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July 14, 2021

Darktrace Detects Egregor Ransomware in Customer Environment

See how Darktrace managed to detect and eliminate an Egregor ransomware extortion attack in a customer environment without the use of any 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
Justin Fier
SVP, Red Team Operations
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14
Jul 2021

Ransomware groups are coming and going faster than ever. In June alone we saw Avaddon release its decryption keys unprompted and disappear from sight, while members of CLOP were arrested in Ukraine. The move follows increasing pressure from the US intelligence community and Ukrainian authorities, who took down Egregor ransomware back in February. Egregor had only been around since September 2020. It survived less than six months.

But these gangs aren’t going away – they are simply going underground. Despite ‘closures’, cases of ransomware continue to rise and new threat actors and independent hackers pop up on the Dark Web every day.

As malware actors lay low and resurface with new variants, keeping up with the stream of signatures and new strains has become untenable. This blog studies the techniques, tools and procedures (TTPs) observed from a real-life Egregor intrusion last autumn, which showcases how Self-Learning AI detected the attack without relying on signatures.

Egregor: Maze reloaded

150 companies
worldwide have fallen victim to Egregor.

Law enforcement authorities have been busy this year. Aside from Egregor and CLOP, actions were taken against Netwalker in Bulgaria and the US, while Europol announced that an international operation had disrupted the core infrastructure of Emotet, one of the most prominent botnets of the past decade.

All parties – from governments down to individual businesses – are taking the threat of ransomware more seriously. In response to this added pressure, cyber-criminals often prefer to shut up shop rather than hang around long enough to be arrested.

DarkSide famously closed down after the Colonial Pipeline attacks, only nine months after it had been created. An admin from the Ziggy gang announced that it would issue refunds and was looking for a job as a threat hunter.

“Hi. I am Ziggy ransomware administrator. We decided to publish all decryption keys.

We are very sad about what we did. As soon as possible, all the keys will be published in this channel.”

Take this apology with a pinch of salt. The players which have ‘closed down’ have not had a change of heart, they’ve just changed tack. Different names and new infrastructure can help keep the heat off and circumvent US sanctions or federal scrutiny. PayloadBIN (a new ransomware which cropped up last month), WastedLocker, Dridex, Hades, Phoenix, Indrik Spider… all just aliases for one single group: Evil Corp.

The FBI are becoming more aggressive in their methods of infiltration and disruption, so it is likely we will see more of these U-turns and guerrilla-style tactics. Temporary pop-up gangs are an emerging trend in place of large, established enterprises like REvil, whose websites also vanished following the attack against Kaseya. And there is no doubt we will continue to witness these ‘exit scams’, where groups retire and re-brand, like Maze did last September, when it came back as Egregor.

Darktrace detects malware regardless of the name or strain. It stopped Maze last year, and, as we shall see below, it stopped its successor Egregor, even though the code and C2 endpoints used in the intrusion had never been seen before.

30%
of ransom profits are taken by Egregor developers.

Egregor ransomware attack

Back in November 2020, Egregor was in full bloom, targeting major organizations and exfiltrating data in ‘double extortion’ attacks. At a logistics company in Europe with around 20,000 active devices, during a Darktrace Proof of Value (POV) trial, Egregor struck.

Figure 1: Timeline of the attack. The overall dwell time — from first C2 connection to encryption — was five days.

As a Ransomware-as-a-Service (RaaS) gang, it appears Egregor had partnered with botnet providers to facilitate initial access. In this case, the compromised device carried signs of prior infection. It was seen connecting to an apparent Webex endpoint, before connecting to the Akamai doppelganger, amajai-technologies[.]network. This activity was followed by a number of command and control (C2) and exfiltration-related breaches.

Three days later, Darktrace observed lateral movement over HTTPS. Another device – a server – was seen connecting to the amajai host. This server wrote unusual numeric exectuables to shared SMB drives and took new service control. A third host then made a ~50GB upload to a rare IP.

Figure 2: Cyber AI Analyst summarizes the initial C2 and unusual SMB writes in a similar incident, followed later by a large upload to a rare external endpoint.

After two days, encryption began. This triggered multiple hosts breaches. On the final day, the attacker made large uploads to various endpoints, all from ostensibly compromised hosts.

Retrospective analysis

$4m
is the highest recorded cost of an Egregor ransom.

If the attack had not been neutralized at this point, it could have resulted in significant financial loss and reputational damage for the company. The two-pronged attack enabled Egregor both to encrypt critical resources and to exfiltrate them, with a view to publicizing sensitive data if the victims refused to pay up.

The affiliates who deployed the ransomware in this case were highly skilled. They leveraged a number of sophisticated techniques including the use of a large number of C2 endpoints, with doppelgangers and off-the-shelf tools.

The adoption of HTTPS for lateral movement and reconnaissance reduced lateral noise for scans and enumeration. The complex C2 had numerous endpoints, some of which were doppelgangers of legitimate sites. Furthermore, some malware was downloaded as masqueraded files: the mimetype Octet Streams were downloaded as ‘g.pixel’. These three tactics helped obfuscate the attacker’s movements and trick traditional security tools.

Ransomware attacks are occurring at a speed that even five years ago was unimaginable. In this case, the overall dwell time was less than a week, and part of the attack happened out of office hours. This highlights the need for Autonomous Response, which can keep up with novel threats and does not rely on humans being in the loop to contain cyber-attacks.

Gone today, here tomorrow

Egregor was busted in February, but we may well see it resurface under a different name and with modified code. If and when this happens, signatures will be of no use. Catching never-before-seen ransomware, which employs novel methods of intrusion and extortion, requires a different approach.

The endpoint in the case study above is now associated via open-source intelligence (OSINT) with Cobalt Strike. But at the time of the investigation, the C2 was unlisted. Similarly, the malware was unknown to OSINT and thus evaded signature-based tools.

Despite this, Self-Learning AI detected every single stage of the in-progress attack. No action was taken as it was only a trial POV so Darktrace had no remote access in the environment. However, after seeing the power of the technology, the organization decided to implement Darktrace across its digital estate.

Thanks to Darktrace analyst Roberto Romeu for his insights on the above threat find.

Learn how Darktrace stops Egregor and all forms of ransomware

Darktrace model detections:

  • Agent Beacon to New Endpoint
  • Agent Beacon (Long Period)
  • Agent Beacon (Medium Period)
  • Agent Beacon (Short Period)
  • Anomalous Octet Stream
  • Anomalous Server Activity / Outgoing from Server
  • Anomalous SMB Followed By Multiple Model Breaches
  • Anomalous SSL without SNI to New External
  • Beaconing Activity To External Rare
  • Beacon to Young Endpoint
  • Data Sent To New External Device
  • Data Sent to Rare Domain
  • DGA Beacon
  • Empire Python Activity Pattern
  • EXE from Rare External Location
  • High Volume of Connections with Beacon Score
  • High Volume of New or Uncommon Service Control
  • HTTP Beaconing to Rare Destination
  • Large Number of Model Breaches
  • Long Agent Connection to New Endpoint
  • Low and Slow Exfiltration
  • Multiple C2 Model Breaches
  • Multiple Connections to New External TCP Port
  • Multiple Failed Connections to Rare Endpoint
  • Multiple Lateral Movement Model Breaches
  • Network Scan
  • New Failed External Connections
  • New or Uncommon Service Control
  • Numeric Exe in SMB Write
  • Rare External SSL Self-Signed
  • Slow Beaconing Activity To External Rare
  • SMB Drive Write
  • SMB Enumeration
  • SSL Beaconing to Rare Destination
  • SSL or HTTP Beacon
  • Suspicious Beaconing Behaviour
  • Suspicious Self-Signed SSL
  • Sustained SSL or HTTP Increase
  • Quick and Regular Windows HTTP Beaconing
  • Uncommon 1 GiB Outbound
  • Unusual BITS Activity
  • Unusual Internal Connections
  • Unusual SMB Version 1 Connectivity
  • Zip or Gzip from Rare External Location

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
Justin Fier
SVP, Red Team Operations

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June 2, 2026

Stopping Stealth Attacks with Precision: How Núclea Prevented a Breach Without Disruption

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Núclea is a Brazilian data and technology company that supports the country’s financial system by delivering digital services exclusively to banks and financial institutions. Operating in an environment where trust, availability, and data integrity are critical, the company faces a threat landscape that has evolved rapidly—particularly with the rise of AI-driven cyberattacks.

Brazil has experienced a wave of successful cyber incidents targeting financial institutions, many of them enabled by insiders or compromised credentials. The result was a noticeable shift in attacker strategy: instead of focusing on end customers, threat actors began targeting the institutions and platforms that underpin the financial ecosystem itself.

“Attacks became far more directed and contextual,” explains Guilherme, who leads incident response within Núclea’s security platform engineering team. “They weren’t noisy or obviously malicious—they were precise, patient, and designed to blend into normal operations.”

That precision was on full display in January 2026, when Núclea faced one of the most convincing phishing attacks the team had seen.

A real attack, built on trust and context

The attack began with a seemingly routine email.

It was sent from a real Brazilian government institution, using legitimate infrastructure and valid credentials that were later confirmed to have been compromised. Núclea had an established, ongoing relationship with this organization, and the email’s language, tone, and subject matter aligned perfectly with the type of communication the recipient team handled every day.

Attached to the email was a PDF document containing content that looked entirely legitimate.

The problem? A single URL embedded inside that PDF.

“The message itself was correct. The sender was real. The context was familiar. Even the document content made sense,” Guilherme explains. “There was just one small element that didn’t belong.”

That small detail was enough to initiate a full attack chain.

What the attackers were trying to do

If clicked, the URL would have downloaded a malicious payload designed to:

  • Collect information about the user and device
  • Identify where the system was located within the financial ecosystem
  • Install remote access tools to maintain control
  • Deploy an infostealer to extract sensitive data
  • Execute anti-forensic scripts to erase traces of the intrusion

In other words, it was a carefully engineered operation designed for persistence and stealth, not immediate disruption.

The attack also employed urgency—a classic social engineering technique. When the link didn’t open as expected, employees requested assistance from the security team, insisting the document was important and needed to be accessed quickly.

This is precisely the kind of scenario where traditional security tools struggle: almost everything about the interaction is legitimate.

Where Darktrace made the difference

Instead of blocking the entire message or relying on known indicators of compromise, Darktrace focused on behavioral context.

Darktrace recognized:

  • That the sending organization was normally trusted
  • That the communication pattern matched historical behavior
  • That the PDF content itself was not suspicious

But it also identified that the URL embedded within the document deviated from established behavioral patterns.

Rather than disrupting business operations, Darktrace took precise action: it rewrote the URL, preventing the malicious download while leaving the rest of the email untouched.

“When we analyzed it afterward, it became clear how dangerous the attack would have been,” says Guilherme. “But it never progressed—because Darktrace acted at exactly the right point.”

Subsequent forensic analysis confirmed the payload’s malicious intent. The attack never succeeded.

Precision over disruption

For Núclea, this incident reinforced a critical lesson: modern attacks don’t always look malicious—they hide within normal activity.

“What stands out to me is the precision,” Guilherme says. “Darktrace doesn’t rely on big, obvious signals. It’s effective in situations that fall outside the standard patterns we all know.”

Building resilience in a high trust ecosystem

For Núclea, cybersecurity is not just a defensive measure—it’s a business enabler.

Availability failures or successful breaches in the financial ecosystem can have immediate, large-scale consequences, from financial loss to reputational damage. Preventing those outcomes protects not just Núclea, but its partners and customers as well.

“Cyber resilience means keeping the business running—even under attack,” Guilherme explains. “And that requires people, processes, and technology working together.”

As AI continues to accelerate both attacks and defenses, the role of security is evolving. Precision, behavioral understanding, and intelligent automation are no longer optional—they’re essential.

“The easy days were yesterday,” Guilherme says. “The challenges ahead are bigger. We need to be prepared—internally and with partners that help us build resilience.”

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June 1, 2026

効率化の裏にあるリスク:AI導入が製造現場にもたらす見えない脆弱性

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AIエージェントが製造業に与える影響

製造業界のセキュリティチームやIT担当者は、生産を守り、稼働時間を維持し、重要資産を保護するという絶え間ないプレッシャー下にあります。そしてAIは非常に大きなチャンスとともに、新たなサイバーリスクももたらしています。製造業全体で、AIはワークフローや意思決定に組み込まれつつあり、自律型AIエージェントが従業員やシステムに代わって行動する場面が増えています。

エージェント型システムは独立して行動できるため強力ですが、その同じ自律性がサイバーリスク、運用上のリスクも生み出します。エージェントは広範な権限を持ち、複雑なタスクの実行、意思決定、ツールや外部システムとのやり取りを、ほとんどまたは全く人間の介入なしに行うことができます。

あらかじめ定義されたタスクを実行する従来のAIモデルとは異なり、AIエージェントは高度なテクニックを使用して人間の意思決定プロセスを模倣することにより、新たな課題に動的に適応し、また自らの判断に基づいて意思決定し、アクションを実行します。彼らは業務の上では従業員のように見えますが、人間が持つ判断力、倫理観、または行動の結果に対する恐れが欠けています。これは、サイバー犯罪者によって簡単に操られる可能性があることを意味しており、OTネットワーク全体に埋め込まれたAIエージェントは、データ漏洩をはるかに超える脅威を生み出します。たとえば、BMWでは、AI は溶接プロセスのエラーの発生を識別するのに使われています。同社のスパータンバーグ(米サウスカロライナ州)の工場では、すべてのSUVフレーム上の300-400個のスタッドの溶接をAIが監視し、スタッドの配置間違いや欠陥を検知し直ちに修正します。このAIシステムが破損すれば壊滅的な品質管理問題につながる恐れがあります。

製造全体にエージェント型AIシステムを導入することについて多くのセキュリティチームはさまざまな懸念を示しています。ダークトレースの行ったAIサイバーセキュリティの現状調査では、製造業のセキュリティプロフェッショナルの78%が従業員によるAIエージェントの利用に懸念を抱いており、これは彼らの最も大きな危惧でした。それに続く問題点が従業員によるCopilotやChatGPT等の生成AIツールの使用であり、製造業のセキュリティプロフェッショナルの76%が懸念を抱いていました。これらのツールがますます多くのビジネスデータやプロセスにアクセスし、組織内でより多くの自律性を持つようになるにつれ、エージェントのアクティビティがほとんど可視化されていない現在、セキュリティチームにおいては機密データの露出(60%)や偶発的なポリシーおよび規制違反(59%)への懸念が高まっています。

外部からのAIによる脅威も急激に進化

製造業を変革しているのと同じAIの能力が、サイバー攻撃の形も変貌させています。

AIにより攻撃者は偵察を自動化し、標的をより高度に絞り込み、リアルタイムで適応できるようになっています。かつては人手による作業と時間を要していたことが、今では継続的かつ大規模に実行できるようになりました。そして、製造業はすでにその影響を実感しています。当社が調査した製造業のセキュリティプロフェッショナルの76%は、すでにAIを活用した脅威の影響を受けており、90%がAIによってソーシャルエンジニアリング攻撃の成功率が高まっていると回答しています。

また、攻撃のテクニック自体も進化しています。製造業界全体で、AIを利用した攻撃の経路の多様化に対する懸念が高まっています。特にリアルタイムで進化する適応型マルウェアについて、調査対象の製造業のセキュリティプロフェッショナルの半数近く(49%)が懸念しており、これは全産業の平均よりも9%高い数値です。AIを使った適応型マルウェアに続くその他の懸念には次が含まれます:

  • 自動化された脆弱性スキャンとエクスプロイトチェイニング(48%):Anthropicの新しいMythos AIモデルにより脆弱性探索が深刻化する中で、この問題は一層差し迫ったものとなっています。
  • 超パーソナライズされたフィッシングキャンペーン(46%):フィッシングは依然としてハッカーの主力兵器の1つであり、AIによってフィッシングメールはより説得力が高く検知困難なものとなり、その効果は増幅されました。

これは単に攻撃の量の増加だけでなく、攻撃の展開につれて静的な防御が対応できるよりも速く進化する脅威への変化なのです。

こうした認識が高まっているにもかかわらず、製造業の多くはまだこの変化に対応する準備ができていません。半数以上(51%)がAI駆動の脅威への準備が十分にできていないと回答し、AIの導入を管理する正式なポリシーを持っている組織はわずか37%でした。  

可視性、コンテキスト、およびガードレールを通じてAIのセキュリティを確保

これらの問題に対処するためにAIイノベーションを遅らせる必要はありません。それには、AIと同じスピードと規模で動作できる、これまでとは異なるアプローチのセキュリティが必要です。具体的には、製造業がAIの力を活用する上で、次の3つの優先課題が浮上しています。

可視性はすべての土台  

AIがどこで使用されているか、何にアクセスできるか、そしてITおよびOT環境にわたってどのように動作するかを理解する必要があります。それがなければ、リスクを測定したり管理したりすることはできません。ダークトレースの調査において、製造業のセキュリティプロフェッショナルの91%が、AIを信頼する前に、それがどのように意思決定を行うかを理解する必要があると回答したのは当然のことです。OT環境においてこのことはさらに重要です。稼働の中断は安全や環境、財務、および評判に大きな影響を及ぼすからです。

可視性をアクションにつなげるにはコンテキストが必要  

AIによって形作られる環境において、正常とされる挙動は絶えず変化します。つまり、脅威を検知するにはビヘイビアベースのアプローチが必要なのです。組織全体で生活パターンを理解し、わずかな逸脱をリアルタイムに検知すること- これは従来のセキュリティとリスク管理に対するアプローチからの根本的な変化です。

エージェントからの露出を防ぐガードレール  

AIシステムがより大きな責任を担うようになるなかで、組織はAIが何をできるか、そしていつ独立して行動できるかについて、明確な境界を設ける必要があります。これらのコントロールは何かがあってから適用されるのではなく、システム自体に組み込んでおかなければなりません。  

製造業のITおよびOT環境におけるAIエージェントのセキュリティ

エージェント型AIの出現は製造業を変革し、次世代のオペレーションを支える一方で、脅威ランドスケープも一変させています。これは単なる脅威の増加ではなく、自律型システムへの移行、挙動の絶え間ない変化、そしてマシンスピードで進行するリスクです。AIを活用しつつリスクを管理するという課題に取り組む組織にとって、可視性、コンテキスト、ガードレールはセキュリティの基盤となります。

Darktraceはこの基盤を実現することにより、製造業の安全なAIアプローチ構築を支援します。ITおよびOT環境全体を可視化し、異常なアクティビティに対するリアルタイムの検知および対応を提供することにより、従業員が使用するプロンプトや構築するエージェントから、それらのエージェントの環境全体での動作に至るまで、AIアクティビティの理解を可能にします。これにより、AIの導入を拡大する製造業はコントロールを犠牲にすることなくイノベーションの基盤を構築することができます。

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About the author
Dr. Oakley Cox-Robinson
Senior Director of Product
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