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November 29, 2020

Darktrace Cyber Analyst Investigates Sodinokibi Ransomware

Darktrace’s Cyber AI Analyst uncovers the intricate details of a Sodinokibi ransomware attack on a retail organization. Dive into this real-time incident.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Max Heinemeyer
Global Field CISO
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29
Nov 2020

Sodinokibi is one of the most lucrative ransomware strains of 2020, with its creators, cyber-criminal gang REvil, recently claiming over $100 million in profits this year alone. The prevalent threat is known to wipe backup files, encrypt files on local shares and exfiltrate data.

Exfiltration before encryption is a technique being increasingly adopted by profit-seeking cyber-criminals, who can threaten to leak the stolen data should a target organization not comply with their demands. Sodinobiki also makes heavy use of code obfuscation and encryption techniques to evade detection by signature-based, anti-virus solutions.

Darktrace’s AI recently detected Sodinokibi targeting a retail organization in the US. Prior to this year, the company operated primarily face-to-face in physical stores, but have conducted the majority of their business in the digital realm since the onset of the pandemic.

Cyber AI Analyst automatically launched a full investigation into this incident in real time, as the attack was unfolding. The technology provided summary reports of the entire incident which the security team could immediately action for incident response. This blog explores its findings.

Sodinokibi timeline

Darktrace automatically investigated on the full scope of the Sodinokibi attack, with Cyber AI Analyst clearly identifying and summarising every stage of the attack lifecycle, which played out over the course of three weeks as below:

Figure 1: A timeline of the attack

Darktrace produced a large number of security-relevant anomalies associated with just three credentials, and displayed these along a common timeline shown below:

Figure 2: A timeline view of anomaly detections separated by users. Note the clusters of model breaches for the compromised credentials leading up to October 14.

While a human analyst might have been able to identify these unusual patterns and investigate what caused the clusters of anomalous activity, this process would have taken precious hours during a crisis. Cyber AI Analyst automatically performed the same analysis using supervised machine learning trained on Darktrace’s world-leading analysts, generating meaningful summaries of each stage of the event in real time, as the incident unfolded.

REvil ransomware attack

The following events occurred during a free trial period, and Darktrace was not being actively monitored. Its Autonomous Response technology, Darktrace Antigena, was installed in passive mode, and in the absence of automatic interference at an early stage, this compromise was allowed to unfold without interruption. However, with Darktrace’s AI learning normal ‘patterns of life’ for every device in the background, identifying anomalies, and launching an automated investigation into the attack, we are able to go back into the Threat Visualizer and see how the incident unfolded.

The attack began when the credentials of a highly privileged member of the retail organization’s IT team were compromised. REvil is known to make use of phishing emails, exploit kits, server vulnerabilities, and compromised MSP networks for initial intrusion.

In this case, the attacker used the IT credential to compromise a domain controller and exfiltrate data directly after initial reconnaissance. Darktrace’s AI detected the attacker logging into the domain controller via SMB, writing suspicious files and then deleting batch scripts and log files in the root directory to clear their tracks.

The domain controller then made connections to several rare external endpoints, and Darktrace witnessed a 28MB upload that was likely exfiltration of initial reconnaissance data. Four days later, the attacker connected to the same endpoint (sadstat[.]com) – likely a stager download for C2, which was then initiated via connections on port 443 later that same day.

A week on from the intial C2 connection, a SQL server was detected engaging in network scanning as the attacker sought to move laterally in search of sensitive and valuable data. Over the course of two weeks, Darktrace witnessed unusual internal RDP connections using administrative credentials, before data was uploaded to multiple cloud storage endpoints as well as an SSH server. PsExec was used to deploy the ransomware, resulting in file encryption.

The evasive nature of modern ransomware

REvil started with an inherent advantage in that they were armed with the credentials of a highly privileged IT admin. Nevertheless, they still made several attempts to evade traditional, signature-based tools, such as ‘Living off the Land’ – using common tools such PsExec, WMI, RDP to blend into to legitimate activity.

They leveraged frequently-used cloud storage solutions like Dropbox and pCloud for data transfer, and they conducted SSH on port 443, blending in with SSL connections on the same port. They used a newly-registered domain for C2 communication, meaning Open Source Intelligence Tools (OSINT) were blind to the threat.

Finally, the malware itself was evasive in that it made use of code obfuscation and encryption, and had no need for a system library or API imports. This is the basis for most modern ransomware attacks, and the reality is signature-based tools cannot keep up. Darktrace’s AI not only detected the anomalous activity associated with every stage of the attack, but generated fleshed-out summaries of each stage of the attack with Cyber AI Analyst.

Cyber AI Analyst: Real-time incident reporting

Between September 21 and October 12, Cyber AI Analyst created 15 incidents, investigating dozens of point detections and creating a coherent attack narrative.

Figure 3: Cyber AI Incident log of the first compromised DC. This incident tab details the connections to sadstat[.]com

Figure 4: The DC establishes C2 to the first GHOSTnet GmbH IP

Figure 5: This incident tab highlights the file encryption of files on network shares

Figure 6: Darktrace surfaces the IT admin account takeover

Figure 7: Example of a client type device involved in extensive administrative RDP and SMB activity, as well as data uploads to Dropbox (this upload to Dropbox occurs few seconds before file encryption begins)

REvil vs AI

This Sodinokibi ransomware attack slipped under the radar of a range of traditional tools deployed by the retail organization. However, despite the threat dwelling in the retail organization’s digital environment for over a month, and REvil using local tools to blend in to regular traffic, from Darktrace’s perspective these actions were noisy in comparison to the organization’s normal ‘pattern of life’, setting off a series of alerts and investigations.

Darktrace’s Cyber AI Analyst was able to autonomously investigate nearly every attack phase of the ransomware. The technology works around the clock, without requiring training or time off, and can often reduce hours or days of incident response into just minutes, reducing time to triage by up to 92% and augmenting the capabilities of the human security team.

Thanks to Darktrace analyst Joel Lee for his insights on the above threat find.

Learn more about Cyber AI Analyst

Darktrace model detections:

  • Anomalous Connection / Active Remote Desktop Tunnel
  • Anomalous Connection / Data Sent To New External Device
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous Connection / High Volume of New or Uncommon Service Control
  • Anomalous Connection / SMB Enumeration
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Unusual Admin RDP Session
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device
  • Compliance / SMB Drive Write
  • Compliance / Possible Tor Usage
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Compromise / Ransomware / Suspicious SMB Activity
  • Device / ICMP Address Scan
  • Device / Multiple Lateral Movement Model Breaches
  • Device / Network Scan
  • Device / New or Uncommon WMI Activity
  • Device / New or Unusual Remote Command Execution
  • Device / RDP Scan
  • Device / Suspicious Network Scan Activity
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Unusual Activity / Unusual Internal Connections
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Max Heinemeyer
Global Field CISO

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January 14, 2026

React2Shell Reflections: Cloud Insights, Finance Sector Impacts, and How Threat Actors Moved So Quickly

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Introduction

Last month’s disclosure of CVE 2025-55812, known as React2Shell, provided a reminder of how quickly modern threat actors can operationalize newly disclosed vulnerabilities, particularly in cloud-hosted environments.

The vulnerability was discovered on December 3, 2025, with a patch made available on the same day. Within 30 hours of the patch, a publicly available proof-of-concept emerged that could be used to exploit any vulnerable server. This short timeline meant many systems remained unpatched when attackers began actively exploiting the vulnerability.  

Darktrace researchers rapidly deployed a new honeypot to monitor exploitation of CVE 2025-55812 in the wild.

Within two minutes of deployment, Darktrace observed opportunistic attackers exploiting this unauthenticated remote code execution flaw in React Server Components, leveraging a single crafted request to gain control of exposed Next.js servers. Exploitation quickly progressed from reconnaissance to scripted payload delivery, HTTP beaconing, and cryptomining, underscoring how automation and pre‑positioned infrastructure by threat actors now compress the window between disclosure and active exploitation to mere hours.

For cloud‑native organizations, particularly those in the financial sector, where Darktrace observed the greatest impact, React2Shell highlights the growing disconnect between patch availability and attacker timelines, increasing the likelihood that even short delays in remediation can result in real‑world compromise.

Cloud insights

In contrast to traditional enterprise networks built around layered controls, cloud architectures are often intentionally internet-accessible by default. When vulnerabilities emerge in common application frameworks such as React and Next.js, attackers face minimal friction.  No phishing campaign, no credential theft, and no lateral movement are required; only an exposed service and exploitable condition.

The activity Darktrace observed during the React2shell intrusions reflects techniques that are familiar yet highly effective in cloud-based attacks. Attackers quickly pivot from an exposed internet-facing application to abusing the underlying cloud infrastructure, using automated exploitation to deploy secondary payloads at scale and ultimately act on their objectives, whether monetizing access through cryptomining or to burying themselves deeper in the environment for sustained persistence.

Cloud Case Study

In one incident, opportunistic attackers rapidly exploited an internet-facing Azure virtual machine (VM) running a Next.js application, abusing the React/next.js vulnerability to gain remote command execution within hours of the service becoming exposed. The compromise resulted in the staged deployment of a Go-based remote access trojan (RAT), followed by a series of cryptomining payloads such as XMrig.

Initial Access

Initial access appears to have originated from abused virtual private network (VPN) infrastructure, with the source IP (146.70.192[.]180) later identified as being associated with Surfshark

The IP address above is associated with VPN abuse leveraged for initial exploitation via Surfshark infrastructure.
Figure 1: The IP address above is associated with VPN abuse leveraged for initial exploitation via Surfshark infrastructure.

The use of commercial VPN exit nodes reflects a wider trend of opportunistic attackers leveraging low‑cost infrastructure to gain rapid, anonymous access.

Parent process telemetry later confirmed execution originated from the Next.js server, strongly indicating application-layer compromise rather than SSH brute force, misused credentials, or management-plane abuse.

Payload execution

Shortly after successful exploitation, Darktrace identified a suspicious file and subsequent execution. One of the first payloads retrieved was a binary masquerading as “vim”, a naming convention commonly used to evade casual inspection in Linux environments. This directly ties the payload execution to the compromised Next.js application process, reinforcing the hypothesis of exploit-driven access.

Command-and-Control (C2)

Network flow logs revealed outbound connections back to the same external IP involved in the inbound activity. From a defensive perspective, this pattern is significant as web servers typically receive inbound requests, and any persistent outbound callbacks — especially to the same IP — indicate likely post-exploitation control. In this case, a C2 detection model alert was raised approximately 90 minutes after the first indicators, reflecting the time required for sufficient behavioral evidence to confirm beaconing rather than benign application traffic.

Cryptominers deployment and re-exploitation

Following successful command execution within the compromised Next.js workload, the attackers rapidly transitioned to monetization by deploying cryptomining payloads. Microsoft Defender observed a shell command designed to fetch and execute a binary named “x” via either curl or wget, ensuring successful delivery regardless of which tooling was availability on the Azure VM.

The binary was written to /home/wasiluser/dashboard/x and subsequently executed, with open-source intelligence (OSINT) enrichment strongly suggesting it was a cryptominer consistent with XMRig‑style tooling. Later the same day, additional activity revealed the host downloading a static XMRig binary directly from GitHub and placing it in a hidden cache directory (/home/wasiluser/.cache/.sys/).

The use of trusted infrastructure and legitimate open‑source tooling indicates an opportunistic approach focused on reliability and speed. The repeated deployment of cryptominers strongly suggests re‑exploitation of the same vulnerable web application rather than reliance on traditional persistence mechanisms. This behavior is characteristic of cloud‑focused attacks, where publicly exposed workloads can be repeatedly compromised at scale more easily.

Financial sector spotlight

During the mass exploitation of React2Shell, Darktrace observed targeting by likely North Korean affiliated actors focused on financial organizations in the United Kingdom, Sweden, Spain, Portugal, Nigeria, Kenya, Qatar, and Chile.

The targeting of the financial sector is not unexpected, but the emergence of new Democratic People’s Republic of Korea (DPRK) tooling, including a Beavertail variant and EtherRat, a previously undocumented Linux implant, highlights the need for updated rules and signatures for organizations that rely on them.

EtherRAT uses Ethereum smart contracts for C2 resolution, polling every 500 milliseconds and employing five persistence mechanisms. It downloads its own Node.js runtime from nodejs[.]org and queries nine Ethereum RPC endpoints in parallel, selecting the majority response to determine its C2 URL. EtherRAT also overlaps with the Contagious Interview campaign, which has targeted blockchain developers since early 2025.

Read more finance‑sector insights in Darktrace’s white paper, The State of Cyber Security in the Finance Sector.

Threat actor behavior and speed

Darktrace’s honeypot was exploited just two minutes after coming online, demonstrating how automated scanning, pre-positioned infrastructure and staging, and C2 infrastructure traced back to “bulletproof” hosting reflects a mature, well‑resourced operational chain.

For financial organizations, particularly those operating cloud‑native platforms, digital asset services, or internet‑facing APIs, this activity demonstrates how rapidly geopolitical threat actors can weaponize newly disclosed vulnerabilities, turning short patching delays into strategic opportunities for long‑term access and financial gain. This underscores the need for a behavioral-anomaly-led security posture.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Indicators of Compromise (IoCs)

146.70.192[.]180 – IP Address – Endpoint Associated with Surfshark

References

https://www.darktrace.com/resources/the-state-of-cybersecurity-in-the-finance-sector

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

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January 13, 2026

Runtime Is Where Cloud Security Really Counts: The Importance of Detection, Forensics and Real-Time Architecture Awareness

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Introduction: Shifting focus from prevention to runtime

Cloud security has spent the last decade focused on prevention; tightening configurations, scanning for vulnerabilities, and enforcing best practices through Cloud Native Application Protection Platforms (CNAPP). These capabilities remain essential, but they are not where cloud attacks happen.

Attacks happen at runtime: the dynamic, ephemeral, constantly changing execution layer where applications run, permissions are granted, identities act, and workloads communicate. This is also the layer where defenders traditionally have the least visibility and the least time to respond.

Today’s threat landscape demands a fundamental shift. Reducing cloud risk now requires moving beyond static posture and CNAPP only approaches and embracing realtime behavioral detection across workloads and identities, paired with the ability to automatically preserve forensic evidence. Defenders need a continuous, real-time understanding of what “normal” looks like in their cloud environments, and AI capable of processing massive data streams to surface deviations that signal emerging attacker behavior.

Runtime: The layer where attacks happen

Runtime is the cloud in motion — containers starting and stopping, serverless functions being called, IAM roles being assumed, workloads auto scaling, and data flowing across hundreds of services. It’s also where attackers:

  • Weaponize stolen credentials
  • Escalate privileges
  • Pivot programmatically
  • Deploy malicious compute
  • Manipulate or exfiltrate data

The challenge is complex: runtime evidence is ephemeral. Containers vanish; critical process data disappears in seconds. By the time a human analyst begins investigating, the detail required to understand and respond to the alert, often is already gone. This volatility makes runtime the hardest layer to monitor, and the most important one to secure.

What Darktrace / CLOUD Brings to Runtime Defence

Darktrace / CLOUD is purpose-built for the cloud execution layer. It unifies the capabilities required to detect, contain, and understand attacks as they unfold, not hours or days later. Four elements define its value:

1. Behavioral, real-time detection

The platform learns normal activity across cloud services, identities, workloads, and data flows, then surfaces anomalies that signify real attacker behavior, even when no signature exists.

2. Automated forensic level artifact collection

The moment Darktrace detects a threat, it can automatically capture volatile forensic evidence; disk state, memory, logs, and process context, including from ephemeral resources. This preserves the truth of what happened before workloads terminate and evidence disappears.

3. AI-led investigation

Cyber AI Analyst assembles cloud behaviors into a coherent incident story, correlating identity activity, network flows, and Cloud workload behavior. Analysts no longer need to pivot across dashboards or reconstruct timelines manually.

4. Live architectural awareness

Darktrace continuously maps your cloud environment as it operates; including services, identities, connectivity, and data pathways. This real-time visibility makes anomalies clearer and investigations dramatically faster.

Together, these capabilities form a runtime-first security model.

Why CNAPP alone isn’t enough

CNAPP platforms excel at pre deployment checks all the way down to developer workstations, identifying misconfigurations, concerning permission combinations, vulnerable images, and risky infrastructure choices. But CNAPP’s breadth is also its limitation. CNAPP is about posture. Runtime defense is about behavior.

CNAPP tells you what could go wrong; runtime detection highlights what is going wrong right now.

It cannot preserve ephemeral evidence, correlate active behaviors across domains, or contain unfolding attacks with the precision and speed required during a real incident. Prevention remains essential, but prevention alone cannot stop an attacker who is already operating inside your cloud environment.

Real-world AWS Scenario: Why Runtime Monitoring Wins

A recent incident detected by Darktrace / CLOUD highlights how cloud compromises unfold, and why runtime visibility is non-negotiable. Each step below reflects detections that occur only when monitoring behavior in real time.

1. External Credential Use

Detection: Unusual external source for credential use: An attacker logs into a cloud account from a never-before-seen location, the earliest sign of account takeover.

2. AWS CLI Pivot

Detection: Unusual CLI activity: The attacker switches to programmatic access, issuing commands from a suspicious host to gain automation and stealth.

3. Credential Manipulation

Detection: Rare password reset: They reset or assign new passwords to establish persistence and bypass existing security controls.

4. Cloud Reconnaissance

Detection: Burst of resource discovery: The attacker enumerates buckets, roles, and services to map high value assets and plan next steps.

5. Privilege Escalation

Detection: Anomalous IAM update: Unauthorized policy updates or role changes grant the attacker elevated access or a backdoor.

6. Malicious Compute Deployment

Detection: Unusual EC2/Lambda/ECS creation: The attacker deploys compute resources for mining, lateral movement, or staging further tools.

7. Data Access or Tampering

Detection: Unusual S3 modifications: They alter S3 permissions or objects, often a prelude to data exfiltration or corruption.

Only some of these actions would appear in a posture scan, crucially after the fact.
Every one of these runtime detections is visible only through real-time behavioral monitoring while the attack is in progress.

The future of cloud security Is runtime-first

Cloud defense can no longer revolve solely around prevention. Modern attacks unfold in runtime, across a fast-changing mesh of workloads, services, and — critically — identities. To reduce risk, organizations must be able to detect, understand, and contain malicious activity as it happens, before ephemeral evidence disappears and before attacker's pivot across identity layers.

Darktrace / CLOUD delivers this shift by turning runtime, the most volatile and consequential layer in the cloud, into a fully defensible control point through unified visibility across behavior, workloads, and identities. It does this by providing:

  • Real-time behavior detection across workloads and identity activity
  • Autonomous response actions for rapid containment
  • Automated forensic level artifact preservation the moment events occur
  • AI-driven investigation that separates weak signals from true attacker patterns
  • Live cloud environment insight to understand context and impact instantly

Cloud security must evolve from securing what might go wrong to continuously understanding what is happening; in runtime, across identities, and at the speed attackers operate. Unifying runtime and identity visibility is how defenders regain the advantage.

[related-resource]

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About the author
Adam Stevens
Senior Director of Product, Cloud | Darktrace
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