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January 17, 2024

Detecting Trusted Network Relationship Abuse

Discover how Darktrace DETECT and the SOC team responded to a network compromise via a trusted partner relationship with this case study.
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
Adam Potter
Senior Cyber Analyst
Written by
Taylor Breland
Analyst Team Lead, San Francisco
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17
Jan 2024

Trusted relationships between organizations and third parties have become an increasingly popular target for cyber threat actors to gain access to sensitive networks. These relationships are typically granted by organizations to external or adjacent entities and allow for the access of internal resources for business purposes.1 Trusted network relations can exist between constituent elements of an overarching corporation, IT-service providers and their customers, and even implicitly between IT product vendors and their customers.

Several high-profile compromises have occurred due to the leveraging of privileged network access by such third parties. One prominent example is the 2016 DNC network attack, in which the trust between the Democratic Congressional Campaign Committee (DCCC) and the Democratic National Committee (DNC) was exploited. Supply chain attacks, which also leverage the implicit trust between IT vendors and customers, are also on the rise with some estimates projecting that by 2025, almost half of all organizations will be impact by supply chain compromises.2 These trends may also be attributed to the prevalence of remote work as well as the growth in IT-managed service providers.3

Given the nature of such network relationships and threat techniques, signatures-based detection is heavily disadvantaged in the identification and mitigation of such trust abuses; network administrators cannot as easily use firewalls to block IPs that need access to networks. However, Darktrace DETECT™, and its Self-Learning AI, has proven successful in the identification and mitigation of these compromises. In September 2023, Darktrace observed an incident involving the abuse of such a trusted relationship on the network of a healthcare provider.

Attack Overview

In early September 2023, a Darktrace customer contacted the Darktrace Security Operations Center (SOC) through the Ask the Expert™ (ATE) service requesting assistance with suspicious activity detected on their network. Darktrace had alerted the customer’s security team to an unknown device that had appeared on their network and proceeded to perform a series of unexpected activities, including reconnaissance, lateral movement, and attempted data exfiltration.

Unfortunately for this customer, Darktrace RESPOND™ was not enabled in autonomous response mode at the time of this compromise, meaning any preventative actions suggested by RESPOND had to be applied manually by the customer’s security team after the fact.  Nevertheless, Darktrace’s prompt identification of the suspicious activity and the SOC’s investigation helped to disrupt the intrusion in its early stages, preventing it from developing into a more disruptive compromise.

Initial Access

Darktrace initially observed a new device that appeared within the customers internal network with a Network Address Translated (NAT) IP address that suggested remote access from a former partner organization’s network. Further investigation carried out by the customer revealed that poor credential policies within the partner’s organization had likely been exploited by attackers to gain access to a virtual desktop interface (VDI) machine.

Using the VDI appliance of a trusted associate, the threat actor was then able to gain access to the customer’s environment by utilizing NAT remote access infrastructure. Devices within the customer’s network had previously been utilized for remote access from the partner network when such activity was permitted and expected. Since then, access to this network was thought to have been removed for all parties. However, it became apparent that the remote access functionality remained operational. While the customer also had firewalls within the environment, a misconfiguration at the time of the attack allowed inbound port access to the remote environment resulting in the suspicious device joining the network on August 29, 2023.

Internal Reconnaissance

Shortly after the device joined the network, Darktrace observed it carrying out a string of internal reconnaissance activity. This activity was initiated with internal ICMP address connectivity, followed by internal TCP connection attempts to a range of ports associated with critical services like SMB, RDP, HTTP, RPC, and SSL. The device was also detected attempting to utilize privileged credentials, which were later identified as relating to a generic multi-purpose administrative account. The threat actor proceeded to conduct further internal reconnaissance, including reverse DNS sweeps, while also attempting to use six additional user credentials.

In addition to the widespread internal connectivity, Darktrace observed persistent connection attempts focused on the RDP and SMB protocols. Darktrace also detected additional SMB enumeration during this phase of the attacker’s reconnaissance. This reconnaissance activity largely attempted to access a wide variety of SMB shares, previously unseen by the host to identify available share types and information available for aggregation. As such, the breach host conducted a large spike in SMB writes to the server service (srvsvc) endpoint on a range of internal hosts using the credential: extramedwb. SMB writes to this endpoint traditionally indicate binding attempts.

Beginning on August 31, Darktrace identified a new host associated with the aforementioned NAT IP address. This new host appeared to have taken over as the primary host conducting the reconnaissance and lateral movement on the network taking advantage of the VDI infrastructure. Like the previous host, this one was observed sustaining reconnaissance activity on August 31, featuring elevated SMB enumeration, SMB access failures, RDP connection attempts, and reverse DNS sweeps.  The attackers utilized several credentials to execute their reconnaissance, including generic and possibly default administrative credentials, including “auditor” and “administrator”.

Figure 1: Advanced Search query highlighting anomalous activity from the second observed remote access host over the course of one week surrounding the time of the breach.

Following these initial detections by Darktrace DETECT, Darktrace’s Cyber AI Analyst™ launched an autonomous investigation into the scanning and privileged internal connectivity and linked these seemingly separate events together into one wider internal reconnaissance incident.

Figure 2: Timeline of an AI Analyst investigation carried out between August 29 and August 31, 2023, during which it detected an increased volume of scanning and unusual privileged internal connectivity.

Lateral Movement

Following the reconnaissance activity performed by the new host observed exploiting the remote access infrastructure, Darktrace detected an increase in attempts to move laterally within the customer’s network, particularly via RPC commands and SMB file writes.

Specifically, the threat actor was observed attempting RPC binds to several destination devices, which can be used in the calling of commands and/or the creation of services on destination devices. This activity was highlighted in repeated failed attempts to bind to the ntsvcs named pipe on several destination devices within the network. However, given the large number of connection attempts, Darktrace did also detect a number of successful RPC connections.

Darktrace also detected a spike in uncommon service control (SVCCTL) ExecMethod, Create, and Start service operations from the breach device.

Figure 3: Model breach details noting the affected device performing unsuccessful RPC binds to endpoints not supported on the destination device.

Additional lateral movement activity was performed using the SMB/NTLM protocols. The affected device also conducted a series of anonymous NTLM logins, whereby NTLM authentication attempts occurred without a named client principal, to a range of internal hosts. Such activity is highly indicative of malicious or unauthorized activity on the network. The host also employed the outdated SMB version 1 (SMBv1) protocol during this phase of the kill chain. The use of SMBv1 often represents a compliance issue for most networks due to the high number of exploitable vulnerabilities associated with this version of the protocol.

Lastly, Darktrace identified the internal transfer of uncommon executables, such as ‘TRMtZSqo.exe’, via SMB write. The breach device was observed writing this file to the hidden administrative share (ADMIN$) on a destination server. Darktrace recognized that this activity was highly unusual for the device and may have represented the threat actor transferring a malicious payload to the destination server for further persistence, data aggregation, and/or command and control (C2) operations. Further SMB writes of executable files, and the subsequent delete of these binaries, were observed from the device at this time. For example, the additional executable ‘JAqfhBEB.exe’ was seen being deleted by the breach device. This deletion, paired with the spike in SVCCTL Create and Start operations occurring, suggests the transfer, execution, and removal of persistence and data harvesting binaries within the network.

Figure 4: AI Analyst details highlighting the SMB file writes of the unusual executable from the remote access device during the compromise.

Conclusion

Ultimately, Darktrace was able to successfully identify and alert for suspicious activity being performed by a threat actor who had gained unauthorized access to the customer’s network by abusing one of their trusted relationships.

The identification of scanning, RPC commands and SMB sessions directly assisted the customer in their response to contain and mitigate this intrusion. The investigation carried out by the Darktrace SOC enabled the customer to promptly triage and remediate the attack, mitigating the potential damage and preventing the compromise from escalating further. Had Darktrace RESPOND been enabled in autonomous response mode at the time of the attack, it would have been able to take swift action to inhibit the scanning, share enumerations and file write activity, thereby thwarting the attacker’s network reconnaissance and lateral movement attempts.

By exploiting trusted relationships between organizations, threat actors are often able to bypass traditional signatured-based security methods that have previously been reconfigured to allow and trust connections from and to specific endpoints. Rather than relying on the configurations of specific rules and permitted IP addresses, ports, and devices, Darktrace DETECT’s anomaly-based approach to threat detection meant it was able to identify suspicious network activity at the earliest stage, irrespective of the offending device and whether the domain or relationship was trusted.

Credit to Adam Potter, Cyber Security Analyst, Taylor Breland, Analyst Team Lead, San Francisco.

Darktrace DETECT Model Breach Coverage:

  • Device / ICMP Address Scan
  • Device / Network Scan
  • Device / Suspicious SMB Scanning Activity
  • Device / RDP Scan
  • Device / Possible SMB/NTLM Reconnaissance
  • Device / Reverse DNS Sweep
  • Anomalous Connection / SMB Enumeration
  • Device / Large Number of Model Breaches
  • Anomalous Connection / Suspicious Activity On High Risk Device
  • Unusual Activity / Possible RPC Recon Activity
  • Device / Anonymous NTLM Logins
  • Anomalous Connection / Unusual SMB Version 1 Connectivity
  • Device / Repeated Unknown RPC Service Bind Errors
  • Anomalous Connection / New or Uncommon Service Control
  • Compliance / SMB Drive Write
  • Anomalous File / Internal / Unusual Internal EXE File Transfer
  • Device / Multiple Lateral Movement Model Breaches

AI Analyst Incidents:

  • Scanning of Multiple Devices
  • Extensive Unusual RDPConnections
  • SMB Write of Suspicious File
  • Suspicious DCE-RPC Activity

MITRE ATT&CK Mapping

  • Tactic: Initial Access
  • Technique: T1199 - Trusted Relationship
  • Tactic: Discovery
  • Technique:
  • T1018 - Remote System Discovery
  • T1046 - Network Service Discovery
  • T1135 - Network Share Discovery
  • T1083 - File and Directory Discovery
  • Tactic: Lateral Movement
  • Technique:
  • T1570 - Lateral Tool Transfer
  • T1021 - Remote Services
  • T1021.002 - SMB/Windows Admin Shares
  • T1021.003 - Distributed Component Object Model
  • T1550 - Use Alternate Authentication Material

References

1https://attack.mitre.org/techniques/T1199/

2https://www.cloudflare.com/learning/insights-supply-chain-attacks/

3https://newsroom.cisco.com/c/r/newsroom/en/us/a/y2023/m09/companies-reliance-on-it-managed-services-increases-in-2023-sector-valued-at-us-472-billion-globally.html#:~:text=IT%20channel%20partners%20selling%20managed,US%24419%20billion%20in%202022.

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
Adam Potter
Senior Cyber Analyst
Written by
Taylor Breland
Analyst Team Lead, San Francisco

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April 17, 2026

中国系サイバー作戦の進化 - それはサイバーリスクおよびレジリエンスにとって何を意味するか

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サイバーセキュリティにおいては、これまではインシデント、侵害、キャンペーン、そして脅威グループを中心にリスクを整理してきました。これらの要素は現在も重要です -しかし個別のインシデントにとらわれていては、エコシステム全体の形成を見逃してしまう危険があります。国家が支援する攻撃者グループは、個別の攻撃を実行したり短期的な目標を達成したりするためだけではなく、サイバー作戦を長期的な戦略上の影響力を構築するために使用するようになっています。  

当社の最新の調査レポート、Crimson Echoにおいてもこうした状況にあわせて視点を変えています。キャンペーンやマルウェアファミリー、あるいはアクターのラベルを個別のイベントとして分類するのではなく、ダークトレースの脅威調査チームは中国系グループのアクティビティを長期的に連続した行動として分析しました。このように視野を拡大することで、これらの攻撃者がさまざまな環境内でどのように存在しているか、すなわち、静かに、辛抱強く、持続的に、そして多くのケースにおいて識別可能な「インシデント」が発生するかなり前から下準備をしている様子が明らかになりました。  

中国系サイバー脅威のこれまでの変化

中国系サイバーアクティビティは過去20年間において4つのフェーズで進化してきたと言えます。初期の、ボリュームを重視したオペレーションは1990年代にから2000年代初めに見られ、それが2010年代にはより構造化された、戦略に沿った活動となり、そして現在の高度な適応性を備えた、アイデンティティを中心とした侵入へと進化しています。  

現在のフェーズの特徴は、大規模、攻撃の自制、そして永続化です。攻撃者はアクセスを確立し、その戦略的価値を評価し、維持します。これはより全体的な変化を反映したものです。つまりサイバー作戦は長期的な経済的および地政学的戦略に組み込まれる傾向が強まっているということです。デジタル環境へのアクセス、特に国家の重要インフラやサプライチェーン、先端テクノロジーにつながるものは、ある種の長期的な戦略的影響力と見られるようになりました。  

複雑な問題に対するダークトレースのビヘイビア分析アプローチ

国家が支援するサイバーアクティビティを分析する際、難しい問題の1つはアトリビューションです。従来のアプローチは多くの場合、特定の脅威グループ、マルウェアファミリー、あるいはインフラに判定を依存していました。しかしこれらは絶えず変化するものであり、さらに中国系オペレーションの場合、しばしば重複が見られます。

Crimson Echo は2022年7月から2025年9月の間の3年間にDarktrace運用環境で観測された異常なアクティビティを回顧的に分析した結果です。ビヘイビア検知、脅威ハンティング、オープンソースインテリジェンス、および構造化されたアトリビューションフレームワーク(Darktrace Cybersecurity Attribution Framework)を用いて、数十件の中~高確度の事例を特定し、繰り返し発生しているオペレーションのパターンを分析しました。  

この長期的視野を持ったビヘイビア中心型アプローチにより、ダークトレースは侵入がどのように展開していくかについての一定のパターンを特定することができ、動作のパターンが重要であることがあらためて確認されました。  

データが示していること

分析からいくつかの明確な傾向が浮かび上がりました:

  • 標的は戦略的に重要なセクターに集中していたのです。データセット全体で、侵入の88%は重要インフラと分類される、輸送、重要製造業、政府、医療、ITサービスを含む組織で発生しています。   
  • 戦略的に重要な西側経済圏が主な焦点です。米国だけで、観測されたケースの22.5%を占めており、ドイツ、イタリア、スペイン、および英国を含めた主要なヨーロッパの経済圏と合わせると侵入の半数以上(55%)がこれらの地域に集中しています。  
  • 侵入の63%近くがインターネットに接続されたシステムのエクスプロイトから始まっており、外部に露出したインフラの持続的リスクがあらためて浮き彫りになりました。  

サイバー作戦の2つのモデル

データセット全体で、中国系のアクティビティは2つの作戦モデルに従っていることが確認されました。  

1つ目は“スマッシュアンドグラブ”(強奪)型と表現することができます。これらはスピードのために最適化された短期型の侵入です。攻撃者はすばやく動き  – しばしば48時間以内にデータを抜き出し  – ステルス性よりも規模を重視します。これらの侵害の期間の中央値は10日ほどです。検知の危険を冒しても短期的利益を得ようとしていることが明らかです。  

2つ目は“ローアンドスロー”(低速)型です。これらのオペレーションはデータセット内ではあまり多くありませんでしたが、潜在的影響はより重大です。ここでは攻撃者は持続性を重視し、アイデンティティシステムや正規の管理ツールを通じて永続的なアクセスを確立し、数か月間、場合によっては数年にわたって検知されないままアクセスを維持しようとします。1つの注目すべきケースでは、脅威アクターは環境に完全に侵入して永続性を確立し、600日以上経ってからようやく再浮上した例もありました。このようなオペレーションの一時停止は侵入の深さと脅威アクターの長期的な戦略的意図の両方を表しています。このことはサイバーアクセスが長期にわたって保有し活用するべき戦略的資産であることを示しており、これは最も戦略的に重要なセクターにおいて最もよく見られたパターンです。  

同じ作戦エコシステムにおいて両方のモデルを並行して利用し、標的の価値、緊急性、意図するアクセスに基づいて適切なモデルを選択することも可能だという点に注意することも重要です。“スマッシュアンドグラブ” モデルが見られたからといって諜報活動が失敗したとのみ解釈すべきではなく、むしろ目標に沿った作戦上の選択かもしれないと見るべきでしょう。“ローアンドスロー” 型は粘り強い活動のために最適化され、“スマッシュアンドグラブ” 型はスピードのために最適化されています。どちらも意図的な作戦上の選択と見られ、必ずしも能力を表していません。  

サイバーリスクを再考する

多くの組織にとって、サイバーリスクはいまだに一連の個別のイベントとして位置づけられています。何かが発生し、検知され、封じ込められ、組織はそれを乗り越えて前に進みます。しかし永続的アクセスは、特にクラウド、アイデンティティベースのSaaSやエージェント型システム、そして複雑なサプライチェーンネットワークが相互接続された環境では、重大な持続的露出リスクを作り出します。システムの中断やデータの流出が発生していなくても、そのアクセスによって業務や依存関係、そして戦略的意思決定についての情報を得られるかもしれません。サイバーリスクはますます長期的な競合情報収集に似てきています。

その影響はSOCだけの問題ではありません。組織はガバナンス、可視性、レジリエンスについての考え方を見直し、サイバー露出をインシデント対応の問題ではなく構造的なビジネスリスクとして扱う必要があります。  

次の目標

この調査の目的は、これらの脅威の仕組みについてより明確な理解を提供することにより、防御者がより早期にこれらを識別しより効果的に対応できるようにすることです。これには、インジケーターの追跡からビヘイビアの理解にシフトすること、アイデンティティプロバイダーを重要インフラリスクとして扱うこと、サプライヤーの監視を拡大すること、迅速な封じ込めのための能力に投資すること、などが含まれます。  

ダークトレースの最新調査、”Crimson Echo: ビヘイビア分析を通じて中国系サイバー諜報技術を理解する” についてより詳しく知るには、ビジネスリーダー、CISO、SOCアナリストに向けたCrimson Echoレポートのエグゼクティブサマリーを ここからダウンロードしてください。 

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

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Ed Jennings
President and CEO
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