April 5, 2023

Understanding Qakbot Infections and Attack Paths

Explore the network-based analysis of Qakbot infections with Darktrace. Learn about the various attack paths used by cybercriminals and Darktrace's response.

Written by

Sam Lister

Specialist Security Researcher

Written by

Connor Mooney

SOC Analyst

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

Written by

Connor Mooney

SOC Analyst

Apr 2023

In an ever-changing threat landscape, security vendors around the world are forced to quickly adapt, react, and respond to known attack vectors and threats. In the face of this, malicious actors are constantly looking for novel ways to gain access to networks. Whether that’s through new exploitations of network vulnerabilities or new delivery methods, attackers and their methods are continually evolving. Although it is valuable for organizations to leverage threat intelligence to keep abreast of known threats to their networks, intelligence alone is not enough to defend against increasingly versatile attackers. Having an autonomous decision maker able to detect and respond to emerging threats, even those employing novel or unknown techniques, is paramount to defend against network compromise.

At the end of January 2023, threat actors began to abuse OneNote attachments to deliver the malware strain, Qakbot, onto users' devices. Widespread adoption of this novel delivery method resulted in a surge in Qakbot infections across Darktrace's customer base between the end of January 2023 and the end of February 2023. Using its Self-Learning AI, Darktrace was able to uncover and respond to these so-called ‘QakNote’ infections as the new trend emerged. Darktrace detected and responded to the threat at multiple stages of the kill chain, preventing damaging and widespread compromise to customer networks.

Qakbot and The Recent Weaponization of OneNote

Qakbot first appeared in 2007 as a banking trojan designed to steal sensitive data such as banking credentials. Since then, Qakbot has evolved into a highly modular, multi-purpose tool, with backdoor, payload delivery, reconnaissance, lateral movement, and data exfiltration capabilities. Although Qakbot's primary delivery method has always been email-based, threat actors have been known to modify their email-based delivery methods of Qakbot in the face of changing circumstances. In the first half of 2022, Microsoft started rolling out versions of Office which block XL4 and VBA macros by default [1]/[2]/[3]. Prior to this change, Qakbot email campaigns typically consisted in the spreading of deceitful emails with Office attachments containing malicious macros. In the face of Microsoft's default blocking of macros, threat actors appeared to cease delivering Qakbot via Office attachments, and shifted to primarily using HTML attachments, through a method known as 'HTML smuggling' [4]/[5]. After the public disclosure [6] of the Follina vulnerability (CVE-2022-30190) in Microsoft Support Diagnostic Tool (MSDT) in May 2022, Qakbot actors were seen capitalizing on the vulnerability to facilitate their email-based delivery of Qakbot payloads [7]/[8]/[9].

Given the inclination of Qakbot actors to adapt their email-based delivery methods, it is no surprise that they were quick to capitalize on the novel OneNote-based delivery method which emerged in December 2022. Since December 2022, threat actors have been seen using OneNote attachments to deliver a variety of malware strains, ranging from Formbook [10] to AsynRAT [11] to Emotet [12]. The abuse of OneNote documents to deliver malware is made possible by the fact that OneNote allows for the embedding of executable file types such as HTA files, CMD files, and BAT files. At the end of January 2023, actors started to leverage OneNote attachments to deliver Qakbot [13]/[14]. The adoption of this novel delivery method by Qakbot actors resulted in a surge in Qakbot infections in the wider threat landscape and across the Darktrace customer base.

Observed Activity Chains

Between January 31 and February 24, 2023, Darktrace observed variations of the following pattern of activity across its customer base:

1. User's device contacts OneNote-related endpoint

2. User's device makes an external GET request with an empty Host header, a target URI whose final segment consists in 5 or 6 digits followed by '.dat', and a User-Agent header referencing either cURL or PowerShell. The GET request is responded to with a DLL file

3. User's device makes SSL connections over ports 443 and 2222 to unusual external endpoints, and makes TCP connections over port 65400 to 23.111.114[.]52

4. User's device makes SSL connections over port 443 to an external host named 'bonsars[.]com' (IP: 194.165.16[.]56) and TCP connections over port 443 to 78.31.67[.]7

5. User’s device makes call to Endpoint Mapper service on internal systems and then connects to the Service Control Manager (SCM)

6. User's device uploads files with algorithmically generated names and ‘.dll’ or ‘.dll.cfg’ file extensions to SMB shares on internal systems

7. User's device makes Service Control requests to the systems to which it uploaded ‘.dll’ and ‘.dll.cfg’ files

Further investigation of these chains of activity revealed that they were parts of Qakbot infections initiated via interactions with malicious OneNote attachments.

Figure 1: Steps of observed QakNote infections.

Delivery Phase

Users' interactions with malicious OneNote attachments, which were evidenced by devices' HTTPS connections to OneNote-related endpoints, such as 'www.onenote[.]com', 'contentsync.onenote[.]com', and 'learningtools.onenote[.]com', resulted in the retrieval of Qakbot DLLs from unusual, external endpoints. In some cases, the user's interaction with the malicious OneNote attachment caused their device to fetch a Qakbot DLL using cURL, whereas, in other cases, it caused their device to download a Qakbot DLL using PowerShell. These different outcomes reflected variations in the contents of the executable files embedded within the weaponized OneNote attachments. In addition to having cURL and PowerShell User-Agent headers, the HTTP requests triggered by interaction with these OneNote attachments had other distinctive features, such as empty host headers and target URIs whose last segment consists in 5 or 6 digits followed by '.dat'.

Figure 2: Model breach highlighting a user’s device making a HTTP GET request to 198.44.140[.]78 with a PowerShell User-Agent header and the target URI ‘/210/184/187737.dat’.

Figure 3: Model breach highlighting a user’s device making a HTTP GET request to 103.214.71[.]45 with a cURL User-Agent header and the target URI ‘/70802.dat’.

Figure 4: Event Log showing a user’s device making a GET request with a cURL User-Agent header to 185.231.205[.]246 after making an SSL connection to contentsync.onenote[.]com.

Figure 5: Event Log showing a user’s device making a GET request with a cURL User-Agent header to 185.231.205[.]246 after making an SSL connection to www.onenote[.]com.

Command and Control Phase

After fetching Qakbot DLLs, users’ devices were observed making numerous SSL connections over ports 443 and 2222 to highly unusual, external endpoints, as well as large volumes of TCP connections over port 65400 to 23.111.114[.]52. These connections represented Qakbot-infected devices communicating with command and control (C2) infrastructure. Qakbot-infected devices were also seen making intermittent connections to legitimate endpoints, such as 'xfinity[.]com', 'yahoo[.]com', 'verisign[.]com', 'oracle[.]com', and 'broadcom[.]com', likely due to Qakbot making connectivity checks.

Figure 6: Event Log showing a user’s device contacting Qakbot C2 infrastructure and making connectivity checks to legitimate domains.

Figure 7: Event Log showing a user’s device contacting Qakbot C2 infrastructure and making connectivity checks to legitimate domains.

Cobalt Strike and VNC Phase

After Qakbot-infected devices established communication with C2 servers, they were observed making SSL connections to the external endpoint, bonsars[.]com, and TCP connections to the external endpoint, 78.31.67[.]7. The SSL connections to bonsars[.]com were C2 connections from Cobalt Strike Beacon, and the TCP connections to 78.31.67[.]7 were C2 connections from Qakbot’s Virtual Network Computing (VNC) module [15]/[16]. The occurrence of these connections indicate that actors leveraged Qakbot infections to drop Cobalt Strike Beacon along with a VNC payload onto infected systems. The deployment of Cobalt Strike and VNC likely provided actors with ‘hands-on-keyboard’ access to the Qakbot-infected systems.

Figure 8: Advanced Search logs showing a user’s device contacting OneNote endpoints, fetching a Qakbot DLL over HTTP, making SSL connections to Qakbot infrastructure and connectivity checks to legitimate domains, and then making SSL connections to the Cobalt Strike endpoint, bonsars[.]com.

Figure 9: Event Log showing a user’s device contacting the Cobalt Strike C2 endpoint, bonsars[.]com, and the VNC C2 endpoint, 78.31.67[.]7, whilst simultaneously contacting the Qakbot C2 endpoint, 47.32.78[.]150.

Lateral Movement Phase

After dropping Cobalt Strike Beacon and a VNC module onto Qakbot-infected systems, actors leveraged their strengthened foothold to connect to the Service Control Manager (SCM) on internal systems in preparation for lateral movement. Before connecting to the SCM, infected systems were seen making calls to the Endpoint Mapper service, likely to identify exposed Microsoft Remote Procedure Call (MSRPC) services on internal systems. The MSRPC service, Service Control Manager (SCM), is known to be abused by Cobalt Strike to create and start services on remote systems. Connections to this service were evidenced by OpenSCManager2 (Opnum: 0x40) and OpenSCManagerW (Opnum: 0xf) calls to the svcctl RPC interface.

Figure 10: Advanced Search logs showing a user’s device contacting the Endpoint Mapper and Service Control Manager (SCM) services on internal systems.

After connecting to the SCM on internal systems, infected devices were seen using SMB to distribute files with ‘.dll’ and ‘.dll.cfg’ extensions to SMB shares. These uploads were followed by CreateWowService (Opnum: 0x3c) calls to the svcctl interface, likely intended to execute the uploaded payloads. The naming conventions of the uploaded files indicate that they were Qakbot payloads.

Figure 11: Advanced Search logs showing a user’s device making Service Control DCE-RPC requests to internal systems after uploading ‘.dll’ and ‘.dll.cfg’ files to them over SMB.

Fortunately, none of the observed QakNote infections escalated further than this. If these infections had escalated, it is likely that they would have resulted in the widespread detonation of additional malicious payloads, such as ransomware.

Darktrace Coverage of QakNote Activity

Figure 1 shows the steps involved in the QakNote infections observed across Darktrace’s customer base. How far attackers got along this chain was in part determined by the following three factors:

Whether the targeted customer had Darktrace/Email
Whether the targeted customer had Darktrace RESPOND/Network
Whether the targeted customer acted in response to Darktrace DETECT/Network alerts

The presence of Darktrace/Email typically stopped QakNote infections from moving past the initial infection stage. The presence of RESPOND/Network significantly slowed down observed activity chains, however, infections left unattended and not mitigated by the security teams were able to progress further along the attack chain.

Darktrace observed varying properties in the QakNote emails detected across the customer base. OneNote attachments were typically detected as either ‘application/octet-stream’ files or as ‘application/x-tar’ files. In some cases, the weaponized OneNote attachment embedded a malicious file, whereas in other cases, the OneNote file embedded a malicious link (typically a ‘.png’ or ‘.gif’ link) instead. In all cases Darktrace observed, QakNote emails used subject lines starting with ‘RE’ or ‘FW’ to manipulating their recipients into thinking that such emails were part of an existing email chain/thread. In some cases, emails impersonated users known to their recipients by including the names of such users in their header-from personal names. In many cases, QakNote emails appear to have originated from likely hijacked email accounts. These are highly successful methods of social engineering often employed by threat actors to exploit a user’s trust in known contacts or services, convincing them to open malicious emails and making it harder for security tools to detect.

The fact that observed QakNote emails used the fake-reply method, were sent from unknown email accounts, and contained attachments with unusual MIME types, caused such emails to breach the following Darktrace/Email models:

Association / Unknown Sender
Attachment / Unknown File
Attachment / Unsolicited Attachment
Attachment / Highly Unusual Mime
Attachment / Unsolicited Anomalous Mime
Attachment / Unusual Mime for Organisation
Unusual / Fake Reply
Unusual / Unusual Header TLD
Unusual / Fake Reply + Unknown Sender
Unusual / Unusual Connection from Unknown
Unusual / Off Topic

QakNote emails impersonating known users also breached the following DETECT & RESPOND/Email models:

Unusual / Unrelated Personal Name Address
Spoof / Basic Known Entity Similarities
Spoof / Internal User Similarities
Spoof / External User Similarities
Spoof / Internal User Similarities + Unrelated Personal Name Address
Spoof / External User Similarities + Unrelated Personal Name Address
Spoof / Internal User Similarities + Unknown File
Spoof / External User Similarities + Fake Reply
Spoof / Possible User Spoof from New Address - Enhanced Internal Similarities
Spoof / Whale

The actions taken by Darktrace on the observed emails is ultimately determined by Darktrace/Email models are breached. Those emails which did not breach Spoofing models (due to lack of impersonation indicators) received the ‘Convert Attachment’ action. This action converts suspicious attachments into neutralized PDFs, in this case successfully unweaponizing the malicious OneNote attachments. QakNote emails which did breach Spoofing models (due to the presence of impersonation indicators) received the strongest possible action, ‘Hold Message’. This action prevents suspicious emails from reaching the recipients’ mailbox.

Figure 12: Email log showing a malicious OneNote email (without impersonation indicators) which received a 87% anomaly score, a ‘Move to junk’ action, and a ‘Convert attachment’ actions from Darktrace/Email.

Figure 13: Email log showing a malicious OneNote email (with impersonation indicators) which received an anomaly score of 100% and a ‘Hold message’ action from Darktrace/Email.

Figure 14: Email log showing a malicious OneNote email (with impersonation indicators) which received an anomaly score of 100% and a ‘Hold message’ action from Darktrace/Email.

If threat actors managed to get past the first stage of the QakNote kill chain, likely due to the absence of appropriate email security tools, the execution of the subsequent steps resulted in strong intervention from Darktrace/Network.

Interactions with malicious OneNote attachments caused their devices to fetch a Qakbot DLL from a remote server via HTTP GET requests with an empty Host header and either a cURL or PowerShell User-Agent header. These unusual HTTP behaviors caused the following Darktrace/Network models to breach:

Device / New User Agent
Device / New PowerShell User Agent
Device / New User Agent and New IP
Anomalous Connection / New User Agent to IP Without Hostname
Anomalous Connection / Powershell to Rare External
Anomalous File / Numeric File Download
Anomalous File / EXE from Rare External Location
Anomalous File / New User Agent Followed By Numeric File Download

For customers with RESPOND/Network active, these breaches resulted in the following autonomous actions:

Enforce group pattern of life for 30 minutes
Enforce group pattern of life for 2 hours
Block connections to relevant external endpoints over relevant ports for 2 hours
Block all outgoing traffic for 10 minutes

Figure 15: Event Log showing a user’s device receiving Darktrace RESPOND/Network actions after downloading a Qakbot DLL.

Figure 16: Event Log showing a user’s device receiving Darktrace RESPOND/Network actions after downloading a Qakbot DLL.

Successful, uninterrupted downloads of Qakbot DLLs resulted in connections to Qakbot C2 servers, and subsequently to Cobalt Strike and VNC C2 connections. These C2 activities resulted in breaches of the following DETECT/Network models:

Compromise / Suspicious TLS Beaconing To Rare External
Compromise / Large Number of Suspicious Successful Connections
Compromise / Large Number of Suspicious Failed Connections
Compromise / Sustained SSL or HTTP Increase
Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
Compromise / Beaconing Activity To External Rare
Compromise / Slow Beaconing Activity To External Rare
Anomalous Connection / Multiple Connections to New External TCP Port
Anomalous Connection / Multiple Failed Connections to Rare Endpoint
Device / Initial Breach Chain Compromise

For customers with RESPOND/Network active, these breaches caused RESPOND to autonomously perform the following actions:

Block connections to relevant external endpoints over relevant ports for 1 hour

Figure 17: Event Log showing a user’s device receiving RESPOND/Network actions after contacting the Qakbot C2 endpoint, Cobalt Strike C2 endpoint, bonsars[.]com.

In cases where C2 connections were allowed to continue, actors attempted to move laterally through usage of SMB and Service Control Manager. This lateral movement activity caused the following DETECT/Network models to breach:

Device / Possible SMB/NTLM Reconnaissance
Anomalous Connection / New or Uncommon Service Control

For customers with RESPOND/Network enabled, these breaches caused RESPOND to autonomously perform the following actions:

Block connections to relevant internal endpoints over port 445 for 1 hour

Figure 18: Event Log shows a user’s device receiving RESPOND/Network actions after contacting the Qakbot C2 endpoint, 5.75.205[.]43, and distributing ‘.dll’ and ‘.dll.cfg’ files internally.

The QakNote infections observed across Darktrace’s customer base involved several steps, each of which elicited alerts and autonomous preventative actions from Darktrace. By autonomously investigating the alerts from DETECT, Darktrace’s Cyber AI Analyst was able to connect the distinct steps of observed QakNote infections into single incidents. It then produced incident logs to present in-depth details of the activity it uncovered, provide full visibility for customer security teams.

Figure 19: AI Analyst incident entry showing the steps of a QakNote infection which AI Analyst connected following its autonomous investigations.

Conclusion

Faced with the emerging threat of QakNote infections, Darktrace demonstrated its ability to autonomously detect and respond to arising threats in a constantly evolving threat landscape. The attack chains which Darktrace observed across its customer base involved the delivery of Qakbot via malicious OneNote attachments, the usage of ports 65400 and 2222 for Qakbot C2 communication, the usage of Cobalt Strike Beacon and VNC for ‘hands-on-keyboard’ activity, and the usage of SMB and Service Control Manager for lateral movement.

Despite the novelty of the OneNote-based delivery method, Darktrace was able to identify QakNote infections across its customer base at various stages of the kill chain, using its autonomous anomaly-based detection to identify unusual activity or deviations from expected behavior. When active, Darktrace/Email neutralized malicious QakNote attachments sent to employees. In cases where Darktrace/Email was not active, Darktrace/Network detected and slowed down the unusual network activities which inevitably ensued from Qakbot infections. Ultimately, this intervention from Darktrace’s products prevented infections from leading to further harmful activity, such as data exfiltration and the detonation of ransomware.

Darktrace is able to offer customers an unparalleled level of network security by combining both Darktrace/Network and Darktrace/Email, safeguarding both their email and network environments. With its suite of products, including DETECT and RESPOND, Darktrace can autonomously uncover threats to customer networks and instantaneously intervene to prevent suspicious activity leading to damaging compromises.

Appendices

MITRE ATT&CK Mapping

Initial Access:

T1566.001 – Phishing: Spearphishing Attachment

Execution:

T1204.001 – User Execution: Malicious Link

T1204.002 – User Execution: Malicious File

T1569.002 – System Services: Service Execution

Lateral Movement:

T1021.002 – Remote Services: SMB/Windows Admin Shares

Command and Control:‍

T1573.002 – Encrypted Channel : Asymmetric Cryptography

T1571 – Non-Standard Port

T1105 – Ingress Tool Transfer

T1095 – Non-Application Layer Protocol

T1219 – Remote Access Software

List of IOCs

IP Addresses and/or Domain Names:

- 103.214.71[.]45 - Qakbot download infrastructure

- 141.164.35[.]94 - Qakbot download infrastructure

- 95.179.215[.]225 - Qakbot download infrastructure

- 128.254.207[.]55 - Qakbot download infrastructure

- 141.164.35[.]94 - Qakbot download infrastructure

- 172.96.137[.]149 - Qakbot download infrastructure

- 185.231.205[.]246 - Qakbot download infrastructure

- 216.128.146[.]67 - Qakbot download infrastructure

- 45.155.37[.]170 - Qakbot download infrastructure

- 85.239.41[.]55 - Qakbot download infrastructure

- 45.67.35[.]108 - Qakbot download infrastructure

- 77.83.199[.]12 - Qakbot download infrastructure

- 45.77.63[.]210 - Qakbot download infrastructure

- 198.44.140[.]78 - Qakbot download infrastructure

- 47.32.78[.]150 - Qakbot C2 infrastructure

- 197.204.13[.]52 - Qakbot C2 infrastructure

- 68.108.122[.]180 - Qakbot C2 infrastructure

- 2.50.48[.]213 - Qakbot C2 infrastructure

- 66.180.227[.]60 - Qakbot C2 infrastructure

- 190.206.75[.]58 - Qakbot C2 infrastructure

- 109.150.179[.]236 - Qakbot C2 infrastructure

- 86.202.48[.]142 - Qakbot C2 infrastructure

- 143.159.167[.]159 - Qakbot C2 infrastructure

- 5.75.205[.]43 - Qakbot C2 infrastructure

- 184.176.35[.]223 - Qakbot C2 infrastructure

- 208.187.122[.]74 - Qakbot C2 infrastructure

- 23.111.114[.]52 - Qakbot C2 infrastructure

- 74.12.134[.]53 – Qakbot C2 infrastructure

- bonsars[.]com • 194.165.16[.]56 - Cobalt Strike C2 infrastructure

- 78.31.67[.]7 - VNC C2 infrastructure

Target URIs of GET Requests for Qakbot DLLs:

- /70802.dat

- /51881.dat

- /12427.dat

- /70136.dat

- /35768.dat

- /41981.dat

- /30622.dat

- /72286.dat

- /46557.dat

- /33006.dat

- /300332.dat

- /703558.dat

- /760433.dat

- /210/184/187737.dat

- /469/387/553748.dat

- /282/535806.dat

User-Agent Headers of GET Requests for Qakbot DLLs:

- curl/7.83.1

- curl/7.55.1

- Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.19041.2364

- Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.17763.3770

- Mozilla/5.0 (Windows NT; Windows NT 10.0; en-GB) WindowsPowerShell/5.1.19041.2364

SHA256 Hashes of Downloaded Qakbot DLLs:

- 83e9bdce1276d2701ff23b1b3ac7d61afc97937d6392ed6b648b4929dd4b1452

- ca95a5dcd0194e9189b1451fa444f106cbabef3558424d9935262368dba5f2c6

- fa067ff1116b4c8611eae9ed4d59a19d904a8d3c530b866c680a7efeca83eb3d

- e6853589e42e1ab74548b5445b90a5a21ff0d7f8f4a23730cffe285e2d074d9e

- d864d93b8fd4c5e7fb136224460c7b98f99369fc9418bae57de466d419abeaf6

- c103c24ccb1ff18cd5763a3bb757ea2779a175a045e96acbb8d4c19cc7d84bea

Names of Internally Distributed Qakbot DLLs:

- rpwpmgycyzghm.dll

- rpwpmgycyzghm.dll.cfg

- guapnluunsub.dll

- guapnluunsub.dll.cfg

- rskgvwfaqxzz.dll

- rskgvwfaqxzz.dll.cfg

- hkfjhcwukhsy.dll

- hkfjhcwukhsy.dll.cfg

- uqailliqbplm.dll

- uqailliqbplm.dll.cfg

- ghmaorgvuzfos.dll

- ghmaorgvuzfos.dll.cfg

Links Found Within Neutralized QakNote Email Attachments:

- hxxps://khatriassociates[.]com/MBt/3.gif

- hxxps://spincotech[.]com/8CoBExd/3.gif

- hxxps://minaato[.]com/tWZVw/3.gif

- hxxps://famille2point0[.]com/oghHO/01.png

- hxxps://sahifatinews[.]com/jZbaw/01.png

- hxxp://87.236.146[.]112/62778.dat

- hxxp://87.236.146[.]112/59076.dat

- hxxp://185.231.205[.]246/73342.dat

References

[1] https://techcommunity.microsoft.com/t5/excel-blog/excel-4-0-xlm-macros-now-restricted-by-default-for-customer/ba-p/3057905

[2] https://techcommunity.microsoft.com/t5/microsoft-365-blog/helping-users-stay-safe-blocking-internet-macros-by-default-in/ba-p/3071805

[3] https://learn.microsoft.com/en-us/deployoffice/security/internet-macros-blocked

[4] https://www.cyfirma.com/outofband/html-smuggling-a-stealthier-approach-to-deliver-malware/

[5] https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/html-smuggling-the-hidden-threat-in-your-inbox/

[6] https://twitter.com/nao_sec/status/1530196847679401984

[7] https://www.fortiguard.com/threat-signal-report/4616/qakbot-delivered-through-cve-2022-30190-follina

[8] https://isc.sans.edu/diary/rss/28728

[9] https://darktrace.com/blog/qakbot-resurgence-evolving-along-with-the-emerging-threat-landscape

[10] https://www.trustwave.com/en-us/resources/blogs/spiderlabs-blog/trojanized-onenote-document-leads-to-formbook-malware/

[11] https://www.proofpoint.com/uk/blog/threat-insight/onenote-documents-increasingly-used-to-deliver-malware

[12] https://www.malwarebytes.com/blog/threat-intelligence/2023/03/emotet-onenote

[13] https://blog.cyble.com/2023/02/01/qakbots-evolution-continues-with-new-strategies/

[14] https://news.sophos.com/en-us/2023/02/06/qakbot-onenote-attacks/

[15] https://isc.sans.edu/diary/rss/29210

[16] https://unit42.paloaltonetworks.com/feb-wireshark-quiz-answers/

Written by

Sam Lister

Specialist Security Researcher

Written by

Connor Mooney

SOC Analyst

Inside the SOC

Written by

Sam Lister

Specialist Security Researcher

Written by

Connor Mooney

SOC Analyst

Malware Research Engineer

•

June 11, 2026

Nathaniel Jones

VP, Security & AI Strategy, Field CISO

Watch the NIS2 Webinar

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AI

June 11, 2026

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

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

Blog

OT

June 11, 2026

Protecting Stadiums & Events with AI

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).

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Figure 1: The interconnected IT/OT components of a stadium

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.

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‍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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About the author

Karim Benslimane

VP, Field CISO

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