prompt and Output Guardrails

The ability to introduce guardrails for AI systems offers something of a traditional “perimeter protection” layer in AI defense architecture; checking prompts and outputs against known threat vectors, or insider threat methodologies. Alone, such traditional guardrails offer limited assurance.  But, if tied with behavior-centric threat detection, and an enforcement system that deters both malicious and accidental insider activities, this would offer considerable defense- in- depth containment.  

Forensic logging and incident readiness response

The need for detection, data capture, forensics, and investigation are inherent elements of any good insider threat strategy. To fully understand the extent or scope of any suspected insider activity (such as understanding if it was deliberate, targeted, or likely to occur again), this rich vein of analysis could prove invaluable.  As the nature of business increasingly turns ephemeral; with assets secured in remote containers, information parsed through temporary or cloud-based architecture, and access nodes distributed beyond the immediate visibility of internal security teams, the development of AI governance through containment, detection, and enforcement will grow ever more important.

Enabling these controls can offer visibility and supervision over some of the often-expressed risks about AI management. With the right kind of data analytics, and with appropriate human oversight for high-risk actions, it can illuminate the core concerns expressed through a new paradigm of AI-powered insider threats by:

  • Ensuring deliberately mis-configured AI systems are exposed through regular monitoring.
  • Highlighting changes in systems-based activity that might indicate harmful insider actions; whether malicious or accidental.
  • Promoting a secure-by-design process that discourages and deters insider-related ambitions.
  • Ensuring the control plane for identity-based access spans humans, NHIs and AI models, and:
  • Offering positive containment strategies that will help curate the extent of AI control, and minimize unwanted activities.

Why insider threat remains a human challenge

At its root, and however it has been configured, AI is still an algorithmic tool; something designed to automate, process and manage computational functions at machine speed, and boost productivity.  Even with the best cybersecurity defenses in place, the success of an insider threat management program will still depend on the ability of human operators to identify, triage, and manage the insider threat attack surface.  

AI governance policies, human-in-the-loop break points, and automated monitoring functions will not guard against acts of insider harm unless there is intention to manage this proactively, and through a strong culture of how to guard against abuses of trust and responsibility.

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Jason Lusted
AI Governance Advisor

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May 18, 2026

中国系APTキャンペーン、アップデートされたFDMTPバックドアで企業を狙う

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ダークトレースは、中国系グループの活動と一致する動きを特定しました。これは、主にアジア太平洋および日本(APJ)地域の顧客環境を標的としたTwill Typhoonに関連するキャンペーンです。

2025年9月下旬から、影響を受けた複数のホストが、YahooやApple関連のサービスを装ったインフラを含む、コンテンツ配信ネットワーク(CDN)を偽装したドメインへのリクエストを行っていることが観察されました。これらの事例において、ダークトレースは一貫した動作のパターンを特定しました。それは、正当なバイナリと悪意あるダイナミックリンクライブラリ(DLL)を同時に取得し、モジュラー型の.NETベースのリモートアクセス型トロイの木馬(RAT)フレームワークのサイドローディングと実行を可能にするものでした。

これらはダークトレースが先日発表した中国系オペレーションについてのレポート、 Crimson Echoで説明されているパターンとも一致しています。このケースでは、正規のソフトウェア上にモジュラー型の侵入チェーンが構築され、ステージングされたペイロードの投下が見られました。脅威アクターは正当なバイナリをコンフィギュレーションファイルや悪意あるDLLとともに取得することにより、.NETベースのRATのサイドローディングを可能にしました。

キャンペーンの確認

これらのケースには同じ順序のシーケンスが現れています:(1) 正規の実行可能ファイルの取得、(2) 対応する .config ファイルの取得、(3) 悪意あるDLLの取得、(4) DLLの繰り返しダウンロード、(5) コマンド&コントロール(C2)通信。 正規のバイナリは正規のプロセスを提供しますが、.config ファイルは悪意あるバイナリを取得します。

ダークトレースは、この活動が公に報告されているTwill Typhoonの手法と一致していると中程度の確信を持って評価しています。FDMTPの使用、DLLサイドローディング、および重複するインフラストラクチャが観察されたことは、以前に見られた作戦と一致していますが、これは特定の単一のアクターに固有のものではありません。アトリビューションには可視性による制限があります。初期アクセスは直接確認されませんでしたが、侵入のパターンは同様の作戦で報告されている既知のフィッシングによる侵入手法と一致しています。

Darktraceによる観測

2025年9月下旬より、Darktraceは複数の顧客環境において良く知られたプラットフォームの“CDN”エンドポイントと称するインフラ(YahooやAppleを偽装したものを含む)に対してHTTP GETリクエストが行われていることを観測しました。これらのケースでは、影響を受けたホストは正当な実行形式、対応する.configファイル(同じベース名)、そしてサイドローディング用DLLを取得しています。正当なバイナリ+コンフィギュレーション+DLLのシーケンスは中国系の攻撃キャンペーンで見られているものです。

いくつかのケースでは、ホストはさらに/GetClusterエンドポイントへのアウトバウンドリクエストを発行しており、protocol=Dotnet-Tcpdmtpパラメータも含まれていました。このアクティビティの後繰り返しDLLコンテンツの取得が行われ、その後これが正当なプロセス内でサーチオーダー杯ジャッキングに使われました。

2025年9月~10月に見られた多くのケースで、Darktraceのアラートは初期段階の登録およびC2セットアップ動作を識別しました。その後同じ外部ホストからのDLL(Client.dll等)取得(一部のケースでは複数日に渡って繰り返し)が続き、これは実行チェーンの確立と維持を示すものでした。2026年4月、金融セクターの顧客のエンドポイントがyahoo-cdn[.]it[.]comに対して一連のGETリクエストを開始し、最初に正当なバイナリ(vshost.exeおよびdfsvc.exeを含む)を取得し、その後11日間にわたり関連するコンフィギュレーションファイルおよびDLLコンポーネント(dfsvc.exe.configおよびdnscfg.dllを含む)を繰り返し取得しました。Visual Studio ホスティングと OneClick(dfsvc.exe)のパスの使用はどちらも、マルウェアをターゲット環境で実行できるようにするためのものです。

技術分析

初期ステージングおよび実行

最初のアクセスはわかっていませんが、ダークトレースの研究者はマルウェアを含む複数のアーカイブを特定しました。

代表的なサンプルには以下を含むZIPアーカイブ(“test.zip”)が含まれていました:

  • 正規の実行形式:biz_render.exe(Sogou Pinyin IME)
  • 悪意あるDLL: browser_host.dll

"test.zip" という名前のzipアーカイブには、正規のバイナリ"biz_render.exe" が含まれており、これは人気のある中国語IMEであるSogou Pinyinです。

正規のバイナリと共に ”browser_host.dll” という悪意のあるDLLがあります。</x1>この正規のバイナリは ”browser_host.dll”という正規のDLLを、LoadLibraryExWを介して読み込みますが、悪意のあるDLLにも同じ名前がつけられることにより、biz_render.exeに悪意のあるDLLをサイドロードします。同名の悪意あるDLLを提供することで、攻撃者は実行フローを乗っ取り、信頼されたプロセス内でペイロードを実行することができます。

図1.Biz_render.exe による browser_host.dll のローディング

正規のバイナリは、サイドロードされた"browser_host.dll"から関数GetBrowserManagerInstanceを呼び出し、その後、埋め込まれた文字列に対してXORベースの復号化(キー 0x90)を実行して、mscoree.dllを解決し動的にロードします。

このDLLは、ネイティブバイナリのみに依存するのではなく、Windowsの共通言語ランタイム(CLR)を使用することにより、プロセス内で管理された.NETコードを実行します。実行中、ローダーはペイロードを.NETアセンブリとして直接メモリにロードし、メモリ内での実行を可能にします。

C2 登録

GETリクエストが以下に対して実行されます:

GET /GetCluster?protocol=DotNet-TcpDmtp&tag={0}&uid={1}

カスタムヘッダ:

Verify_Token: Dmtp

これは、後の通信に使用されるIPアドレスをbase64でエンコードし、gzipで圧縮したものを返します。

図2.デコードされたIP

ステージングされたペイロードの取得

その後のアクティビティには、yahoo-cdn.it[.]comからの複数のコンポーネントの取得が含まれます。以下のGETリクエストが行われます:

/dfsvc.exe

/dnscfg.dll

/dfsvc.exe.config

/vhost.exe

/Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll

/config.etl

ClickOnceおよびAppDomainのハイジャッキング

Dfsvc.exeは正当なWindowsのClickOnceエンジンであり、ClickOnceアプリケーションの更新に使用される.NETフレームワークの一部です。付随するdfsvc.exeには、アプリケーションのコンフィギュレーションデータを保存するために使用されるdfsvc.exe.configファイルが含まれています。しかし、このケースではマルウェアが正規のdfsvc.exe.configをC:\Windows\Microsoft.NET\Framework64\v4.0.30319のサーバーから取得したものと置き換えます。

さらに、正当なVisual Studioホスティングプロセスであるvhost.exeがサーバーから取得され、それとともに”Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll”と”config.etl”も取得されます。このDLLは、config.etl内のAESで暗号化されたペイロードを復号してロードするために使用されます。暗号化されたペイロードはdnscfg.dllであり、これはdfsvcの代わりにvshostにロードすることができ、環境が.NETをサポートしていない場合に使用することができます。

図3.ClickOnceのコンフィギュレーション

悪意あるコンフィギュレーションはログ記録を無効にし、アプリケーションがリモートサーバーからdnscfg.dllを読み込むようにし、カスタムのAppDomainManagerを使用してdfsvc.exeの初期化時にDLLが実行されるようにします。永続性を確保するために、%APPDATA%\Local\Microsoft\WindowsApps\dfsvc.exeのスケジュールされたタスクが追加されます。

コアペイロード

DLL dnscfg.dll は、カスタムTCPベースのプロトコルであるDMTP(Duplex Message Transport Protocol)を使用して通信する、著しく難読化された.NET RAT(Client.TcpDmtp.dll) です。 観察された特徴から、これはFDMTPフレームワーク(v3.2.5.1)の更新版であると思われます。

図4.InitializeNewDomain

ペイロードは:

  • クラスタベースの解決を使用 (GetHostFromCluster)
  • トークン検証を実装
  • 永続的な実行ループに入る (LoopMessage)
  • DMTPを介した構造化されたリモートタスキングをサポート

接続が確立されると、マルウェアは永続的なループ(LoopMessage)に入り、リモートサーバーからのコマンドを受信できるようになります。

図5.DMTP接続関数

値は直接参照するのではなく、実行時に解決されるコンテナを通じて取得されます。文字列値は暗号化されたバイト配列(_0)に格納され、カスタムのXORベースの文字列復号ルーチン(dcsoft)によって復号されます。キーの下位16ビットは0xA61D(42525)とXORされて初期のXORキーが導出され、それに続くビットは文字列の長さと暗号化されたバイト配列へのオフセットを定義します。各文字は2つの暗号化されたバイトから再構成され、増加するキー値とXORされて、ペイロードで使用される平文文字列が生成されます。

図6.復号化された文字列

リソースセクションには複数の圧縮されたバイナリが埋め込まれており、その大多数はライブラリファイルです。

図7: リソース

モジュラー型フレームワークとプラグイン

ペイロードには以下を含む複数の圧縮ライブラリが埋め込まれています:

  • client.core.dll
  • client.dmtpframe.dll

Client.core.dllは、システムプロファイリング、C2通信、およびプラグイン実行に使用されるコアライブラリです。インプラントは、アンチウイルス製品、ドメイン名、HWID、CLRバージョン、管理者権限、ハードウェアの詳細、ネットワークの詳細、オペレーティングシステム、およびユーザーを含む情報を取得する機能を備えています。

図8: Client.Core.Info 関数

さらに、このコンポーネントはプラグインの読み込みを担当しており、バイナリおよびJSONベースのプラグイン実行の両方をサポートしています。これにより、プラグインは実行されるタスクに応じて異なる形式のコマンドやパラメータを受け取ることができます。

このフレームワークがプラグインのハッシュ、メソッド名、タスク識別子、呼び出し元追跡、引数の処理などの詳細を管理し、プラグインを環境内で一貫して実行することができます。実行管理に加えて、このライブラリはログ記録、通信、プロセス処理などの共通のランタイム機能へのアクセスをプラグインに提供します。

図9: Client.core 関数

client.dmtpframe.dllは次を処理します:

  • DMTP通信
  • ハートビートおよび再接続
  • レジストリを通じたプラグイン永続化:

HKCU\Software\Microsoft\IME\{id}

Client.dmtpframe.dllはTouchSocket DMTPネットワーキングライブラリ上に構築されており、リモートプラグインの管理を行います。このDLLは、ハートビートの維持、再接続処理、RPCスタイルのメッセージング、SSLサポート、およびトークンベースの認証を含むリモート通信機能を実装しています。このDLLは、永続化のためにHKCU/Software/Microsoft/IME/{id} のレジストリにプラグインを追加する機能も備えています。  

観測されたプラグイン

使用されたすべてのプラグインは判明していませんが、研究者たちは以下の4つを確認することができました:

  • Persist.WpTask.dll - リモートでスケジュールされたWindowsタスクを作成、削除、トリガーするために使用されます。
  • Persist.registry.dll - レジストリの永続性を管理するために使用され、レジストリ値の作成および削除、隠し永続化キーの操作が可能です。
  • Persist.extra.dll - メインフレームワークの読み込みと永続化に使用されます。
  • Assist.dll - リモートでファイルやコマンドを取得したり、システムプロセスを操作したりするために使用されます。
図10: IME レジストリに格納されたプラグイン
図11: プラグインリソース内の難読化されたスクリプト

Persist.extra.dll は、スクリプト"setup.log"を、読み込みメインフレームワークをロードおよび永続化するために使用されるモジュールです。バイナリのリソースセクションに格納されている難読化されたスクリプトは、.NET COMオブジェクトを作成し、永続化のためにレジストリキーHKCU\Software\Classes\TypeLib\ {9E175B61-F52A-11D8-B9A5-505054503030}\1.0\1\Win64 に追加します。このスクリプトの難読化を解除すると、"WindowsBase.dll”という別のDLLが明らかになります。

図12: スクリプトのレジストリエントリ

バイナリは5分ごとにicloud-cdn[.]netをチェックし、バージョン文字列を取得し、暗号化されたペイロードであるchecksum.binをダウンロードし、ローカルにC:\ProgramData\USOShared\Logs\checksum.etlとして保存し、ハードコードされたキーPOt_L[Bsh0=+@0a.を使用してAESで復号化し、Assembly.Load(byte[])を介して復号化されたアセンブリをメモリから直接ロードします。version.txtファイルは更新マーカーとして機能し、リモートのバージョンが変更された場合にのみ再ダウンロードされるようにします。また、ミューテックスは重複したインスタンスの起動を防ぎます。

図13: USOShared/Logs.

Checksum.etlはAESで復号化され、メモリにロードされ、別の.NET DLLである"Client.dll"がロードされます。このバイナリは前述の"dnscfg.dll"と同じものであり、脅威アクターがバージョンに基づいてメインフレームワークを更新することを可能にします。

まとめ

これらの事例で一貫して観測されたシーケンスは以下の通りです:

  • 正規の実行形式の取得
  • サイドローディング用DLLの取得
  • /GetClusterによるC2登録

侵入は単一の足場に依存しておらず、独立して更新、交換、再読み込みが可能なコンポーネントに分散されています。このアプローチは、中国系脅威アクターの手法と一致しています。Crimson Echoレポートで説明されているように、安定した特徴は技術的なものではなく、動作上の特徴です。インフラストラクチャは変化し、ペイロードも変わりますが、実行モデルは同じです。防御者にとって、その意味は明白です。それは個別の指標に基づく検知は急速に劣化するということです。動作のシーケンスや、アクセスがどのように構築され再確立されるかに基づく検知は、はるかに永続的です。

協力:Tara Gould (Malware Research Lead), Adam Potter (Senior Cyber Analyst), Emma Foulger (Global Threat Research Operations Lead), Nathaniel Jones (VP, Security & AI Strategy)

編集: Ryan Traill (Content Manager)


付録

検知モデルとトリガーされたインジケータのリストをIOCとともに提示します。

Indicators of Compromise (IoCs)

Test.zip - fc3959ebd35286a82c662dc81ca658cb

Dnscfg.dll - b2c8f1402d336963478f4c5bc36c961a

Client.TcpDmtp.dll - c52b4a16d93a44376f0407f1c06e0b

Browser_host.dll - c17f39d25def01d5c87615388925f45a

Client.DmtpFrame.dll - 482cc72e01dfa54f30efe4fefde5422d

Persist.Extra - 162F69FE29EB7DE12B684E979A446131

Persist.Registry - 067FBAD4D6905D6E13FDC19964C1EA52

Assist - 2CD781AB63A00CE5302ED844CFBECC27

Persist.WpTask - DF3437C88866C060B00468055E6FA146

Microsoft.VisualStudio.HostingProcess.Utilities.Sync.dll - c650a624455c5222906b60aac7e57d48

www.icloud-cdn[.]net

www.yahoo-cdn.it[.]com

154.223.58[.]142[AP8] [EF9]

MITRE ATT&CK テクニック

T1106 – ネイティブAPI

T1053.005 -スケジュールされたタスク

T1546.16 - コンポーネントオブジェクトモデルハイジャッキング

T1547.001 – レジストリ実行キー

T1511.001 -DLLインジェクション

T1622 – デバッガ回避

T1027 – ファイルおよび情報の難読化解除/復号化解除

T1574.001 - 実行フローハイジャック:DLL

T1620 – リフレクティブコードローディング

T1082 – システム情報探索

T1007 – システムサービス探索

T1030 – システムオーナー/ユーザー探索

T1071.001 - Webプロトコル

T1027.007 - 動的API解決

T1095 – 非アプリケーションレイヤプロトコル

Darktrace モデルアラート

·      Compromise / Beaconing Activity To External Rare

·      Compromise / HTTP Beaconing to Rare Destination

·      Anomalous File / Script from Rare External Location

·      Compromise / Sustained SSL or HTTP Increase

·      Compromise / Agent Beacon to New Endpoint

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Compromise / Quick and Regular Windows HTTP Beaconing

·      Compromise / High Volume of Connections with Beacon Score

·      Anomalous File / Anomalous Octet Stream (No User Agent)

·      Compromise / Repeating Connections Over 4 Days

·      Device / Large Number of Model Alerts

·      Anomalous Connection / Multiple Connections to New External TCP Port

·      Compromise / Large Number of Suspicious Failed Connections

·      Anomalous Connection / Multiple Failed Connections to Rare Endpoint

·      Device / Increased External Connectivity

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About the author
Tara Gould
Malware Research Lead
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