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November 8, 2022

How Raccoon Stealer v2 Infects Systems

Learn about Raccoon Stealer v2's infection process and its implications for cybersecurity. Discover effective strategies to protect your systems.
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
Sam Lister
Specialist Security Researcher
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08
Nov 2022

Raccoon Stealer Malware

Since the release of version 2 of Raccoon Stealer in May 2022, Darktrace has observed huge volumes of Raccoon Stealer v2 infections across its client base. The info-stealer, which seeks to obtain and then exfiltrate sensitive data saved on users’ devices, displays a predictable pattern of network activity once it is executed. In this blog post, we will provide details of this pattern of activity, with the goal of helping security teams to recognize network-based signs of Raccoon Stealer v2 infection within their own networks. 

What is Raccoon Stealer?

Raccoon Stealer is a classic example of information-stealing malware, which cybercriminals typically use to gain possession of sensitive data saved in users’ browsers and cryptocurrency wallets. In the case of browsers, targeted data typically includes cookies, saved login details, and saved credit card details. In the case of cryptocurrency wallets (henceforth, ‘crypto-wallets’), targeted data typically includes public keys, private keys, and seed phrases [1]. Once sensitive browser and crypto-wallet data is in the hands of cybercriminals, it will likely be used to conduct harmful activities, such as identity theft, cryptocurrency theft, and credit card fraud.

How do you obtain Raccoon Stealer?

Like most info-stealers, Raccoon Stealer is purchasable. The operators of Raccoon Stealer sell Raccoon Stealer samples to their customers (called ‘affiliates’), who then use the info-stealer to gain possession of sensitive data saved on users’ devices. Raccoon Stealer affiliates typically distribute their samples via SEO-promoted websites providing free or cracked software. 

Is Raccoon Stealer Still Active?

On the 25th of March 2022, the operators of Raccoon Stealer announced that they would be suspending their operations because one of their core developers had been killed during the Russia-Ukraine conflict [2]. The presence of the hardcoded RC4 key ‘edinayarossiya’ (Russian for ‘United Russia’) within observed Raccoon Stealer v2 samples [3] provides potential evidence of the Raccoon Stealer operators’ allegiances.

Recent details shared by the US Department of Justice [4]/[5] indicate that it was in fact the arrest, rather than the death, of an operator which led the Raccoon Stealer team to suspend their operations [6]. As a result of the FBI, along with law enforcement partners in Italy and the Netherlands, dismantling Raccoon Stealer infrastructure in March 2022 [4], the Raccoon Stealer team was forced to build a new version of the info-stealer.  

On the 17th May 2022, the completion of v2 of the info-stealer was announced on the Raccoon Stealer Telegram channel [7].  Since its release in May 2022, Raccoon Stealer v2 has become extremely popular amongst cybercriminals. The prevalence of Raccoon Stealer v2 in the wider landscape has been reflected in Darktrace’s client base, with hundreds of infections being observed within client networks on a monthly basis.   

Since Darktrace’s SOC first saw a Raccoon Stealer v2 infection on the 22nd May 2022, the info-stealer has undergone several subtle changes. However, the info-stealer’s general pattern of network activity has remained essentially unchanged.  

How Does Raccoon Stealer v2 Infection Work?

A Raccoon Stealer v2 infection typically starts with a user attempting to download cracked or free software from an SEO-promoted website. Attempting to download software from one of these cracked/free software websites redirects the user’s browser (typically via several .xyz or .cfd endpoints) to a page providing download instructions. In May, June, and July, many of the patterns of download behavior observed by Darktrace’s SOC matched the pattern of behavior observed in a cracked software campaign reported by Avast in June [8].   

webpage whose download instructions led to a Raccoon Stealer v2
Figure 1: Above is a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on Discord CDN
example of a webpage whose download instructions led to a Raccoon Stealer v2
Figure 2: Above is an example of a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on Bitbucket
example of a webpage whose download instructions led to a Raccoon Stealer v2
Figure 3: Above is an example of a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on MediaFire

Following the instructions on the download instruction page causes the user’s device to download a password-protected RAR file from a file storage service such as ‘cdn.discordapp[.]com’, ‘mediafire[.]com’, ‘mega[.]nz’, or ‘bitbucket[.]org’. Opening the downloaded file causes the user’s device to execute Raccoon Stealer v2. 

The Event Log for an infected device,
Figure 4: The Event Log for an infected device, taken from Darktrace’s Threat Visualiser interface, shows a device contacting two cracked software websites (‘crackedkey[.]org’ and ‘crackedpc[.]co’) before contacting a webpage (‘premiumdownload[.]org) providing instructions to download Raccoon Stealer v2 from Bitbucket

Once Raccoon Stealer v2 is running on a device, it will make an HTTP POST request with the target URI ‘/’ and an unusual user-agent string (such as ‘record’, ‘mozzzzzzzzzzz’, or ‘TakeMyPainBack’) to a C2 server. This POST request consists of three strings: a machine GUID, a username, and a 128-bit RC4 key [9]. The posted data has the following form:

machineId=X | Y & configId=Z (where X is a machine GUID, Y is a username and Z is a 128-bit RC4 key) 

PCAP showing a device making an HTTP POST request with the User Agent header ‘record’ 
Figure 5:PCAP showing a device making an HTTP POST request with the User Agent header ‘record’ 
PCAP showing a device making an HTTP POST request with the User Agent header ‘mozzzzzzzzzzz’
Figure 6: PCAP showing a device making an HTTP POST request with the User Agent header ‘mozzzzzzzzzzz’
PCAP showing a device making an HTTP POST request with the User Agent header ‘TakeMyPainBack’
Figure 7: PCAP showing a device making an HTTP POST request with the User Agent header ‘TakeMyPainBack’

The C2 server responds to the info-stealer’s HTTP POST request with custom-formatted configuration details. These configuration details consist of fields which tell the info-stealer what files to download, what data to steal, and what target URI to use in its subsequent exfiltration POST requests. Below is a list of the fields Darktrace has observed in the configuration details retrieved by Raccoon Stealer v2 samples:

  • a ‘libs_mozglue’ field, which specifies a download address for a Firefox library named ‘mozglue.dll’
  • a ‘libs_nss3’ field, which specifies a download address for a Network System Services (NSS) library named ‘nss3.dll’ 
  • a ‘libs_freebl3’ field, which specifies a download address for a Network System Services (NSS) library named ‘freebl3.dll’
  • a ‘libs_softokn3’ field, which specifies a download address for a Network System Services (NSS) library named ‘softokn3.dll’
  • a ‘libs_nssdbm3’ field, which specifies a download address for a Network System Services (NSS) library named ‘nssdbm3.dll’
  • a ‘libs_sqlite3’ field, which specifies a download address for a SQLite command-line program named ‘sqlite3.dll’
  • a ‘libs_ msvcp140’ field, which specifies a download address for a Visual C++ runtime library named ‘msvcp140.dll’
  • a ‘libs_vcruntime140’ field, which specifies a download address for a Visual C++ runtime library named ‘vcruntime140.dll’
  • a ‘ldr_1’ field, which specifies the download address for a follow-up payload for the sample to download 
  • ‘wlts_X’ fields (where X is the name of a crypto-wallet application), which specify data for the sample to obtain from the specified crypto-wallet application
  • ‘ews_X’ fields (where X is the name of a crypto-wallet browser extension), which specify data for the sample to obtain from the specified browser extension
  • ‘xtntns_X’ fields (where X is the name of a password manager browser extension), which specify data for the sample to obtain from the specified browser extension
  • a ‘tlgrm_Telegram’ field, which specifies data for the sample to obtain from the Telegram Desktop application 
  • a ‘grbr_Desktop’ field, which specifies data within a local ‘Desktop’ folder for the sample to obtain 
  • a ‘grbr_Documents’ field, which specifies data within a local ‘Documents’ folder for the sample to obtain
  • a ‘grbr_Recent’ field, which specifies data within a local ‘Recent’ folder for the sample to obtain
  • a ‘grbr_Downloads’ field, which specifies data within a local ‘Downloads’ folder for the sample to obtain
  • a ‘sstmnfo_System Info.txt’ field, which specifies whether the sample should gather and exfiltrate a profile of the infected host 
  • a ‘scrnsht_Screenshot.jpeg’ field, which specifies whether the sample should take and exfiltrate screenshots of the infected host
  • a ‘token’ field, which specifies a 32-length string of hexadecimal digits for the sample to use as the target URI of its HTTP POST requests containing stolen data 

After retrieving its configuration data, Raccoon Stealer v2 downloads the library files specified in the ‘libs_’ fields. Unusual user-agent strings (such as ‘record’, ‘qwrqrwrqwrqwr’, and ‘TakeMyPainBack’) are used in the HTTP GET requests for these library files. In all Raccoon Stealer v2 infections seen by Darktrace, the paths of the URLs specified in the ‘libs_’ fields have the following form:

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/X (where X is the name of the targeted DLL file) 

Advanced Search logs for an infected host
Figure 8: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘record’ for DLL files
Advanced Search logs for an infected host
Figure 9: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘qwrqrwrqwrqwr’ for DLL files
Advanced Search logs for an infected host
Figure 10: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘TakeMyPainBack’ for DLL files

Raccoon Stealer v2 uses the DLLs which it downloads to gain access to sensitive data (such as cookies, credit card details, and login details) saved in browsers running on the infected host.  

Depending on the data provided in the configuration details, Raccoon Stealer v2 will typically seek to obtain, in addition to sensitive data saved in browsers, the following information:

  • Information about the Operating System and applications installed on the infected host
  • Data from specified crypto-wallet software
  • Data from specified crypto-wallet browser extensions
  • Data from specified local folders
  • Data from Telegram Desktop
  • Data from specified password manager browser extensions
  • Screenshots of the infected host 

Raccoon Stealer v2 exfiltrates the data which it obtains to its C2 server by making HTTP POST requests with unusual user-agent strings (such as ‘record’, ‘rc2.0/client’, ‘rqwrwqrqwrqw’, and ‘TakeMyPainBack’) and target URIs matching the 32-length string of hexadecimal digits specified in the ‘token’ field of the configuration details. The stolen data exfiltrated by Raccoon Stealer typically includes files named ‘System Info.txt’, ‘---Screenshot.jpeg’, ‘\cookies.txt’, and ‘\passwords.txt’. 

Advanced Search logs for an infected host
Figure 11: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating files named ‘System Info.txt’ and ‘---Screenshot.jpeg’
Advanced Search logs for an infected host
Figure 12: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating a file named ‘System Info.txt’ 
Advanced Search logs for an infected host
Figure 13: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating files named ‘System Info.txt’, ‘\cookies.txt’ and ‘\passwords.txt’
Advanced Search logs for an infected host
Figure 14: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating a file named ‘System Info.txt’

If a ‘ldr_1’ field is present in the retrieved configuration details, then Raccoon Stealer will complete its operation by downloading the binary file specified in the ‘ldr_1’ field. In all observed cases, the paths of the URLs specified in the ‘ldr_1’ field end in a sequence of digits, followed by ‘.bin’. The follow-up payload seems to vary between infections, likely due to this additional-payload feature being customizable by Raccoon Stealer affiliates. In many cases, the info-stealer, CryptBot, was delivered as the follow-up payload. 

Darktrace Coverage of Raccoon Stealer

Once a user’s device becomes infected with Raccoon Stealer v2, it will immediately start to communicate over HTTP with a C2 server. The HTTP requests made by the info-stealer have an empty Host header (although Host headers were used by early v2 samples) and highly unusual User Agent headers. When Raccoon Stealer v2 was first observed in May 2022, the user-agent string ‘record’ was used in its HTTP requests. Since then, it appears that the operators of Raccoon Stealer have made several changes to the user-agent strings used by the info-stealer,  likely in an attempt to evade signature-based detections. Below is a timeline of the changes to the info-stealer’s user-agent strings, as observed by Darktrace’s SOC:

  • 22nd May 2022: Samples seen using the user-agent string ‘record’
  • 2nd July 2022: Samples seen using the user-agent string ‘mozzzzzzzzzzz’
  • 29th July 2022: Samples seen using the user-agent string ‘rc2.0/client’
  • 10th August 2022: Samples seen using the user-agent strings ‘qwrqrwrqwrqwr’ and ‘rqwrwqrqwrqw’
  • 16th Sep 2022: Samples seen using the user-agent string ‘TakeMyPainBack’

The presence of these highly unusual user-agent strings within infected devices’ HTTP requests causes the following Darktrace DETECT/Network models to breach:

  • Device / New User Agent
  • Device / New User Agent and New IP
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Device / Three or More New User Agents

These DETECT models look for devices making HTTP requests with unusual user-agent strings, rather than specific user-agent strings which are known to be malicious. This method of detection enables the models to continually identify Raccoon Stealer v2 HTTP traffic, despite the changes made to the info-stealer’s user-agent strings.   

After retrieving configuration details from a C2 server, Raccoon Stealer v2 samples make HTTP GET requests for several DLL libraries. Since these GET requests are directed towards highly unusual IP addresses, the downloads of the DLLs cause the following DETECT models to breach:

  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Script from Rare External Location
  • Anomalous File / Multiple EXE from Rare External Locations

Raccoon Stealer v2 samples send data to their C2 server via HTTP POST requests with an absent Host header. Since these POST requests lack a Host header and have a highly unusual destination IP, their occurrence causes the following DETECT model to breach:

  • Anomalous Connection / Posting HTTP to IP Without Hostname

Certain Raccoon Stealer v2 samples download (over HTTP) a follow-up payload once they have exfiltrated data. Since the target URIs of the HTTP GET requests made by v2 samples end in a sequence of digits followed by ‘.bin’, the samples’ downloads of follow-up payloads cause the following DETECT model to breach:

  • Anomalous File / Numeric File Download

If Darktrace RESPOND/Network is configured within a customer’s environment, then Raccoon Stealer v2 activity should cause the following inhibitive actions to be autonomously taken on infected systems: 

  • Enforce pattern of life — This action results in a device only being able to make connections which are normal for it to make
  • Enforce group pattern of life — This action results in a device only being able to make connections which are normal for it or any of its peers to make
  • Block matching connections — This action results in a device being unable to make connections to particular IP/Port pairs
  • Block all outgoing traffic — This action results in a device being unable to make any connections 
The Event Log for an infected device
Figure 15: The Event Log for an infected device, taken from Darktrace’s Threat Visualiser interface, shows Darktrace RESPOND taking inhibitive actions in response to the HTTP activities of a Raccoon Stealer v2 sample downloaded from MediaFire

Given that Raccoon Stealer v2 infections move extremely fast, with the time between initial infection and data exfiltration sometimes less than a minute, the availability of Autonomous Response technology such as Darktrace RESPOND is vital for the containment of Raccoon Stealer v2 infections.  

Timeline of Darktrace stopping raccoon stealer.
Figure 16: Figure displaying the steps of a Raccoon Stealer v2 infection, along with the corresponding Darktrace detections

Conclusion

Since the release of Raccoon Stealer v2 back in 2022, the info-stealer has relentlessly infected the devices of unsuspecting users. Once the info-stealer infects a user’s device, it retrieves and then exfiltrates sensitive information within a matter of minutes. The distinctive pattern of network behavior displayed by Raccoon Stealer v2 makes the info-stealer easy to spot. However, the changes which the Raccoon Stealer operators make to the User Agent headers of the info-stealer’s HTTP requests make anomaly-based methods key for the detection of the info-stealer’s HTTP traffic. The operators of Raccoon Stealer can easily change the superficial features of their malware’s C2 traffic, however, they cannot easily change the fact that their malware causes highly unusual network behavior. Spotting this behavior, and then autonomously responding to it, is likely the best bet which organizations have at stopping a Raccoon once it gets inside their networks.  

Thanks to the Threat Research Team for its contributions to this blog.

References

[1] https://www.microsoft.com/security/blog/2022/05/17/in-hot-pursuit-of-cryware-defending-hot-wallets-from-attacks/

[2] https://twitter.com/3xp0rtblog/status/1507312171914461188

[3] https://www.esentire.com/blog/esentire-threat-intelligence-malware-analysis-raccoon-stealer-v2-0

[4] https://www.justice.gov/usao-wdtx/pr/newly-unsealed-indictment-charges-ukrainian-national-international-cybercrime-operation

[5] https://www.youtube.com/watch?v=Fsz6acw-ZJ

[6] https://riskybiznews.substack.com/p/raccoon-stealer-dev-didnt-die-in

[7] https://medium.com/s2wblog/raccoon-stealer-is-back-with-a-new-version-5f436e04b20d

[8] https://blog.avast.com/fakecrack-campaign

[9] https://blog.sekoia.io/raccoon-stealer-v2-part-2-in-depth-analysis/

Appendices

MITRE ATT&CK Mapping

Resource Development

• T1588.001 — Obtain Capabilities: Malware

• T1608.001 — Stage Capabilities: Upload Malware

• T1608.005 — Stage Capabilities: Link Target

• T1608.006 — Stage Capabilities: SEO Poisoning

Execution

•  T1204.002 — User Execution: Malicious File

Credential Access

• T1555.003 — Credentials from Password Stores:  Credentials from Web Browsers

• T1555.005 — Credentials from Password Stores:  Password Managers

• T1552.001 — Unsecured Credentials: Credentials  In Files

Command and Control

•  T1071.001 — Application Layer Protocol: Web Protocols

•  T1105 — Ingress Tool Transfer

IOCS

Type

IOC

Description

User-Agent String

record

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

mozzzzzzzzzzz

String used inUser Agent header of Raccoon Stealer v2’s HTTP requests

User-Agent String

rc2.0/client

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

qwrqrwrqwrqwr

String used in  User Agent header of Raccoon Stealer v2’s HTTP requests

User-Agent String

rqwrwqrqwrqw

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

TakeMyPainBack

String used in  User Agent header of Raccoon Stealer v2’s HTTP requests

Domain Name

brain-lover[.]xyz  

Raccoon Stealer v2 C2 infrastructure

Domain  Name

polar-gift[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

cool-story[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

fall2sleep[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

broke-bridge[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

use-freedom[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

just-trust[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

soft-viper[.]site

Raccoon Stealer  v2 C2 infrastructure

Domain Name

tech-lover[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

heal-brain[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

love-light[.]xyz

Raccoon Stealer v2 C2 infrastructure

IP  Address

104.21.80[.]14

Raccoon Stealer  v2 C2 infrastructure

IP Address

107.152.46[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

135.181.147[.]255

Raccoon Stealer  v2 C2 infrastructure

IP Address

135.181.168[.]157

Raccoon Stealer v2 C2 infrastructure

IP  Address

138.197.179[.]146

Raccoon Stealer  v2 C2 infrastructure

IP Address

141.98.169[.]33

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.170[.]100

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.170[.]175

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.170[.]98

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.173[.]33

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.173[.]72

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.247[.]175

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.247[.]177

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.70.125[.]95

Raccoon Stealer v2 C2 infrastructure

IP  Address

152.89.196[.]234

Raccoon Stealer  v2 C2 infrastructure

IP Address

165.225.120[.]25

Raccoon Stealer v2 C2 infrastructure

IP  Address

168.100.10[.]238

Raccoon Stealer  v2 C2 infrastructure

IP Address

168.100.11[.]23

Raccoon Stealer v2 C2 infrastructure

IP  Address

168.100.9[.]234

Raccoon Stealer  v2 C2 infrastructure

IP Address

170.75.168[.]118

Raccoon Stealer v2 C2 infrastructure

IP  Address

172.67.173[.]14

Raccoon Stealer  v2 C2 infrastructure

IP Address

172.86.75[.]189

Raccoon Stealer v2 C2 infrastructure

IP  Address

172.86.75[.]33

Raccoon Stealer  v2 C2 infrastructure

IP Address

174.138.15[.]216

Raccoon Stealer v2 C2 infrastructure

IP  Address

176.124.216[.]15

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.106.92[.]14

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.173.34[.]161

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.173.34[.]161  

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.225.17[.]198

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.225.19[.]190

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.225.19[.]229

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.53.46[.]103

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.53.46[.]76

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.53.46[.]77

Raccoon Stealer v2 C2 infrastructure

IP  Address

188.119.112[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

190.117.75[.]91

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.106.191[.]182

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.149.129[.]135

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.149.129[.]144

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.149.180[.]210

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.149.185[.]192

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.233.193[.]50

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]17

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]192

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]213

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]214

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]215

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]26

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]45

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.56.146[.]177

Raccoon Stealer  v2 C2 infrastructure

IP Address

194.180.174[.]180

Raccoon Stealer v2 C2 infrastructure

IP  Address

195.201.148[.]250

Raccoon Stealer  v2 C2 infrastructure

IP Address

206.166.251[.]156

Raccoon Stealer v2 C2 infrastructure

IP  Address

206.188.196[.]200

Raccoon Stealer  v2 C2 infrastructure

IP Address

206.53.53[.]18

Raccoon Stealer v2 C2 infrastructure

IP  Address

207.154.195[.]173

Raccoon Stealer  v2 C2 infrastructure

IP Address

213.252.244[.]2

Raccoon Stealer v2 C2 infrastructure

IP  Address

38.135.122[.]210

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.10.20[.]248

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.11.19[.]99

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]110

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.133.216[.]145

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]148

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.133.216[.]249

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]71

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.140.146[.]169

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.140.147[.]245

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.212[.]100

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.142.213[.]24

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.215[.]91

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.142.215[.]91  

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.215[.]92

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.144.29[.]18

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.144.29[.]243

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.15.156[.]11

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.15.156[.]2

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.15.156[.]31

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.15.156[.]31

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.150.67[.]156

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.153.230[.]183

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.153.230[.]228

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.159.251[.]163

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.159.251[.]164

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.61.136[.]67

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.61.138[.]162

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.67.228[.]8

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.67.231[.]202

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.67.34[.]152

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.67.34[.]234

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.144[.]187

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.144[.]54

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.144[.]55

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.145[.]174

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.145[.]83

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.147[.]39

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.147[.]79

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.84.0.152

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.86.86[.]78

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.54[.]110

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.54[.]110

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.54[.]95

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]115

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]117

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]193

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]198

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]20

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.92.156[.]150

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]154

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.36[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]231

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.36[.]232

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]233

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.39[.]34

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.39[.]74

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.39[.]75

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.39[.]77

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.118[.]33

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.176[.]62

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]217

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]234

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]43

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]47

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]92

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]98

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.22[.]142

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.23[.]100

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.23[.]25

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.23[.]76

Raccoon Stealer v2 C2 infrastructure

IP  Address

51.195.166[.]175

Raccoon Stealer  v2 C2 infrastructure

IP Address

51.195.166[.]176

Raccoon Stealer v2 C2 infrastructure

IP  Address

51.195.166[.]194

Raccoon Stealer  v2 C2 infrastructure

IP Address

51.81.143[.]169

Raccoon Stealer v2 C2 infrastructure

IP  Address

62.113.255[.]110

Raccoon Stealer  v2 C2 infrastructure

IP Address

65.109.3[.]107

Raccoon Stealer v2 C2 infrastructure

IP  Address

74.119.192[.]56

Raccoon Stealer  v2 C2 infrastructure

IP Address

74.119.192[.]73

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.232.39[.]101

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.73.133[.]0

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.73.133[.]4

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.73.134[.]45

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.75.230[.]25

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.75.230[.]39

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.75.230[.]70

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.75.230[.]93

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.100[.]101

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]12

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.102[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]44

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.102[.]57

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.103[.]31

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.73[.]154

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.73[.]213

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.73[.]32

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.74[.]67

Raccoon Stealer  v2 C2 infrastructure

IP Address

78.159.103[.]195

Raccoon Stealer v2 C2 infrastructure

IP  Address

78.159.103[.]196

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.66.87[.]23

Raccoon Stealer v2 C2 infrastructure

IP  Address

80.66.87[.]28

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.71.157[.]112

Raccoon Stealer v2 C2 infrastructure

IP  Address

80.71.157[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.92.204[.]202

Raccoon Stealer v2 C2 infrastructure

IP  Address

87.121.52[.]10

Raccoon Stealer  v2 C2 infrastructure

IP Address

88.119.175[.]187

Raccoon Stealer v2 C2 infrastructure

IP  Address

89.185.85[.]53

Raccoon Stealer  v2 C2 infrastructure

IP Address

89.208.107[.]42

Raccoon Stealer v2 C2 infrastructure

IP  Address

89.39.106[.]78

Raccoon Stealer  v2 C2 infrastructure

IP Address

91.234.254[.]126

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.104[.]16

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.104[.]17

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.104[.]18

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.106[.]116

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.106[.]224

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.107[.]132

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.107[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.96[.]109

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.97[.]129

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.97[.]53

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.97[.]56

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.97[.]57

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.98[.]5

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.244[.]114

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.244[.]119

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.244[.]21

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.247[.]24

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.247[.]26

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.247[.]30

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.247[.]44

Raccoon Stealer v2 C2 infrastructure

IP  Address

95.216.109[.]16

Raccoon Stealer  v2 C2 infrastructure

IP Address

95.217.124[.]179

Raccoon Stealer v2 C2 infrastructure

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/mozglue.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/nss3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/freebl3.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/softokn3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/nssdbm3.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/sqlite3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/msvcp140.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/vcruntime140.dll

URI used in download of library file

URI

/C9S2G1K6I3G8T3X7/56296373798691245143.bin

URI used in  download of follow-up payload

URI

/O6K3E4G6N9S8S1/91787438215733789009.bin

URI used in download of follow-up  payload

URI

/Z2J8J3N2S2Z6X2V3S0B5/45637662345462341.bin

URI used in  download of follow-up payload

URI

/rgd4rgrtrje62iuty/19658963328526236.bin

URI used in download of follow-up  payload

URI

/sd325dt25ddgd523/81852849956384.bin

URI used in  download of follow-up payload

URI

/B0L1N2H4R1N5I5S6/40055385413647326168.bin

URI used in download of follow-up  payload

URI

/F5Q8W3O3O8I2A4A4B8S8/31427748106757922101.bin

URI used in  download of follow-up payload

URI

/36141266339446703039.bin

URI used in download of follow-up  payload

URI

/wH0nP0qH9eJ6aA9zH1mN/1.bin

URI used in  download of follow-up payload

URI

/K2X2R1K4C6Z3G8L0R1H0/68515718711529966786.bin

URI used in download of follow-up  payload

URI

/C3J7N6F6X3P8I0I0M/17819203282122080878.bin

URI used in  download of follow-up payload

URI

/W9H1B8P3F2J2H2K7U1Y7G5N4C0Z4B/18027641.bin

URI used in download of follow-up  payload

URI

/P2T9T1Q6P7Y5J3D2T0N0O8V/73239348388512240560937.bin

URI used in  download of follow-up payload

URI

/W5H6O5P0E4Y6P8O1B9D9G0P9Y9G4/671837571800893555497.bin

URI used in download of follow-up  payload

URI

/U8P2N0T5R0F7G2J0/898040207002934180145349.bin

URI used in  download of follow-up payload

URI

/AXEXNKPSBCKSLMPNOMNRLUEPR/3145102300913020.bin

URI used in download of follow-up  payload

URI

/wK6nO2iM9lE7pN7e/7788926473349244.bin

URI used in  download of follow-up payload

URI

/U4N9B5X5F5K2A0L4L4T5/84897964387342609301.bin

URI used in download of follow-up  payload

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
Sam Lister
Specialist Security Researcher

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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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OT

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June 12, 2026

Protecting Stadiums & Events with AI

Default blog imageDefault blog image

Stadium and large public venue operators are confronted with a unique set of cyber security challenges. Often described as a ‘honeypot’ for cyber-criminals, the sports and entertainment industry is an attractive target for threat actors for three main reasons:

  • Modern sports organizations process sensitive and highly valuable data at scale;
  • Sporting events are highly visible and time-critical, operating in front of live audiences with no room for error;
  • Sports organizations rely on sprawling vendor ecosystems and supply chains to deliver broadcast, commerce, fan engagement services, and more.

In a recent Darktrace-commissioned survey, 84% of professional sports organizations reported at least one cyber incident in the past year, and 57% were hit more than once [1]. The potential ramifications of cyber disruption during a large-scale sports event cannot be overstated. A momentary lapse in access to power could bring TV broadcasts to a halt; disruption to access controls could restrict fans from entering the grounds; CCTV outages could increase the risk of criminal behavior and physical injuries. If data is not reliable and stadium machines are outputting the wrong metrics, a venue could become dangerously overcrowded. The barrier between the cyber and physical worlds has long dissolved – cyber-attacks threaten human safety.

In this blog, I explore the key challenges of stadium cyber security and explain the unique capabilities of Self-Learning AI that led me to adopt Darktrace as a head of ICT and cyber security for international venues and events. Over my career I have helped secure football and rugby World Cups, World Athletics Championships and more than 500 events ,and the lessons from each have only sharpened my conviction in this approach.

The access paradox

The biggest challenge lies in the paradox of securing a site where various internal services are provided to a large number of unknown and unmanaged users, suppliers and devices. When it’s game time, or ‘D-Day’, you see a huge influx of thousands of people, each with their own devices, needing to connect to your network and your infrastructure. The floodgates are opened. But certain parts of your digital environment need to remain protected: your sensitive employee and customer data, your critical OT systems. I liken this to opening the door to your home, and letting the entire town come in and wander around. But you still need to secure your master bedroom.

A multitude of different actors must be able to work on-site to provide services or content during the event. Broadcasters, staff and suppliers need to have access to manage the show, and all these people need to access or interact with the IT infrastructure. In many ways, these additional bodies are already inside the perimeter and could host unknown malicious threats.

This year, the paradox is wider than ever. A tournament spread across hundreds of suppliers and vendors means the foothold an attacker needs may already belong to a trusted partner – a single compromised supplier can become the doorway to everything else. And the adversary is no longer working alone: generative AI now lets attackers probe and weaponize vulnerabilities across thousands of software dependencies at a speed no human team could match, turning the access paradox from a manageable risk into a fast-moving target.

Achieving this balance between accessibility and security requires a shift in mindset from perimeter-based security to one that can detect and respond to threats on the inside. The complexities involved requires technology that can identify malicious behavior in real time based on the wider context of an incident. A particular behavior or connection may be benign in one context and yet critically disruptive in another — tools and technology must be able to discern between the two.

This is why I considered Darktrace’s Self-Learning AI a suitable fit: rather than defending at the perimeter, it focuses on detecting and responding to malicious activity already inside. Because it learns the unique ‘patterns of life’ of its surroundings, it can detect subtle deviations that indicate a threat and initiate a targeted response – without relying on pre-programmed rules and playbooks.

IT/OT convergence

The second key challenge is the issue of IT and OT convergence. Typical stadiums and arenas consist of a wide range of Industrial Control Systems (ICS).

This involves a complex and messy array of switches, cables, CCTV cameras, as well as devices and technologies being brought in by the media and the press, and all these IT and OT components are now interconnected, which means these technologies now have Internet Protocol (IP)-based threats to manage. The same challenges that the corporate infrastructure for stadium management faces in cyber security are therefore also now an issue for ICS security.

This challenge cannot be addressed by viewing IT and OT security in isolation — these two environments are linked because of the analogue migration to IP. A unified approach is required to detect and respond to threats that start in IT before moving to industrial systems.

The stakes are physical. CCTV, Access Control, Public Annoucement system, lighting and the giant screens are all now running over IP, and a disruption to any of them can force a venue to halt play on safety grounds. Scale compounds the problem. At the Qatar 2022 World Cup, eight stadiums were purpose-built to a single technical standard, which made the digital environment relatively uniform to defend. The 2026 tournament is the opposite: dozens of host venues across three countries, each with its own operator, its own contractors and its own legacy systems.This creates a far more fragmented and unpredictable estate to secure.

In addition, cyber security technology must be able to deal with complexity. Darktrace’s AI thrives in the most complex environments, with more data points adding more context to inform the AI’s decision making. It covers OT and IT with a single, unified AI engine, that can also detect and respond across cloud infrastructure, SaaS applications, email systems and endpoints. It is ready to adapt to the messy, interconnected systems that make up large stadiums’ digital infrastructure.

The time factor

Finally, the nature of stadium events means that timing is critical and puts enormous pressure on the organizers and operators. ‘D-Day’ cannot be replayed or postponed, and so if cyber disruption occurs during the event, every minute is crucial. You cannot reschedule a World Cup final or move an opening ceremony; the date is fixed, the world is watching, and there is no second take.

There is consequently a strong emphasis on two key metrics

  • Mean Time To Know (MTTK) — how long it takes the security team need to be aware of an incident; and
  • Mean Time To Restore (MTTR) — how quickly a team can act to contain the threat.

It is perhaps more imperative in stadium event management than anywhere else that these two metrics be minimized.

This leads to the third criteria in assessing cyber security technology: does it help with response? And critically, can that response be nuanced and targeted, able to contain that threat without causing further disruption?

To this end, Darktrace’s Autonomous Response takes machine-speed action to contain cyber-attacks, when humans are too slow to react or aren’t around at all. It’s powered by Darktrace’s AI, so it has a nuanced and continuously updating understanding of what’s ‘normal’ across IT and OT systems. This means its response actions are targeted: designed to eliminate the threat, but not at the cost of disruption. Crucially, this enables responses that are surgical rather than blunt. For example, taking an entire server offline to stop a ransomware attack can cause more disruption than the attack itself, so the real value lies in neutralizing the malicious activity precisely — containing the threat without taking down the systems the event and business depends on.

Depending on the nature and severity of the threat, the technology can block specific malicious connections by enforcing the normal ‘pattern of life’ of a device or account. When every second counts, this is the speed and granularity that you need in a cybersecurity technology.

Darktrace can be deployed across every area of the digital enterprise, including network, email, cloud and SaaS environments with the same self-learning approach, stopping anomalous behaviors that point to account takeover and other cloud-based threats. Earlier this year, we announced that Darktrace is also extending its behavioral approach to help businesses deploy and scale AI securely by understanding how these AI systems and agents behave, interact with other systems and humans, and evolve over time. This is critical because 72% of cybersecurity professionals at sports organizations believe AI will increase their cyber risk over the next 12 months [2].

Wherever it is deployed, Darktrace allows the stadium operator to focus on the vital part of the game and offers real-time protection without any modification in the network topology or infrastructure.

An adaptive defense

Cyber-criminals are constantly developing their approach in an attempt to evade security tools trained to look for specific hallmarks of an attack. As they get creative and continuously experiment with new tactics and techniques, the human operators using these tools are forced into a constant state of catch up.

An AI-based approach that learns an organization and its normal behavior patterns from the ground up puts an end to this game of ‘cat and mouse’, shifting the balance in favor of the defenders and allowing them to stay ahead of the threat. This matters more than ever, because adversaries are now using AI to scale their attacks. If you do not have AI working to protect you against malicious AI, you are already at a disadvantage.

With a nuanced understanding of what’s ‘normal’ for the business, unified IT/OT coverage, and an Autonomous Response solution that takes immediate, surgical action, the playing field is leveled, and large stadium and events operators can focus on delivering the best possible experience for attendees, digital viewers, partners and performers.

References:

[1] [2] Darktrace: Cybersecurity in Global Sport, June 2026. Findings based on survey of 875 IT cybersecurity professionals based in the US, UK, Australia and Germany, working in professional sports organizations (including clubs, societies & sporting bodies) employing 10+ people. The survey was fielded between May 28, 2026 and June 3, 2026 by independent market research agency, Opinion Matters.

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