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August 7, 2023

Detection of an Evasive Credential Harvester | IPFS Phishing

Discover the emerging trend of malicious actors abusing the Interplanetary File System (IPFS) file storage protocol in phishing campaigns. Learn more here!
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Lena Yu
Cyber Security Analyst
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07
Aug 2023

IPFS Phishing Attacks

Phishing attacks continue to be one of the most common methods of infiltration utilized by threat actors and they represent a significant threat to an organization’s digital estate. As phishing campaigns typically leverage social engineering methods to evade security tools and manipulate users into following links, downloading files, or divulging confidential information. It is a relatively low effort but high-yield type of cyber-attack.

That said, in recent years security teams have become increasingly savvy to these efforts. Attackers are having to adapt and come up with novel ways to carry out their phishing campaigns. Recently, Darktrace has observed a rise in phishing attacks attempting to abuse the InterPlanetary File System (IPFS) in campaigns that are able to dynamically adapt depending on the target, making it extremely difficult for security vendors to detect and investigate.

What is a IPFS?

IPFS is a file storage protocol a peer-to-peer (P2P) network used for storing and sharing resources in a distributed file system [1]. It is also a file storage system similar in nature to other centralized file storage services like Dropbox and Google Drive.

File storage systems, like IPFS, are often abused by malicious actors, as they allow attackers to easily host their own content without maintaining infrastructure themselves. However, as these file storage systems often have legitimate usages, blocking everything related to file storages may cause unwanted problems and affect normal business operations. Thus, the challenge lies in differentiating between legitimate and malicious usage.

While centralized, web-based file storage services use a Client-Server model and typically deliver files over HTTP, IPFS uses a Peer-to-Peer model for storing and sharing files, as shown in Figure 1.

Figure 1: (a) shows the Client-Server model that centralized, web-based file storage services use. The resource is available on the server, and the clients access the resource from the server. (b) shows the Peer-to-Peer model that IPFS use. The resources are available on the peers.

To verify the authenticity and integrity of files, IPFS utilizes cryptographic hashes.

A cryptographic hash value is generated using a file’s content upon upload to IPFS. This is used to generate the Content Identifier (CID). IPFS uses Content Addressing as opposed to Location Addressing, and this CID is used to point to a resource in IPFS [4].

When a computer running IPFS requires a particular file, it asks the connected peers if they have the file with a specific hash. If a peer has the file with the matching hash, it will provide it to the requesting computer [1][6].

Taking down content on IPFS is much more difficult compared to centralized file storage hosts, as content is stored on several nodes without a centralized entity, as shown in Figure 2. To take down content from IPFS, it must be removed from all the nodes. Thus, IPFS is prone to being abused for malicious purposes.

Figure 2: When the resource is unavailable on the server for (a), all the clients are unable to access the resource. When the resource is unavailable on one of the peers for (b), the resources are still available on the other peers.

The domains used in these IPFS phishing links are gateways that enable an HTTPS URL to access resources within the distributed IPFS file system.

There are two types of IPFS links, the Path Gateway and Subdomain Gateway [1].

Path Gateways have a fixed domain/host and identifies the IPFS resource through a resource-identifying string in the path. The Path Gateway has the following structure:

•       https://<gateway-host>.tld/ipfs/<CID>/path/to/resource

•       https://<gateway-host>.tld/ipns/<dnslink/ipnsid>/path/to/resource

On the other hand, Subdomain Gateways have a resource-identifying string in the subdomain. Subdomain Gateways have the following structure:

•       https://<cidv1b32>.ipfs.<gateway-host>.tld/path/to/resource

One gateway domain serves the same role as any other, which means attackers can easily change the gateways that are used.

Thus, these link domains involved in these attacks can be much more variable than the ones in traditional file storage attacks, where a centralized service with a single domain is used (e.g., Dropbox, Google Docs), making detecting the malicious use of IPFS extremely challenging for traditional security vendors. Through its anomaly-based approach to threat detection, Darktrace/Email™ is consistently able to identify such tactics and respond to them, preventing malicious actors from abusing file storage systems life IPFS.

IPFS Campaign Details

In several recent examples of IPFS abuse that Darktrace detected on a customer’s network, the apparent end goal was to harvest user credentials. Stolen credentials can be exploited by threat actors to further their attacks on organizations by escalating their privileges within the network, or even sold on the dark web.

Darktrace detected multiple IPFS links sent in malicious emails that contained the victim’s email address. Based on the domain in this email address, users would then be redirected to a fake login page that uses their organizations’ webpage visuals and branding to convince targets to enter their login details, unknowingly compromising their accounts in the process.

Figure 3: The credential harvester changes visuals depending on the victim’s email address specified in the URL.

These IPFS credential harvesting sites use various techniques to evade detection the detection of traditional security tools and prevent further analysis, such as obfuscation by Percent Encoding and Base64 Encoding the code.

There are also other mechanisms put into place to hinder investigation by security teams. For example, some IPFS credential harvester sites investigated by Darktrace did not allow right clicking and certain keystrokes, as a means to make post-attack analysis more difficult.

Figure 4: The code shows that it attempts to prevent certain keystrokes.

In the campaign highlighted in this blog, the following IPFS link was observed:

hxxps://ipfs[.]io/ipfs/QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP?filename=at ob.html#<EmailAddress>

This uses a Path Gateway, as it identifies the IPFS resource through a resource-identifying string in the path. The CID is QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP in this case.

It makes a GET request to image[.]thum[.]io and logo[.]clearbit[.]com as shown in Figure 5. The image[.]thum[.]io is a Free Website Screenshot Generator, that provides real-time screenshot of websites [2]. The logo[.]clearbit[.]com is used to lookup company logos using the domain [3]. These visuals are integrated into the credential harvester site. Figure 6 shows the domain name being extracted from the victim’s email address and used to obtain the visuals.

Figure 5: The GET requests to image[.]thum[.]io and logo[.]clearbit[.].
Figure 6: The code shows that it utilizes the domain name from the victim’s email address to obtain the visuals from logo.clearbit[.]com and image[.]thum.io.

The code reveals the credential POST endpoint as shown in Figure 16. When credentials are submitted, it makes a POST request to this endpoint as shown in Figure 7.

Figure 7: The credential POST endpoint can be seen inside the code.
Figure 8: The Outlook credential harvester will redirect to the real Outlook page when wrong credentials are submitted multiple times.

From the IPFS link alone, it is difficult to determine whether it leads to a malicious endpoint, however Darktrace has consistently identified emails containing these IPFS credential harvesting links as phishing attempts.

Darktrace Coverage

During one case of IPFS abuse detected by Darktrace in March 2023, a threat actor sent malicious emails with the subject “Renew Your E-mail Password” to 55 different recipients at. The sender appeared to be the organization’s administrator and used their internal domain.

Figure 9: Darktrace/Email’s detection of the “Renew Your E-mail Password” emails from “administrator”. These were all sent at 2023.03.21 02:39 UTC.

However, Darktrace recognized that the email did not pass Sender Policy Framework (SPF), and therefore it could not be validated as being sent from the organization’s domain. Darktrace also detected that the email contained a link to “ipfs.io, the official IPFS gateway. This was identified as a spoofing and phishing attempt by Darktrace/Email.

Figure 10: The Darktrace/Email overview tab shows the Anomaly Indicators, History, Association, and Validation information of this sender. It contained a link to “ipfs.io”, and did not pass SPF.

Following the successful identification of the malicious emails, Darktrace RESPOND™ took immediate autonomous action to prevent them from leading to potentially damaging network compromise. For email-based threats, Darktrace RESPOND is able to carry out numerous actions to stop malicious emails and reduce the risk of compromise. In response to this specific incident, RESPOND took multiple preventative actions (as seen in Figure 11), including include lock link, an action that prevents access to URLs deemed as suspicious, send to junk, an action that automatically places emails in the recipient’s junk folder, and hold message, the most severe RESPOND action that prevents malicious emails from reaching the recipients inbox at all.

Figure 11: The Darktrace/Email model tab shows all the models that triggered on the email and the associated RESPOND actions.
Figure 12: The ipfs.io link used in this email contains the recipient’s email address, and has a CID of QmfDDxLWoLiqFURX6dUZcsHxVBP1ZnM21H5jXGs1ffNxtP. It has a Darktrace Domain Rarity Score of 100
Figure 13: The IPFS credential harvester that uses the organization’s website’s visuals.

Further investigation revealed that the IPFS link contained the recipients’ email address, and when clicked led to a credential harvester that utilized the same visuals and branding as the customer’s website.

Concluding Thoughts

Ultimately, despite the various tactics employed threat actors to evade the detection of traditional security tools, Darktrace was able to successfully detect and mitigate these often very fruitful phishing attacks that attempted to abuse the IPFS file storage system.

As file storage platforms like IPFS do have legitimate business uses, blocking traffic related to file storage is likely to negatively impact the day-to-day operations of an organization. The challenge security teams face is to differentiate between malicious and legitimate uses of such services, and only act on malicious cases. As such, it is more important than ever for organizations to have an effective anomaly detection tool in place that is able to identify emerging threats without relying on rules, signatures or previously observed indicators of compromise (IoC).

By leveraging its Self-Learning AI, Darktrace understands what represents expected activity on customer networks and can recognize subtle deviations from expected behavior, that may be indicative of compromise. Then, using its autonomous response capabilities, Darktrace RESPOND is able to instantly and autonomously take action against emerging threats to stop them at the earliest possible stage.

Credit to Ben Atkins, Senior Model Developer for their contribution to this blog.

Appendices

Example IOCs

Type: URL

IOC: hxxps://ipfs[.]io/ipfs/QmfDDxLWoLi qFURX6dUZcsHxVBP1ZnM21H5jXGs

1ffNxtP?filename=atob.html#<Email Address>

Description: Path Gateway link

Type: URL

IOC: hxxps://bafybeibisyerwlu46re6rxrfw doo2ubvucw7yu6zjcfjmn7rqbwcix2 mku.ipfs[.]dweb.link/webn cpmk.htm?bafybeigh77sqswniy74nzyklybstfpkxhsqhpf3qt26nwnh4wf2vv gbdaybafybeigh77sqswniy74nzyklybstfpkxhsqhpf3qt26nwnh4wf2vvgbda y#<EmailAddress>

Description: Subdomain Gateway link

Relevant Darktrace DETECT Models

•       Spoof / Internal Domain from Unexpected Source + New Unknown Link

•       Link / High Risk Link + Low Sender Association

•       Link / New Correspondent Classified Link

•       Link / Watched Link Type

•       Proximity / Phishing + New activity

•       Proximity / Phishing + New Address Known Domain

•       Spoof / Internal Domain from Unexpected Source + High Risk Link

References

[1]    https://docs.ipfs.tech/

[2]    https://www.thum.io/

[3]    https://clearbit.com/logo

[4]    https://filebase.com/blog/ipfs-content-addressing-explained/

[5]    https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/the-attack-of-the-chameleon-phishing-page/

[6]    https://wiki.ipfsblox.com/

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
Lena Yu
Cyber Security 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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