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May 19, 2021

The Dangers of Double Extortion Ransomware Attacks

Learn about the latest trend in ransomware attacks known as double extortion. Discover how Darktrace can help protect your organization from this threat.
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
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May 2021

A year and a half ago, ‘double extortion’ ransomware was being used by only one known threat actor. Now, over 16 ransomware groups actively utilize this tactic. So, what is it, and why has it become so popular?

What is double extortion ransomware?

The traditional story of ransomware was one of malicious code rapidly encrypting files with public-key RSA encryption, and then deleting those files if the victim did not pay the ransom.

However, after the infamous WannaCry and NotPetya ransomware campaigns over 2017, companies ramped up their cyber defense. More emphasis was placed on backups and restoration processes, so that even if files were destroyed, organizations had copies in place and could easily restore their data.

Yet in turn, cyber-criminals have also adapted their techniques. Now, rather than just encrypting files, double extortion ransomware exfiltrates the data first. This means that if the company refuses to pay up, information can be leaked online or sold to the highest bidder. Suddenly, all those backups and data recovery plans became worthless.

Maze ransomware and friends

In late 2019, Maze ransomware emerged as the first high-profile case of double extortion. Other strains soon followed, with the Sodinokibi attack — which crippled foreign exchange company Travelex — occurring on the final day of that year.

By mid-2020, hundreds of organizations were falling victim to double extortion attacks, various websites on the dark net were leaking company data, and the Ransomware-as-a-Service business was booming as developers sold and rented new types of malware.

Furthermore, cyber security regulations started being weaponized by cyber-criminals who could leverage the threat of having to pay a hefty compliance fine (CCPA, GDPR, NYSDFS regulations) to encourage their victims to keep quiet by offering them a ransom smaller than the penalty fee.

There were 1,200 double extortion ransomware incidents in 2020, across 63 countries, with over 60% of these aimed at the US and the UK.

Despite new legislation being written regularly to try and mitigate these attacks, they aren’t slowing down. According to a recent study by RUSI, there were 1,200 double extortion ransomware incidents in 2020 alone, across 63 different countries. 60% of these were aimed at organizations headquartered in the US, and the UK suffered the second highest number of breaches.

Last month, the cyber-criminal gang known as REvil released details about Apple’s new Macbook Pro on their site ‘Happy Blog’, threatening to release more blueprints and demanding a ransom of $50 million. And last week, Colonial Pipeline purportedly paid $5 million in bitcoin to recover from a devastating OT ransomware attack.

Anatomy of a double extortion ransomware attack

Darktrace has detected a huge upsurge in double extortion ransomware threats in the last year, most recently at an energy company based in Canada. The hackers had clearly done their homework, tailoring the attack to the company and moving quickly and stealthily once inside. Below is a timeline of this real-world incident, which was mostly carried out in the space of 24 hours.

Figure 1: A timeline of the attack

Darktrace detected every stage of the intrusion and notified the security team with high-priority alerts. If Darktrace Antigena had been active in the environment, the compromised server would have been isolated as soon as it began to behave anomalously, preventing the infection from spreading.

Encryption and exfiltration

The initial infection vector is not known, but the admin account was compromised most likely from a phishing link or a vulnerability exploit. This is indicative of a trend away from the widespread ‘spray and pray’ ransomware campaigns of the last decade, towards a more targeted approach.

Cyber AI identified an internal server engaging in unusual network scanning and attempted lateral movement using the Remote Desktop Protocol (RDP). Compromised admin credentials were used to spread rapidly from the server to another internal device, ‘serverps’.

The device ‘serverps’ initiated an outbound connection to TeamViewer, a legitimate file storage service, which was active for nearly 21 hours. This connection was used for remote control of the device and to facilitate the further stages of attack. Although TeamViewer was not in wide operation in the company’s digital environment, it was not blocked by any of the legacy defenses.

The device then connected to an internal file server and downloaded 1.95 TB of data, and uploaded the same volume of data to pcloud[.]com. This exfiltration took place during work hours to blend in with regular admin activity.

The device was also seen downloading Rclone software – an open source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud.

The compromised admin credential allowed the threat actor to move laterally during this time. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000.

As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.

AI-powered investigation

Cyber AI Analyst reported on four incidents related to the attack, highlighting the suspicious behavior to the security team and providing a report on the affected devices for immediate remediation. Such concise reporting allowed the security team to quickly identify the scope of the infection and respond accordingly.

Figure 2: Cyber AI Analyst incident tray for a week

Cyber AI Analyst investigates on demand

Following further analysis on March 13, the security team employed Cyber AI Analyst to conduct on-demand investigations into the compromised admin credential in Microsoft 365, as well as another device which was identified as a potential threat.

Cyber AI Analyst created an incident for this other device, which resulted in the identification of unusual port scanning during the time period of infection. The device was promptly removed from the network.

Figure 3: Cyber AI Analyst incident for a compromised device, detailing an unusual internal download

Double trouble

The use of legitimate tools and ‘Living off the Land’ techniques (using RDP and a compromised admin credential) allowed the threat actors to carry out the bulk of the attack in less than 24 hours. By exploiting TeamViewer as a legitimate file storage solution for the data exfiltration, as opposed to relying on a known ‘bad’ or recently registered domain, the hackers easily circumvented all the existing signature-based defenses.

If Darktrace had not detected this intrusion and immediately alerted the security team, the attack could have resulted not only in a ‘denial of business’ with employees locked out of their files, but also in sensitive data loss. The AI went a step further in saving the team vital time with automatic investigation and on-demand reporting.

There is so much more to lose from double extortion ransomware. Exfiltration provides another layer of risk, leading to compromised intellectual property, reputational damage, and compliance fines. Once a threat group has your data, they might easily ask for more payments down the line. It is important therefore to defend against these attacks before they happen, proactively implementing cyber security measures that can detect and autonomously respond to threats as soon as they emerge.

Learn more about double extortion ransomware.

Darktrace model detections:

  • Device / Suspicious Network Scan Activity
  • Device / RDP Scan
  • Device / Network Scan
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous Connection / Unusual Admin RDP Session
  • Device / Multiple Lateral Movement Model Breaches
  • User / New Admin Credentials on Client
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration
  • Device / Anomalous SMB Followed By Multiple Model
  • Anomalous Connection / Download and Upload
  • Anomalous Connection / Suspicious Activity On High Risk Device
  • Anomalous File / Internal::Additional Extension Appended to SMB File
  • Compromise / Ransomware::Suspicious SMB Activity
  • Anomalous Connection / Sustained MIME Type Conversion
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / Large Number of Model Breaches
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager

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July 22, 2026

When AI Agents Go Off Script: What the OpenAI and Hugging Face Incident Means for Defenders

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The recent security incident disclosed jointly by OpenAI and Hugging Face is quickly becoming one of the most discussed AI security stories of the year; it's one that has generated great discussion amongst our researchers and technical teams here at Darktrace.

During an internal evaluation of advanced OpenAI models, an autonomous agent reportedly escaped its intended testing boundaries, obtained internet access, and compromised Hugging Face infrastructure while attempting to evaluate and benchmark the model's cyber capabilities. OpenAI has described it as an unprecedented cyber incident and is collaborating with Hugging Face to investigate and share its findings publicly to help defenders understand the implications and improve AI safety across the community.

What this means for organizations deploying AI agents

The most important takeaway for security leaders is not that the AI model is capable of complex and effective chained attacks. It's what this tells us about the guardrails and protections that need to be put in place to ensure these increasingly capable models are safely used within our businesses.

According to OpenAI's investigation, the models were not acting with malicious intent. They were attempting to achieve a legitimate assigned objective. In pursuing that objective, they identified a path that involved obtaining information from a third party's environment and followed it to completion. From the model's perspective, that appears to have been an effective solution to the problem it was given.

Many organizations still hold the assumption that giving an AI agent a legitimate goal will naturally result in legitimate behavior. Increasingly, that is not something defenders can safely assume.

A human who encounters a "permission denied" message generally understands the technical restriction as well as the social meaning behind it. An agent may interpret that message only as evidence that one method to achieve its objective has failed. What makes this incident notable is the agent’s ability to keep pursuing the objective, revise its plan, and combine many individually useful actions into an outcome its designers did not intend.

This is not an isolated challenge. Recent research from the UK's AI Security Institute found that frontier models often attempt to "cheat" evaluations by finding unintended shortcuts to success. More interestingly, that behavior is not always reflected in chain-of-thought outputs and is not always admitted when models are questioned about their actions. One lesson for defenders is that understanding what an AI system says it is doing is not always the same as understanding its behavior and what it is actually doing.

Why behavioral security is an essential security foundation

That's the principle behind Darktrace / SECURE AI and our broader approach to behavioral security. Understanding intent or putting in place static guardrails is not enough; security teams need visibility into how agents behave over time, what resources they interact with, how their behavior changes, and whether they begin pursuing objectives in unexpected ways. Research released by NIST earlier this summer reinforced that rules alone are not sufficient to defend against AI threats, with one conclusion being that "there will always be a way to prompt an AI system to disregard its rules - it's just a matter of finding it."

Another interesting lesson is that nothing described in OpenAI's preliminary findings appears fundamentally impossible for a skilled human attacker. But it does highlight the accessibility and scale that could be unlocked for attackers as increasingly capable models become more widely available. Resource constraints that attackers deal with will decrease as frontier models can help execute elements of those attack workflows autonomously, persist through failure, and iterate towards an objective. Capabilities that were once limited to skilled attackers can increasingly be supplemented with these systems, lowering the barrier to entry to conduct complex cyber operations.

It is incredibly positive to see OpenAI and Hugging Face investigating this incident collaboratively and sharing their findings publicly. Transparency and collaboration around incidents like these helps the entire security community better understand how rapidly AI capabilities are evolving and where we need to improve safeguards. It also reinforces why Darktrace is excited to be partnering with OpenAI through the Daybreak Cyber Partner Program: to explore how advanced AI can be used safely and responsibly in support of defenders while developing the visibility, governance, and security controls needed for increasingly autonomous systems.

This incident is an important reminder that capable AI systems will not always behave in the ways we expect. As organizations deploy AI agents across critical workflows, understanding behavior is an even more important foundation for effective security.

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July 22, 2026

How to Secure AI in Stadium Operations?

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Key takeaways

  • AI is entering high-impact stadium functions such as access control, crowd management, ticketing, facilities, and surveillance.  
  • Shadow AI and third-party AI use can create risks that stadium security teams cannot readily see.  
  • Security teams must understand not only which AI systems exist, but also what they can access and what actions they can take.  
  • Live-event resilience requires continuous monitoring and response across AI, IT, OT, identities, and third parties.

Modern stadiums are infrastructure unlike any other. I’ve written before on event day sparking stadiums into life with shops and food stands, transport hubs, vast telecommunications infrastructure, field-side technology and beyond, acting as one super-sized, connected ecosystem. Stadiums’ scale and complexity make them some of the toughest environments in cybersecurity. Now, we’re adding AI to those operations and bringing a new dimension of risk.

The benefits of AI in stadium operations are easy to see. It can help stadium operators move fans safely through crowded gates, forecast demand at concession stands, support biometric entry, identify suspicious behavior on CCTV, and manage heating and ventilation. Used well, it can make live events safer, faster, and more efficient.

But it also changes the security model.

In Darktrace’s recent research into the threat landscape surrounding sports, we asked cybersecurity professionals protecting professional sports organizations where in their footprint a cyber compromise would have the greatest impact. The area they named most, highlighted by 34% of the professionals we spoke to, was stadium operations. At the same time, 35% said their organizations are already using AI in stadium operations, or plan to do so in the next 12 months.

Security teams are no longer just protecting traditional IT systems around a stadium. They are increasingly being asked to protect AI systems that are operating in the stadium’s most fundamental functions.

Approved AI vs. shadow AI in stadium operations

There is a clear difference between AI a stadium’s security team knows about and AI it does not.

Approved AI is the AI that has been reviewed, tested, and integrated into the venue’s operating environment. It may support CCTV analytics, access control, facility management, ticketing, logistics, broadcast operations, or anti-piracy monitoring. It should have clear ownership, access controls, logging, vendor review, and data protection rules. That does not make it risk-free, but it allows security teams to institute proper governance.

Shadow AI is different. It is the unapproved use of AI tools by employees, contractors, or suppliers. It often starts with good intent. Someone wants to work faster. A staff member pastes internal information into a public AI tool to draft a briefing. A developer uses an AI assistant to debug ticketing code. A supplier connects an AI scheduling tool to delivery routes. A designer uploads unreleased venue plans or sponsor material to generate a mockup.

None of those actions may feel like a security decision to the person doing them. But each one can move sensitive operational data into an environment the stadium does not control, creating hidden risk.

The approved AI stack may be visible to security teams. The shadow AI stack often is not.

Why game day increases AI cybersecurity risk

In a typical enterprise environment, a security team may have hours to investigate a strange login or an unexpected connection to a third-party service. Within a stadium, the moment an incident is likely to occur is also the moment when teams are at their most stretched and the incident can have the greatest repercussions: game day.

If an AI system used for crowd management behaves unexpectedly, the issue is not only technical. It may affect physical movement inside the venue.

If a supplier tool is sending operational data to an unapproved AI platform, the issue is not only data governance. It may expose delivery routes, restricted access schedules, or staffing plans.

The most dangerous scenario is not always a loud, dramatic attack but a hidden dependency that no one has mapped such as a vendor adding an AI feature through a software update or a staff workflow using an unapproved tool.

By the time the venue is live, those hidden connections can become operational risk.

The supply chain is part of the stadium attack surface

Any major sporting event is made by its supply chain and partnerships: catering firms, transport providers, broadcast systems, facilities teams. Every piece is necessary and each creates a security channel. The risk of supply chain compromise has been well established for some time and has been the source of some of the most high-profile breaches we’ve seen. The data breach at MSG Entertainment, owner of Madison Square Garden, that was widely reported in March, originated in a breach of Oracle’s E-Business Suite, used in MSG Entertainment’s back-office systems, while the 2018 Olympic Destroyer attack on the Pyeongchang Winter Olympics reportedly began with the compromise of the main IT service provider for the Games. The addition of AI is heightening the risk.

A stadium can have strict rules for its own AI systems, but its vendors may be using separate tools. Some may use AI to manage staffing, delivery windows, inventory, or customer communications. Others may not realize that AI features have been added into software they already use.

This is one of the hardest parts of securing AI in stadium operations. The risk does not always come from a tool the venue selected. It may come from a tool a supplier selected or a feature the supplier did not know had been turned on.

Security teams need to treat vendor AI the same way they treat vendor access. They need to know what suppliers can connect to, what data they can see, what tools they use, and whether those tools introduce new routes for data exposure or lateral movement.

A third-party AI tool does not need deep access to create risk. Sometimes it only needs the right operational detail at the wrong time.

Four questions for securing AI in stadium operations

As AI becomes part of stadium operations, security teams need to move beyond basic approval lists. There are four questions they need to ask:

1. Where is AI being used?

This includes obvious tools, such as computer vision, access control, ticketing, logistics, and facility management. But it also includes less visible AI inside SaaS platforms, vendor tools, browser extensions, developer workflows, smart building systems, and collaboration tools.

2. What can the AI access?

Can it see incident logs, staffing plans, ticketing data, video feeds, building controls, fan information, credentials, or supplier systems? Can it only analyze information, or can it also trigger actions?

3. What can the AI do?

AI agents are not just passive tools. Some can call APIs, update records, generate instructions, trigger workflows, or act with the permissions of a user or service account. In a stadium, that distinction is critical. There is a big difference between an AI system that recommends an action and one that can take an action.

4. What does normal look like?

In your security architecture, static rules will not be enough. AI use changes quickly: tools appear inside existing platforms, vendors add new services, and staff find workarounds when they are under pressure. Security teams need to understand normal behavior across people, identities, devices, networks, cloud services, suppliers, and AI tools so they can spot when something changes.

That is especially important in live-event environments, where small anomalies can matter. A connection to an unapproved AI service may be harmless in one context and serious in another, and an AI agent taking action at 3 a.m. may be expected during setup but suspicious during a match. Context is what turns raw activity into useful security insight. It’s also what enables rapid response. Your own AI-based security systems can respond to threats at machine speed if they can build the live context to know action needs to be taken.

AI can make stadiums safer, but only if it is secured

AI has a real role to play in stadium operations. It can help teams detect crowd pressure earlier, reduce bottlenecks, manage facilities more efficiently, improve the fan experience, and support event teams during high-pressure moments.

The answer is not to slow all AI adoption. That's not the goal. The answer is to make AI visible, governed, and secure before it becomes part of match-day operations.

For stadium operators and event organizers, that means mapping AI use across the venue and supplier ecosystem. It means understanding what each AI system can access and what actions it can take. It means giving staff approved tools that meet their needs, rather than leaving them to find workarounds. It means writing AI use into vendor contracts and audits. And it means monitoring behavior across the full environment, not only the systems that are easiest to see. A stadium cannot secure what it cannot see.

When AI becomes part of how a stadium moves people, controls access, manages facilities, supports suppliers, and protects media rights, it stops being a side project. It becomes part of the event infrastructure.

Event infrastructure must be thoroughly prepared before venue gates open and sustained with the operational resilience required to support a secure, seamless, and reliable event experience.

How Darktrace helps secure AI in stadium operations

Darktrace brings more than a decade of behavioral AI expertise, built on an enterprise‑wide platform designed to operate in complex, ambiguous environments. We protect the large-scale integrated IT and OT environments that underpin stadium operations from the 2022 FIFA World Cup in Qatar, to Formula 1 Grand Prixes around the world and stadiums across the USA.

Other cybersecurity technologies try to predict each new attack based on historical attacks. The problem is that AI operates like humans do. Every action introduces new information that changes how AI behaves, making it unpredictable in nature. Historical attack tactics are now only a small part of the equation, forcing vendors to retrofit unproven acquisitions to secure AI.  

Darktrace is fundamentally different. Our Adaptive AI continuously learns how your people and AI behave, building an understanding of your organization so it can detect and respond autonomously when behavior deviates. Our Behavioral Defense Platform secures your AI, people, and infrastructure as you onboard new workflows, agents, and applications, enabling your AI transformation at scale.

As AI changes what organizations can do, Darktrace helps them move forward with confidence. We give the security teams defending the people and technology within stadium infrastructure the understanding, visibility, and autonomous action they need to protect new technologies as they are integrated into operations, so their organizations drive the progress that will define the AI era.

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