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February 12, 2018

The Rise of Cryptocurrency Attacks & Cyber Defense Solutions

Darktrace can detect cryptocurrency-related attacks with machine learning. Identify nefarious use of resources and protect against Coinhive drive-by mining.
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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12
Feb 2018

Prelude

The last 12 months have shown tremendous volatility in the value of cryptocurrencies, of which Bitcoin is the most prominent example. At the start of 2017, Bitcoin lingered around the $2,000 mark before suddenly taking off, climbing to historic highs of close to $20,000 in December 2017. Demand has since subsided, and at the time of writing, the price of Bitcoin is near to $10,772.

While Bitcoin is the most popular cryptocurrency, numerous alternatives, often called ‘altcoins’ have emerged and grown in value in the last 12 months. For example, Dogecoin, originally created to be a spoof cryptocurrency after a widespread internet meme, reached a notable market capitalization milestone of $2bn in January 2018.

Nowadays it is almost impossible to profitably mine Bitcoin on commodity hardware such as laptops, smartphones or desktop computers. At this late state, it just takes too long to perform the relevant calculations, and the cost of electricity is higher than the anticipated revenue in most cases. Other altcoins such as Monero use different algorithms, making them viable alternatives for aspiring crypto miners. It is often still feasible to mine altcoins on commodity hardware and see a return on investment.

The value of most altcoins is closely tied to the value of Bitcoin and, in many cases, the relationship is broadly proportional – a rise in Bitcoin prompting a similar lift in the altcoins. Monero, which has been rapidly adopted by Darknet markets, has profited from this effect. While Monero was valued at around $10 in January 2017, its price has been pumped up to $419 a year later.

There is much that is still not clear about the cryptocurrency phenomenon. Debate as to its relative value and its status as a currency rages, and will not be resolved any time soon. However, from a cyber security perspective there can be no doubt that the combination of altcoins being mineable on commodity hardware, the fact that mining is now becoming profitable as a side-effect of Bitcoin’s rise, and a maturity in cryptocurrency-related tech has led to a surge in cryptocurrency-related attacks.

Attack vectors

Darktrace has observed an abrupt increase of cryptocurrency-related attacks over the last 12 months. Both the frequency and the diversity of these attacks has grown significantly and largely mirrors the remarkable rise in the value of Bitcoin over that period.

Previously, cyber-criminals monetized their operations via banking Trojans/credit card fraud, selling stolen data and ransomware on the Darknet. However, criminals are notoriously adaptable and will follow the money wherever it leads, leading to an increase in cryptojacking’s popularity.

Cryptocurrency mining might not be as profitable as ransomware is upfront, but it can be secretly pursued for months without creating the havoc that characterizes ransomware attacks. Most users and security products might not notice a cryptocurrency miner being installed on a corporate device as it does not show obvious threats or messages to a user, except for an occasional increase in CPU or RAM usage.

Identifying these attacks can be very difficult for traditional security tools as they were not originally designed to catch this type of threat. Nor was Darktrace, but its approach – which relies on its evolving understanding of patterns of behavior – means that it can detect such attacks without having to know what to look for in advance.

Darktrace has detected a number of different attack vectors related to cryptocurrency attacks.

  1. Nefarious use of corporate resources
    Darktrace has detected a range of incidents where employees were intentionally installing cryptocurrency mining software on their corporate devices to mine for personal gain. These employees do not have to pay for the electricity used to run the corporate device in the office – they are basically turning their employer’s electricity into cash by commandeering it for mining operations.

    This is commonly seen as a compliance breach and increases the attack surface of a device that has mining software installed. It puts the corporate device at risk and also increases operational costs as the power consumption usually goes up for mining devices. The most popular cryptocurrency choices for this kind of mining in the last 12 months were Etherium and Monero – altcoins that can profitably be mined without the need for inordinate electricity.
  2. Coinhive drive-by mining
    Coinhive is a technology that allows website owners to use their visitors’ computing power to mine a tiny fraction of cryptocurrency for the website owner. Visitors will experience a small increase in computer resource consumption while browsing the website. Some websites experiment with this model to create new forms of revenue streams alternative to advertisement and banner placements.

    Coinhive usage is often not an opt-in process. Darktrace has observed various customer devices that regularly visit websites leveraging Coinhive technology. While the power consumption increase for a device browsing a website with Coinhive is ultimately negligible, the cumulative effect of a sizeable portion of the workforce unwittingly browsing websites using Coinhive results in increased power consumption cost for the organization as a whole.
  3. Malicious insider
    A malicious insider compromised his employer’s website to put a Coinhive script on there. This then mined Monero for every visitor on the employer’s website for the malicious insider’s personal gain.
  4. Traditional malware
    Cyber criminals are constantly looking to improve the return on investment of their operations. Reports suggest that criminals are starting to adjust their monetization methods based on the financial means of their targets. Suppose you can’t pay the fee extorted in a ransomware attack? They’ll just install a crypto miner on your device instead to ensure that the attack is not completely fruitless.

    As malware authors become more sophisticated, they often deploy multi-staged malware that can swap weaponized payloads. Once malware has infected a system successfully, its authors can often decide what actions to take next. Encrypt the device and extort a ransom? Install a banking Trojan to harvest credit card details? Install more spyware modules to look for data exfiltration? Or, now, install a cryptocurrency miner.

    These pieces of malware operate stealthily and often go undetected for several weeks. An infection might start with a phishing email that contains a macro-enabled document. As soon as a user enabled the macro, the malware will download a file-less stager that lives in memory and cannot be detected by traditional antivirus. Command and control communication is usually maintained via IP addresses that change on a daily basis in order to outrun threat intelligence and blacklisting attempts. As no obvious damage is done straight away, these attacks often stay under the radar for prolonged times, so long as self-learning technology such as Darktrace is not employed.

    This becomes much more concerning as malware authors could swap one payload for another overnight if they deem it more profitable, switching from a furtive crypto mining Trojan to ransomware the next day. While we have not observed this kind of attack in the wild yet, it is plausible, and in cyberspace what can be done, will be done.

Conclusions

Revolutionary technologies like cryptocurrencies have both their dark and light aspects. For all of the creative energy released by the crypto-blockchain revolution, Bitcoin and its alternatives have quickly become the universal currency of the criminal underworld. Indeed, the former Chief Economist of the World Bank, Joseph Stiglitz – an adamant critic of cryptocurrencies – has said that the whole value of Bitcoin resides in its “potential for circumvention” and “lack of oversight”.

While Stiglitz’s case may be overstated, there can be no question that cyber criminals have sensed a new opportunity to make money. A lot of organizations still regard crypto mining as a compliance incident. This can lead to grave consequences as a cryptocurrency mining device might lead to more severe incidents that can have a serious effect on business operations.

This kind of threat is difficult to detect as no obvious damage is done. However, with Darktrace’s machine learning we can correlate even the weakest indicators of such an attack into a compelling picture of threat. While traditional tools may struggle to see these deviations, Darktrace can pinpoint the changes in behavior effected by cryptocurrency miners without having to rely on any blacklists or 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
Max Heinemeyer
Global Field CISO

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June 15, 2026

Hola VPN Abuse: From Proxy Traffic to Malware and Cryptomining

hola vpn malware cryptominingDefault blog imageDefault blog image

Introduction

In enterprise environments, non-compliant software traffic can introduce unexpected exposure by creating unmanaged paths for outbound connectivity. Hola VPN is a notable example because of its peer-to-peer design, which can effectively turn user devices into routing or exit nodes for other parties’ traffic, shifting the risk profile from that of a traditional virtual private network (VPN) to something closer to a distributed proxy.

As a result, the appearance of Hola-related activity, whether from prior installation or unintended background connections, should be treated with caution.  Such activity may provide a foothold for malicious behavior, including lateral movement or command-and-control communication.

This blog explores how Hola-associated activity appeared as part of broader patterns of suspicious behavior observed across the Darktrace customer base.

The campaign

In February and March 2026, Darktrace observed similar anomalous activity across multiple customer environments, with affected devices showing consistent behavioral patterns. These included connections to multiple *.hola[.]org endpoints using Hola-related user agents, suggesting interaction with Hola infrastructure rather than isolated or incidental traffic.

Following these connections, affected customer environments showed downloads of suspicious executable files from rare external endpoints 188.241.219[.]55 and 184.241.218[.]111. Both endpoints have been flagged as potentially malicious by open-source intelligence (OSINT) [1][2].

These downloads were conducted using consistent user agents across impacted customers, specifically ‘Hola svc_js_win32/1.249.408’ and ‘Hola svc_js_win32/1.251.389’, suggesting a possible association with Hola-related activity.

Notably, this pattern aligns with recent reporting that, in some cases, Hola distributed an undeclared executable component, me[.]exe, which was later assessed to be a likely Monero-mining binary introduced via a compromised delivery pipeline [3].

Case Study 1

Darktrace first observed a new device on January 19, 2026, within a customer environment based in the Europe, Middle East, and Africa (EMEA) region. On the same day it appeared on the network, the device communicated with multiple pieces of Hola VPN-linked infrastructure before downloading a binary from a hola[.]org subdomain.

Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.
Figure 1: Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.

Subsequent Darktrace telemetry revealed a recurring pattern of activity from the day the device was first observed through to March 4, 2026. During this period, the device repeatedly issued HTTP GET requests to the URI /bwfile?size=1048576, each returning a 200 OK response, indicating successful file retrieval.

This behavior was accompanied by a POST request to /bwfile, followed by an additional GET request for a significantly larger file at /bwfile?size=26214400, suggesting a deliberate and structured file transfer pattern.

Notably, the binary download activity was not tied to a single static host. Instead, it was observed across multiple URLs that changed over time while remaining within the same hola[.]org domain. This pattern suggests the use of rotating or distributed delivery infrastructure rather than a fixed endpoint.

Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.
Figure 2: Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.

Across these events, the activity was consistently associated with the user agent Hola svc_js_win32/1.249.408, further linking the traffic to Hola-related service components. Amid these persistent and unusual connections, on February 22, Darktrace observed the device connecting to 188.241.219[.]55/proxy-peer-windows-amd64[.]exe, resulting in the download of an executable file.

 File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.
Figure 3: File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.

Based on its file hash, the downloaded file was assessed as a likely Trojan downloader [4], with import hash (imphash) values showing similarities to samples linked to Vidar, Rhadamanthys, and Stealc according to OSINT [5]. Overall, this sequence of activity suggests that Hola-related connectivity may have been leveraged as part of a broader malware delivery chain.

Darktrace’s Autonomous Response

Due to the highly unusual activity observed, Darktrace Autonomous Response was triggered by the device’s behavior. However, as the customer deployment was configured in “Human Confirmation” mode, manual approval was required before any action could be taken.

Had the deployment been set to “Fully Autonomous” mode, Darktrace would have automatically:

  1. Blocked connections to the associated ports and external endpoints
  2. Prevented all outgoing network connections from the device
  3. Enforced the device’s established ‘pattern of life’, allowing normal activity to continue while restricting any anomalous behavior
Figure 4: Example of a Darktrace Autonomous Response model highlighting the action that would have been taken, demonstrating how the system identifies anomalous behavior and applies targeted containment measures to restrict suspicious network activity.

Case Study 2

While the first case focused on anomalous activity from a newly observed device, Darktrace also identified cases in which devices had already been communicating with Hola-related endpoints prior to the suspected campaign. This may suggest pre-existing Hola usage within the environment, potentially increasing exposure and creating an avenue for subsequent suspicious activity.

One case involved three devices within a customer network based in the Americas (AMS). In this instance, a different payload was identified: me[.]exe, a potentially malicious cryptocurrency miner also referred to as HolaMonitorService[.]exe [6][7]. The downloads were observed from infrastructure similar to that seen in Case 1, including an IP address within the same 188.241.0.0/16 subnet.

Connections to *.hola[.]org, alongside the use of potential Hola-related user agents consistent with those in Case 1, were also identified, further suggesting a link between the observed activity and Hola-associated infrastructure.

Darktrace observed activity indicative of unusual VPN usage on the first affected device on February 2, followed by telemetry suggesting potential Tor usage. This was later followed by the download of me[.]exe on March 10 from 188.241.218[.]111. Notably, this device was the earliest among the three within the deployment to exhibit the presence of the suspicious executable.

Figure 5: Cyber AI Analyst detection highlighting the download of a suspicious executable from a similar external endpoint in a separate deployment.

On March 5, 2026, the second affected device exhibited a slightly different progression, initiating connections to http-test1[.]hola[.]org using the user agent ‘hola_get’. This activity was followed by the download of me[.]exe from the same endpoint on March 13, consistent with the broader pattern of Hola-related downloads observed across the environment.

 Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.
Figure 6: Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.

The final affected device within this customer’s network demonstrated a more limited but related pattern, also downloading me[.]exe on March 17 using the same ‘hola_get’ user agent.

While the earlier Hola VPN usage observed across the deployment may not have been directly related to the suspected malware campaign, it may nonetheless have contributed to reduced visibility. The presence of pre-existing Hola-related traffic could have obscured malicious activity, making it more difficult to distinguish legitimate usage from attacker-driven behavior and, in turn, hindering the timely identification of the emerging compromise.

Darktrace’s Autonomous Response

For this deployment, the customer had their Autonomous Response capability configured in “Fully Autonomous” mode, allowing Darktrace to take action without human intervention. As a result, the system was able to autonomously disrupt the activity as soon as relevant events were identified through model detections.

Figure 7: Darktrace Autonomous Response actions taken against suspicious activity linked to Hola VPN.

Suspected cryptomining activity

As previously noted, some of the observed executable payloads appear to be linked to cryptomining malware. Across a subset of affected customer environments, this assessment was further supported by subsequent device activity consistent with Monero mining. Affected devices established follow-on connections to multiple external endpoints aligned with known mining infrastructure, indicating post-download execution.

Considering the broader sequence of activity, this pattern may point to a wider form of abuse in which legitimate VPN-related traffic is used to mask or facilitate malicious behavior following compromise.

On several devices, the download of executable files, including a newly observed peer[.]exe, was followed by alerts indicative of cryptocurrency mining activity. Mining-related credentials such as ‘x’ were observed using the Minergate protocol to communicate with endpoints within the 89.125.255.0/24 subnet and 188.241.218[.]111, the same endpoint involved in earlier download activity. Additional credentials appeared to reflect device-specific CPU identifiers, for example ‘12th Gen Intel(R) Core (TM) i5-1235U’.

Observed mining methods included login, submit, and job, consistent with active participation in a pool-based mining workflow rather than passive or incidental contact. The login method indicates that the host authenticated to the mining service as a worker, job reflects the assignment of computational tasks, and submit shows completed work being returned to the pool [8]. This sequence suggests that affected devices were actively contributing processing resources as part of an unauthorized distributed mining operation.

The presence of unauthorized cryptominers can lead to degraded system performance and reduced device stability. Beyond the immediate resource impact, such activity often serves as an indicator of a broader compromise rather than an isolated issue. This may increase the risk of further malware deployment, persistence mechanisms, and lateral movement, particularly in environments where the initial intrusion has not been fully contained.

Conclusion

Across affected environments, detections such as unusual VPN usage, connections to Hola infrastructure, anomalous HTTP activity, suspicious file downloads, and subsequent cryptomining behavior were linked into a single, evolving incident narrative. This aggregation provided a clearer view of attack progression, enabling security teams to understand not just isolated alerts, but the full sequence of compromise from initial contact through to post-exploitation.

Ultimately, these activities show that the risk posed by non-compliant software such as Hola VPN can extend far beyond simple policy violations. What began as traffic to Hola-related infrastructure was, in multiple cases, followed by behavior suggesting deliberate misuse, including suspicious executable downloads using Hola-related user agents and, in some instances, evidence of active cryptomining. These were not isolated anomalies, but elements of a broader pattern in which seemingly benign proxy or VPN-related communications may have created a pathway for malicious delivery and unauthorized resource exploitation.

The significance of this activity lies not only in the downloads or mining, but in what it reveals about an attacker’s ability to blend malicious operations into traffic associated with software that may already have a foothold in the environment. When unapproved software operates within an enterprise, it can reduce visibility, blur the distinction between legitimate and malicious traffic, and create opportunities to extend compromise in ways that are persistent and difficult to detect. Darktrace’s anomaly-based approach enables these behavioral distinctions to be identified, regardless of whether the device is new or long established within the network.

Credit to Min Kim (Associate Principal Analyst), Priya Thapa (Senior Cyber Analyst)
Edited by Ryan Traill (Content Manager)

Appendices

References

[1] https://www.virustotal.com/gui/ip-address/188.241.219.55

[2]  https://www.virustotal.com/gui/ip-address/188.241.218.111

[3] https://www.sophos.com/en-us/blog/you-do-surprise-me-exe-an-unexpected-executable-in-hola-browser

[4] https://www.virustotal.com/gui/file/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/detection

[5] https://bazaar.abuse.ch/sample/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/

[6] https://any.run/report/4cdeb5df217764a8b6a20d518b76ccb30cbe623365a13d9dcd40900950f1ed99/de3a756a-3101-4369-8922-52c586c939fb

[7] https://www.virustotal.com/gui/file/e3541caf708c075f0bb22fc68b03acd8457fea7cf0732ea935b1eb016d1c7721/community

[8] https://bitcoinwiki.org/wiki/stratum

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Compromise / Crypto Currency Mining Activity

·      Compromise / High Priority Crypto Currency Mining (EM)

·      Device / New User Agent

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·      Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·      Antigena / Network / External Threat / Antigena Tor Block

·      Antigena / Network / External Threat / Antigena File then New Outbound Block

·      Antigena / Network / External Threat / Antigena Suspicious Activity Block

·      Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

·      Antigena / Network / External threat / Antigena Suspicious File Block

Indicators of Compromise (IoCs)

IoC –Type -Description + Confidence

188.241.219[.]55 - IP Address - Malware distribution source

188.241.218[.]111 - IP Address -Malware distribution source

hxxp://188.241.218[.]111:8080/me[.]exe - URI - Malicious payload

hxxp://188.241.219[.]55:9000/proxy-peer-windows-amd64[.]exe - URI - Malicious payload

hxxp://188.241.219[.]55:9000/peer[.]exe - URI - Malicious payload

C8088f3c8bc3542eb1ad78a7cc5306d866c8ac81 - SHA1 - Malicious payload, me[.]exe

b595a6de0f6a18975b29e6f8ebe604956a173478 - SHA1 - Malicious payload, me[.]exe

e9139a2e0839e8b9e5c9787ea936347ae56e5460 - SHA1 - Possible malicious payload

c2e80073e4cafe757d5643bd8fd45f28ad89bff9 - SHA1 - Possible malicious payload

695355eceedcdd337d8fcbd35e6a531cda75b847 - SHA1 - Possible malicious payload

f0b0d8068a1b9ab5d68a8a46842d72b870b292e7 - SHA1 - Possible malicious payload

a21c8b8cabc7670ea45bc175e185a0f9bfcf4733 - SHA1 - Malicious payload, me[.]exe

0353ca44b9f397d8f492db0b2f7a1d00a9e4406a - SHA1 - Possible malicious payload

56824c8a110e35ab303dc27a6c758cd50c36174c - SHA1 - Malicious payload, peer[.]exe

c141fa0fa505fe7f9ad5dd21d9d4d6d411739682 - SHA1 - Malicious payload, peer[.]exe

0417ec988b16f1267065185a6eea98f0bd2e17cd - SHA1 - Possible malicious payload

c54f7eaaeb3e0b528cd2584bdcb3a4b13cc0f8a2 - SHA1 - Malicious payload, peer[.]exe

11c78f15fafd53f8cc5a52b828d7cbf2a99e0b09 - SHA1 - Malicious payload, peer[.]exe

0258bf7dbb0123247db29e8799991140bbdbd9bb - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

b46043a06dd9bbd63e4214d5fbc7fd56e1ff0618 - SHA1 - Possible malicious payload

753afdecd9f5402d004e8e5f768170ae9a468ca5 - SHA1 - Possible malicious payload

8f533c7cb1524b00f7b0311c2ea8603298d6b2ca - SHA1 - Possible malicious payload

3a3bc6a5b4db1a4e961abcb002d26fe9d5e5c349 - SHA1 - Possible malicious payload

897f70eb41d302b045fcb05ed0693675e778ce57 - SHA1 - Possible malicious payload

6ddd5644809606e3dc1e2cc06059c3f5e6176f85 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

68a94f7cdcaf8853ea99251c1ecc67ae9b32eba8 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

MITRE ATT&CK Mapping

T1659 -Initial Access, Command and Control -Content Injection

T1588.001 -Resource Development -Malware

T1189 -Initial Access -Drive-by Compromise

T1105 -Command and Control -Ingress Tool Transfer

T1657 -Impact -Financial Theft

T1497.001 -Impact -Compute Hijacking

T1496 -Impact -Resource Hijacking

T1210 -Lateral Movement -Exploitation of Remote Services

T1036.012 -Stealth -Browser Fingerprint

T1071.001 -Command and Control -Web Protocols

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About the author
Min Kim
Cyber Security Analyst

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June 12, 2026

Cybersecurity for the Sports Sector: The Threats Facing a Digitized Industry in 2026

Sports Stadium cybersecurityDefault blog imageDefault blog image

Securing sporting events in 2026

When you walk into a stadium on game day, you are entering a small smart city. Ticketing, turnstiles, payments, public Wi-Fi for tens of thousands of fans, CCTV, lighting, even the HVAC all run on connected systems. The experience for fans has become unmatched, but that dependency has created a much larger attack surface than people may realize.

Our latest threat research backs that up. In the past year, a survey that Darktrace commissioned found that 84% of respondents from professional sports organizations had at least one cyber incident, and 57% were hit more than once. For a sector that relies on the impact of the live moment, those numbers translate directly into operational risk.

Why sports is a target for cyber attacks

Sport is a highly visible target with fixed timelines, so attackers know exactly when disruption will have the most impact. It also holds valuable data, athlete medical records, contracts, sponsorship deals, which carry financial, reputational, and regulatory risk if exposed. At the same time, delivery depends on a wide set of third parties: ticketing providers, broadcasters, cloud services, stadium technology. Any of those connections can become an entry point. Put visibility, timing, data, and dependency together, and you get an environment where even a small foothold can turn into a visible, time-critical incident.

How attackers target email and identity

Email and identity remain the front door. From October 2025 through March 2026, Darktrace / EMAIL™ detected more than 116,000 phishing emails aimed at sports organizations across our customer base, and our sports customers received 19% more phishing emails than organizations in other sectors. The numbers tell the story:

BY THE NUMBERS

  • 21% of phishing emails were aimed at VIPs.
  • 37% used novel social engineering.
  • 84% of malicious emails passed DMARC authentication

A large proportion of these emails passed authentication checks, which means traditional security controls are no longer a reliable barrier. Attackers are not relying on spoofed domains – they're using legitimate infrastructure and trusted platforms. Behavior matters. Once an account is compromised, the behavior shifts quickly. Login patterns change, inbox rules are created to hide responses, and accounts start being used for internal discovery or further phishing. These aren’t high-noise events. They sit in normal workflows, which is why they’re often missed.

Ransomware tells a similar story. In one case inside a sports deployment, attackers had quietly been moving data to an outside server for a full two weeks before they triggered encryption. By the time the ransom note appeared, the outcome was already set. That sequence shows up consistently is access first, movement next, disruption last. If detection starts at encryption, it’s already too late.

Why AI is an emerging blind spot in sports

The increasing adoption of AI is expanding the potential attack surface. 72% of the security professionals we surveyed expect AI to increase their cyber risk over the next year, and yet 35% are already using or planning to use it in stadium operations, the most critical functions to protect. In addition to prompt injection and AI build risks, shadow AI is becoming a more immediate issue. Staff are already putting sensitive data—performance metrics, scouting reports, contracts, health data—into tools with little or no governance. The upside is clear, but so is the exposure—and it is happening before most organizations have any visibility or control. At the same time, attackers are using the same technology to scale phishing and social engineering. The net effect is simple: more exposure, at higher speed.

How can cybersecurity professionals prepare

Across high profile events, Darktrace’s experience shows that effective cyber defense includes preparation, real‑time visibility, and the ability to respond dynamically and decisively when timing, complexity, and public exposure converge.

There are a few strategic implications for cybersecurity teams:

  • Get behavioral visibility across IT and OT, not just corporate systems.
  • Treat identity as your control plane. Most attacks in this sector start with credentials, not malware. MFA with behavioral detection helps solve that challenge.
  • Control third party and AI access the same way you control your own environment.
  • Rehearse response for live conditions, where decisions happen in minutes. Detection and response need to account for non-ideal conditions when engineers are under pressure and time constrained. In sport, timing is what turns small issues into major incidents. The same activity that would be manageable midweek becomes critical during a live event.

Why 2026 raises the cybersecurity stakes for sports

With the 2026 World Cup about to stretch across three countries and dozens of host cities, the attack surface is wide and the schedule is unforgiving.

Geopolitical signaling is raising the threat profile further. Previous international sporting events have demonstrated that nation‑state actors use the cyber domain to signal intent, influence narratives, or retaliate symbolically. In the context of the 2026 World Cup, Russia’s continued exclusion from international sport, the ongoing conflict in Ukraine, US defensive support to Ukraine, and Iran’s likely participation in the tournament introduce additional motivations for state‑aligned and non‑traditional affiliated actors to operate below the threshold of armed conflict. This doesn’t require new techniques—just the right timing and visibility.

In practice, this comes down to preparation: knowing what normal looks like across IT and OT, controlling third-party access, and spotting when behavior shifts.

In sport, disruption does not build slowly—it happens in real time and in public. By that point, the groundwork has already been set, long before the whistle goes.

About this research

Findings are based on Darktrace threat-research telemetry across sports-sector customer deployments (Q4 2025–Q1 2026) and a survey of 875 IT cybersecurity professionals in the US, UK, Australia, and Germany, fielded by Opinion Matters between May 28 and June 3, 2026. Read the full report for complete methodology, incident analysis, and strategic recommendations.

[related-resource]

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