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February 20, 2020

Lessons Learned from a Sodinokibi Ransomware Attack

Gain insights into a targeted Sodinokibi ransomware attack and learn how to better prepare your organization for potential cyber threats.
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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20
Feb 2020

Introduction

Last week, Darktrace detected a targeted Sodinokibi ransomware attack during a 4-week trial with a mid-sized company.

This blog post will go through every stage of the attack lifecycle and detail the attacker’s techniques, tools and procedures used, and how Darktrace detected the attack.

The Sodinokibi group is an innovative threat-actor that is sometimes referred to as a ‘double-threat’, due to their ability to run targeted attacks using ransomware while simultaneously exfiltrating their victim’s data. This enables them to threaten to make the victim’s data publicly available if the ransom is not paid.

While Darktrace’s AI was able to identify the attack in real time as it was emerging, unfortunately the security team didn’t have eyes on the technology and was unable to action the alerts — nor was Antigena set in active mode, which would have slowed down and contained the threat instantaneously.

Timeline

The timeline below provides a rough overview of the major attack phases. Most of the attack took place over the course of a week, with the majority of activity distributed over the last three days.

Technical analysis

Darktrace detected two main devices being hit by the attack: an internet-facing RDP server (‘RDP server’) and a Domain Controller (‘DC’), that also acts as a SMB file server.

In previous attacks, Sodinokibi has used host-level encryption for ransomware activity where the encryption takes place on the compromised host itself — in contrast to network-level encryption where the bulk of the ransomware activity takes place over network protocols such as SMB.

Initial compromise

Over several days, the victim’s external-facing RDP server was receiving successful RDP connections from a rare external IP address located in Ukraine.

Shortly before the initial reconnaissance started, Darktrace saw another RDP connection coming into the RDP server with the same RDP account as seen before. This connection lasted for almost an hour.

It is highly likely that the RDP credential used in this attack had been compromised prior to the attack, either via common brute-force methods, credential stuffing attacks, or phishing.

Thanks to Darktrace’s Deep-Packet Inspection, we can clearly see the connection and all related information.

Suspicious RDP connection information:

Time: 2020-02-10 16:57:06 UTC
Source: 46.150.70[.]86 (Ukraine)
Destination: 192.168.X.X
Destination Port: 64347
Protocol: RDP
Cookie: [REDACTED]
Duration: 00h41m40s
Data out: 8.44 MB
Data in: 1.86 MB

Darktrace detects incoming RDP connections from IP addresses that usually do not connect to the organization.

Attack tools download

Approximately 45 minutes after the suspicious RDP connection from Ukraine, the RDP server connected to the popular file sharing platform, Megaupload, and downloaded close to 300MB from there.

Darktrace’s AI recognized that neither this server, nor its automatically detected peer group, nor, in fact, anyone else on the network commonly utilized Megaupload — and therefore instantly detected this as anomalous behavior, and flagged it as unusual.

As well as the full hostname and actual IP used for the download, Megaupload is 100% rare for this organization.

Later on, we will see over 40GB being uploaded to Megaupload. This initial download of 300MB however is likely additional tooling and C2 implants downloaded by the threat-actor into the victim’s environment.

Internal reconnaissance

Only 3 minutes after the download from Megaupload onto the RDP server, Darktrace alerted on the RDP server doing an anomalous network scan:

The RDP server scanned 9 other internal devices on the same subnet on 7 unique ports: 21, 80, 139, 445, 3389, 4899, 8080
 . Anybody with some offensive security know-how will recognize most of these ports as default ports one would scan for in a Windows environment for lateral movement. Since this RDP server does not usually conduct network scans, Darktrace again identified this activity as highly anomalous.

Later on, we see the threat-actor do more network scanning. They become bolder and use more generic scans — one of them showing that they are using Nmap with a default user agent:

Additional Command and Control traffic

While the initial Command and Control traffic was most likely using predominantly RDP, the threat-actor now wanted to establish more persistence and create more resilient channels for C2.

Shortly after concluding the initial network scans (ca. 19:17 on 10th February 2020), the RDP server starts communicating with unusual external services that are unique and unusual for the victim’s environment.

Communications to Reddcoin

Again, nobody else is using Reddcoin on the network. The combination of application protocol and external port is extremely unusual for the network as well.

The communications also went to the Reddcoin API, indicating the installation of a software agent rather than manual communications. This was detected as Reddcoin was not only rare for the network, but also ‘young’ — i.e. this particular external destination had never been seen to be contacted before on the network until 25 minutes before.

Communications to the Reddcoin API

Communications to Exceptionless[.]io

As we can see, the communications to exceptionalness[.]io were done in a beaconing manner, using a Let’s Encrypt certificate, being rare for the network and using an unusual JA3 client hash. All of this indicates the presence of new software on the device, shortly after the threat-actor downloaded their 300MB of tooling.

While most of the above network activity started directly after the threat-actor dropped their tooling on the RDP server, the exact purpose of interfacing with Reddcoin and Exceptionless is unclear. The attacker seems to favor off-the-shelf tooling (Megaupload, Nmap, …) so they might use these services for C2 or telemetry-gathering purposes.

This concluded most of the activity on February 10.

More Command and Control traffic

Why would an attacker do this? Surely using all this C2 at the same time is much noisier than just using 1 or 2 channels?

Another significant burst of activity was observed on February 12 and 13.

The RDP server started making a lot of highly anomalous and rare connections to external destinations. It is inconclusive if all of the below services, IPs, and domains were used for C2 purposes only, but they are linked with high-confidence to the attacker’s activities:

  • HTTP beaconing to vkmuz[.]net
  • Significant amount of Tor usage
  • RDP connections to 198-0-244-153-static.hfc.comcastbusiness[.]net over non-standard RDP port 29348
  • RDP connections to 92.119.160[.]60 using an administrative account (geo-located in Russia)
  • Continued connections to Megaupload
  • Continued SSL beaconing to Exceptionless[.]io
  • Continued connections to api.reddcoin[.]com
  • SSL beaconing to freevpn[.]zone
  • HTTP beaconing to 31.41.116[.]201 to /index.php using a new User Agent
  • Unusual SSL connections to aj1713[.]online
  • Connections to Pastebin
  • SSL beaconing to www.itjx3no[.]com using an unusual JA3 client hash
  • SSL beaconing to safe-proxy[.]com
  • SSL connection to westchange[.]top without prior DNS hostname lookups (likely machine-driven)

What is significant here is the diversity in (potential) C2 channels: Tor, RDP going to dynamic ISP addresses, VPN solutions and possibly custom / customized off-the-shelf implants (the DGA-looking domains and HTTP to IP addresses to /index.php).

Why would an attacker do this? Surely using all this C2 at the same time is much noisier than just using 1 or 2 channels?

One answer might be that the attacker cared much more about short-term resilience than about stealth. As the overall attack in the network took less than 7 days, with a majority of the activity taking place over 2.5 days, this makes sense. Another possibility might be that various individuals were involved in parallel during this attack — maybe one attacker prefers the comfort of RDP sessions for hacking while another is more skilled and uses a particular post-exploitation framework.

The overall modus operandi in this financially-motivated attack is much more smash-and-grab than in the stealthy, espionage-related incidents observed in Advanced Persistent Threat campaigns (APT).

Data exfiltration

The DC uploaded around 40GB of data to Megaupload over the course of 24 hours.

While all of the above activity was seen on the RDP server (acting as the initial beach-head), the following data exfiltration activity was observed on a Domain Controller (DC) on the same subnet as the RDP server.

The DC uploaded around 40GB of data to Megaupload over the course of 24 hours.

Darktrace detected this data exfiltration while it was in progress — never did the DC (or any similar devices) upload similar amounts of data to the internet. Neither did any client nor server in the victim’s environment use Megaupload:

Ransom notes

Finally, Darktrace observed unusual files being accessed on internal SMB shares on February 13. These files appear to be ransom notes — they follow a similar, randomly-generated naming convention as other victims of the Sodinokibi group have reported:

413x0h8l-readme.txt
4omxa93-readme.txt

Conclusion and observations

The threat-actor seems to be using mostly off-the-shelf tooling which makes attribution harder — while also making detection more difficult.

This attack is representative of many of the current ransomware attacks: financially motivated, fast-acting, and targeted.

The threat-actor seems to be using mostly off-the-shelf tooling (RDP, Nmap, Mega, VPN solutions) which makes attribution harder — while also making detection more difficult. Using this kind of tooling often allows to blend in with regular admin activity — only once anomaly detection is used can this kind of activity be detected.

How can you spot the one anomalous outbound RDP connection amongst the thousands of regular RDP connections leaving your environment? How do you know when the use of Megaupload is malicious — compared to your users’ normal use of it? This is where the power of Darktrace’s self-learning AI comes into play.

Darktrace detected every stage of the visible attack lifecycle without using any threat intelligence or any static signatures.

The graphics below show an overview of detections on both compromised devices. The compromised devices were the highest-scoring assets for the network — even a level 1 analyst with limited previous exposure to Darktrace could detect such an in-progress attack in real time.

RDP Server

Some of the detections on the RDP server include:

  • Compliance / File Storage / Mega — using Megaupload in an unusual way
  • Device / Network Scan — detecting unusual network scans
  • Anomalous Connection / Application Protocol on Uncommon Port — detecting the use of protocols on unusual ports
  • Device / New Failed External Connections — detecting unusual failing C2
  • Compromise / Unusual Connections to Let’s Encrypt — detecting potential C2 over SSL using Let’s Encrypt
  • Compromise / Beacon to Young Endpoint — detecting C2 to new external endpoints for the network
  • Device / Attack and Recon Tools — detecting known offensive security tools like Nmap
  • Compromise / Tor Usage — detecting unusual Tor usage
  • Compromise / SSL Beaconing to Rare Destination — detecting generic SSL C2
  • Compromise / HTTP Beaconing to Rare Destination — detecting generic HTTP C2
  • Device / Long Agent Connection to New Endpoint — detecting unusual services on a device
  • Anomalous Connection / Outbound RDP to Unusual Port — detecting unusual RDP C2

DC

Some of the detections on the DC include:

  • Anomalous Activity / Anomalous External Activity from Critical Device — detecting unusual behaviour on dcs
  • Compliance / File storage / Mega — using Megaupload in an unusual way
  • Anomalous Connection / Data Sent to New External Device — data exfiltration to unusual locations
  • Anomalous Connection / Uncommon 1GB Outbound — large amounts of data leaving to unusual destinations
  • Anomalous Server Activity / Outgoing from Server — likely C2 to unusual endpoint on the internet


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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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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Jamie Bali
Technical Author (AI) Developer

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May 21, 2026

Data Center Security: Improving Visibility and Threat Detection Across IT, OT, and IoT

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What is data center cybersecurity?

Much of the conversation surrounding the data center boom has focused on power generation, cooling efficiency and water resources, construction, and compute capacity. In addition, cybersecurity has quietly become one of the most critical operational concerns as modern data centers are becoming some of the most operationally complex networked environments.

The more connected data center environments become, the larger and more dynamic their attack surface grows. What makes data center security particularly challenging is that they no longer resemble traditional enterprise IT environments alone. Instead, they operate like critical infrastructure facilities

Challenges of securing data centers

What makes these environments complicated is that the technologies responsible for keeping them operational: power distribution, cooling systems, airflow management, environmental controls, surveillance, and physical access management, all rely heavily on Operational Technology (OT), Industrial IoT (IIoT), and IoT systems alongside traditional IT infrastructure.

Programmable logic controllers (PLCs), building management systems (BMS), energy management systems (EMS), surveillance cameras, access control platforms, virtualization infrastructure, engineering workstations, contractor laptops, and cloud-connected orchestration systems now coexist within the same environment. Many are connected through routable networks, managed remotely, and accessed by 3rd party OEMs or System Integrators.

Why modern data center infrastructure faces increasing cyber risk

The challenge is not simply that there are more devices. It is that these IT, OT and IOT systems and devices are now deeply interconnected in ways that blur the boundaries between operational and enterprise infrastructure.

OT systems responsible for cooling and power distribution communicate alongside enterprise IT infrastructure. IoT devices used for physical security sit adjacent to cloud-connected management platforms. Third-party vendors and contractors frequently require remote access to maintain operations and optimize performance. AI-driven automation platforms increasingly orchestrate workflows across multiple environments simultaneously.

Every additional connection improves efficiency and scalability, but every additional connection also creates new relationships between systems that adversaries may exploit.

How IT, OT, and IoT convergence expands the data center attack surface

Historically in critical infrastructure environments enterprise IT, and OT or industrial control systems ICS, have been often separated by a DMZ.

That separation has steadily disappeared in pursuit of efficiency and access to valuable data that lives within the OT networks such as how many widgets were produced today. This conceptually is commonly referred to as “IT OT convergence.”

Modern data centers increasingly depend on interconnected systems operating across multiple domains simultaneously and face a similar reality when it comes to IT OT convergence.  

This convergence creates efficiency and visibility benefits, but it also introduces structural security challenges that traditional approaches struggle to address.

Many of the OT systems were never originally designed with modern cybersecurity requirements in mind. OT devices often prioritize uptime and operational continuity over security controls. IoT and OT devices may have limited security hardening, are inconsistently patched, or insecure default configurations. Third-party connectivity introduces external dependencies that organizations do not fully control.

As environments converge the attack surface changes and grows, attackers may exploit weaker systems positioned adjacent to critical operations for initial access. For example, a compromised IoT device may provide access into broader infrastructure, or an exposed remote management interface may enable lateral movement into OT systems.  

For defenders, rather than forcing segmentation where it’s not possible, focus oversight and monitoring across interconnected systems and how this activity might create operational risk, gaining visibility across these systems will ensure better awareness of and protection across the cracks in your systems attackers look to exploit.

Why traditional data center security tools create visibility gaps

Many organizations still secure IT, OT, and IoT environments through separate tools, teams, and workflows. Historically, this made sense. The environments themselves were more isolated, and the operational priorities were different.

But convergence changes the nature of detection and response.

Modern attacks increasingly move across domains as lateral movement and discovery techniques are pervasive amongst all the most well-known attacks to have disrupted OT. Adversaries may gain access through phishing or credential compromise, establish persistence in IT systems, pivot into operational infrastructure, exploit unmanaged IoT devices, and move laterally across cloud-connected environments.

Viewed independently, many of these signals may appear low priority or disconnected.

An anomalous login attempt, unusual device communication, changes in network traffic patterns, or abnormal behavior from an industrial controller may not appear significant on their own. The problem emerges when these activities are part of a broader attack chain unfolding across multiple systems simultaneously.

Siloed security models struggle to correlate this activity effectively because they lack shared operational context. Security teams may see isolated indicators while missing the relationships between them.

This creates a fundamental visibility problem that has discursive effects across security teams, leading to analyst overload, tedious alert investigations, and slower response times.

The issue is not simply detecting threats faster. It is understanding how activity across IT, OT, IoT, cloud, and remote access systems relate to one another in real time before operational disruption occurs.

Security measures to safeguard modern data center infrastructure

Rule-based systems, predefined indicators, and signature-driven approaches remain useful for identifying known threats, but they are less effective at identifying subtle behavioral deviations, novel attack paths, insider activity, 3rd party supply chain exploitation or attacks that move across operational domains.  

Darktrace’s Self-Learning AI approach is designed to operate across converged IT, OT, IoT, and cloud environments. Using multiple layers of AI models, Darktrace solutions come together to achieve behavioral prediction, real-time threat detection and response, and incident investigation, all while empowering your security team with visibility and control.

Because the models are environment-specific, they can adapt across highly diverse infrastructure including operational technology, physical security systems, enterprise IT, cloud workloads, and third-party connectivity.

This enables organizations to build a more unified understanding of activity across the data center.

Unified visibility across interconnected environments

Darktrace provides visibility across IT, OT, IoT, and cloud systems through a centralized platform. Security teams and data center operators can maintain live asset inventories, monitor data flows, identify vulnerable or end-of-life systems, and better understand how interconnected infrastructure communicates across the environment.

This becomes increasingly important in environments where unmanaged devices, transient contractor systems, and third-party connectivity continuously alter operational conditions.

Threat detection, investigation, and response

Darktrace applies multiple AI models to identify anomalous activity that may indicate known threats, novel attacks, insider activity, or cross-domain compromise.

By understanding how devices and systems normally behave within the environment, Darktrace can identify subtle deviations that may otherwise remain undetected in siloed environments.

Its autonomous response capabilities can also help contain threats during their early stages before they escalate into operational disruption. Meanwhile, Cyber AI Analyst provides explainable AI-driven investigations that help security teams understand the relationships between events, systems, and users involved in potential incidents.

Proactive risk identification

As data center environments continue to evolve, organizations increasingly need to understand not only active threats, but also where structural weaknesses may exist across interconnected systems.

Through capabilities such as attack path modeling and behavioral risk analysis, Darktrace helps organizations prioritize remediation efforts and identify areas where operational exposure may increase over time.

This supports a more proactive security posture in environments where operational continuity is critical.

Securing the future of interconnected infrastructure

As data centers continue to scale in size, complexity, and operational importance, their reliance on interconnected IT, OT, IoT, cloud, and AI-driven systems will only deepen.

The challenge organizations face is no longer simply protecting individual devices or isolated environments. It is understanding how risk emerges across interconnected systems operating together and detecting threats to these systems in real time.

This is ultimately what makes modern data center security different from traditional enterprise security models. The operational dependencies are broader, the environments are more heterogeneous, and the consequences of disruption and intent of adversaries are more like those in the critical infrastructure space.

Securing these environments therefore requires more than fragmented visibility across disconnected tools. Organizations increasingly need unified approaches capable of understanding relationships across systems, detecting threats early, and responding before operational disruption spreads across critical infrastructure.

As the infrastructure powering the digital economy continues to evolve, cybersecurity resilience will become increasingly inseparable from operational resilience itself.

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Daniel Simonds
Director of Operational Technology
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