Darktrace Defends McLaren Racing From Supply Chain Attacks
15
Nov 2021
McLaren Racing chose Darktrace's self-learning AI to fight off supply chain attacks. Learn how Darktrace safeguards their organization with elite cybersecurity.
McLaren Racing has a track record of forming valuable and innovative partnerships. Without these partnerships and the web of organisations that make up our supply chain, it’s unlikely we could be where we are today.
Figure 1: The origins of the different components of McLaren’s 2021 car
Each component of the McLaren Formula 1 car – engine, tyres, brakes, suspension – has a long and complicated backstory, from the R&D labs where it was conceived, to the factory floor on which it was manufactured, to transport and logistics getting it to where it needs to be.
Looking at the entire organisation, the situation is even more complex. IT hardware and software, telemetry, and data analysis tools, each represent a critical component to McLaren Racing’s ecosystem. Without it, we couldn’t function at the top of our game.
But from a security perspective, each of these represent a potential chink in the team’s defensive armour, against a backdrop of a cyber-threat landscape which becomes more hostile every year. As we’ve seen this year from the likes of the SolarWinds hack and the Kaseya software exploit, attackers are waking up to the fact that the supply chain represents a significant opportunity.
A single supplier may represent a point of entry into thousands of organisations. For cyber-criminals, this means one successful compromise can result in more access, more data, and ultimately greater profit.
McLaren Racing is all too aware of recent shifts in the cyber security landscape. A successful cyber-attack on our organisation could have implications on race-day performance, as well as our wider reputation. Last year, we brought in a new line of defence with Darktrace’s Self-Learning AI technology, that learns our business from the ground up, and interrupts subtle and fast-moving cyber-threats wherever they emerge – including from our supply chain.
Threat find: Attacking through the inbox
In this attack, 12 employees were targeted in a systematic phishing attack, receiving an email from a long-established team supplier, notifying them that a voicemail had been left for them.
Figure 2: An extract of the phishing email coaxing the recipient to click
The link to play the voicemail led to a legitimate-looking voicemail service site.
When following the link to access the message, the site requested Office 365 credentials to authenticate the user, designed to harvest the McLaren Racing credentials that could be used to access our environment.
Figure 3: The fake login page
Of the 12 recipients, several key people within our team were targeted, including technical directors and purchase ledgers. The attackers behind this phishing campaign no doubt hand-picked these individuals both due to their authorization powers and the likelihood their accounts had access to sensitive data.
Had these accounts been compromised, the attackers would have had access to some of the highest sensitivity of intellectual property, finance information and executive level strategy within racing.
Darktrace’s email security technology, Antigena Email, assessed the content of these emails as they were delivered, and identified several unusual indicators of attack. While it recognised that the account was one familiar to McLaren, it compared this attack with previous emails sent from the supplier and recognised several risk indicators. Darktrace Antigena autonomously took the decision to hold the email from being delivered to users’ mailboxes.
Figure 4: Antigena Email reveals in plain language why the email was suspicious and the action it took
Legitimate communication between our team and the supplier was still flowing uninterrupted, as Darktrace Antigena was assessing each email’s indicators for risk. The following day, the supplier’s account manager in our team received an email from the supplier in question, informing them that one of their accounts had been compromised and was used to send phishing emails to some of their customers. This confirmed that Antigena Email had correctly identified the email as malicious.
Traditional email security tools rely on historical attack data to determine friend from foe, but this is only effective in cases where an email domain or a malicious URL has been previously encountered. In this case, traditional filtering allowed the email through. Only by having Darktrace’s understanding of ‘self’ and Autonomous Response was McLaren able to avoid exposure to risk on this occasion.
This is reflective of a wider pattern noticed by the security team. Darktrace determines that around 40% of emails going through Antigena Email would have been detected by our other security tools, suggesting that Darktrace is detecting an extra 60% of malicious emails and taking action to ensure we are protected 24/7.
This was just one example of an attempted attack on McLaren through the inbox. On another occasion, Antigena Email identified an email that was attempting to impersonate a sponsor. The email in question was requesting that a senior McLaren Racing figure reset their password and contained a suspicious link that led to a credential harvester. Again, Antigena took action on the emails at time of delivery, and our internal cyber team never had to respond to what could have been a serious incident. It’s through Darktrace taking autonomous action like this on a daily basis that we are able to focus our time on higher-value, strategic work, driving success for the wider team.
Why the supply chain demands a new approach to security
In today’s digitised world, it is impossible to operate as a fluid, dynamic organisation without interacting with suppliers and partners at every digital layer: from email, to file sharing services and technology partners delivered through the cloud. As McLaren grows and works with leading global organisations to improve its performance, its supply chain ecosystem will only get broader.
Attackers are targeting suppliers because they represent a single key that opens potentially dozens or even hundreds of locks, and email is just one avenue of attack. By partnering with Darktrace, McLaren experiences the value of self-learning protection on a daily basis, across its email systems, cloud services, and corporate network.
Whether it’s email or some other form of communication from a supplier, you cannot assume you know who’s on the other side of the keyboard. This is what so many existing security defences do – with static rules and signatures unable to truly tell friend from foe and reveal account takeovers and compromised systems. Modern organisations need a solution that is able to identify potentially malicious activity from suppliers by analysing a broad range of indicators and revealing subtle deviations that indicate threat, and this is where Self-Learning AI shines.
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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.
Darktrace's Detection of State-Linked ShadowPad Malware
An integral part of cybersecurity is anomaly detection, which involves identifying unusual patterns or behaviors in network traffic that could indicate malicious activity, such as a cyber-based intrusion. However, attribution remains one of the ever present challenges in cybersecurity. Attribution involves the process of accurately identifying and tracing the source to a specific threat actor(s).
Given the complexity of digital networks and the sophistication of attackers who often use proxies or other methods to disguise their origin, pinpointing the exact source of a cyberattack is an arduous task. Threat actors can use proxy servers, botnets, sophisticated techniques, false flags, etc. Darktrace’s strategy is rooted in the belief that identifying behavioral anomalies is crucial for identifying both known and novel threat actor campaigns.
The ShadowPad cluster
Between July 2024 and November 2024, Darktrace observed a cluster of activity threads sharing notable similarities. The threads began with a malicious actor using compromised user credentials to log in to the target organization's Check Point Remote Access virtual private network (VPN) from an attacker-controlled, remote device named 'DESKTOP-O82ILGG'. In one case, the IP from which the initial login was carried out was observed to be the ExpressVPN IP address, 194.5.83[.]25. After logging in, the actor gained access to service account credentials, likely via exploitation of an information disclosure vulnerability affecting Check Point Security Gateway devices. Recent reporting suggests this could represent exploitation of CVE-2024-24919 [27,28]. The actor then used these compromised service account credentials to move laterally over RDP and SMB, with files related to the modular backdoor, ShadowPad, being delivered to the ‘C:\PerfLogs\’ directory of targeted internal systems. ShadowPad was seen communicating with its command-and-control (C2) infrastructure, 158.247.199[.]185 (dscriy.chtq[.]net), via both HTTPS traffic and DNS tunneling, with subdomains of the domain ‘cybaq.chtq[.]net’ being used in the compromised devices’ TXT DNS queries.
Figure 1: Darktrace’s Advanced Search data showing the VPN-connected device initiating RDP connections to a domain controller (DC). The device subsequently distributes likely ShadowPad-related payloads and makes DRSGetNCChanges requests to a second DC.
Figure 2: Event Log data showing a DC making DNS queries for subdomains of ‘cbaq.chtq[.]net’ to 158.247.199[.]185 after receiving SMB and RDP connections from the VPN-connected device, DESKTOP-O82ILGG.
Additional cases of ShadowPad were observed across Darktrace’s customer base in 2024. In some cases, common C2 infrastructure with the cluster discussed above was observed, with dscriy.chtq[.]net and cybaq.chtq[.]net both involved; however, no other common features were identified. These ShadowPad infections were observed between April and November 2024, with customers across multiple regions and sectors affected. Darktrace’s observations align with multiple other public reports that fit the timeframe of this campaign.
Darktrace has also observed other cases of ShadowPad without common infrastructure since September 2024, suggesting the use of this tool by additional threat actors.
The data theft thread
One of the Darktrace customers impacted by the ShadowPad cluster highlighted above was a European manufacturer. A distinct thread of activity occurred within this organization’s network several months after the ShadowPad intrusion, in October 2024.
The thread involved the internal distribution of highly masqueraded executable files via Sever Message Block (SMB) and WMI (Windows Management Instrumentation), the targeted collection of sensitive information from an internal server, and the exfiltration of collected information to a web of likely compromised sites. This observed thread of activity, therefore, consisted of three phrases: lateral movement, collection, and exfiltration.
The lateral movement phase began when an internal user device used an administrative credential to distribute files named ‘ProgramData\Oracle\java.log’ and 'ProgramData\Oracle\duxwfnfo' to the c$ share on another internal system.
Figure 3: Darktrace model alert highlighting an SMB write of a file named ‘ProgramData\Oracle\java.log’ to the c$ share on another device.
Over the next few days, Darktrace detected several other internal systems using administrative credentials to upload files with the following names to the c$ share on internal systems:
ProgramData\Adobe\ARM\webservices.dll
ProgramData\Adobe\ARM\wksprt.exe
ProgramData\Oracle\Java\wksprt.exe
ProgramData\Oracle\Java\webservices.dll
ProgramData\Microsoft\DRM\wksprt.exe
ProgramData\Microsoft\DRM\webservices.dll
ProgramData\Abletech\Client\webservices.dll
ProgramData\Abletech\Client\client.exe
ProgramData\Adobe\ARM\rzrmxrwfvp
ProgramData\3Dconnexion\3DxWare\3DxWare.exe
ProgramData\3Dconnexion\3DxWare\webservices.dll
ProgramData\IDMComp\UltraCompare\updater.exe
ProgramData\IDMComp\UltraCompare\webservices.dll
ProgramData\IDMComp\UltraCompare\imtrqjsaqmm
Figure 4: Cyber AI Analyst highlighting an SMB write of a file named ‘ProgramData\Adobe\ARM\webservices.dll’ to the c$ share on an internal system.
The threat actor appears to have abused the Microsoft RPC (MS-RPC) service, WMI, to execute distributed payloads, as evidenced by the ExecMethod requests to the IWbemServices RPC interface which immediately followed devices’ SMB uploads.
Figure 5: Cyber AI Analyst data highlighting a thread of activity starting with an SMB data upload followed by ExecMethod requests.
Several of the devices involved in these lateral movement activities, both on the source and destination side, were subsequently seen using administrative credentials to download tens of GBs of sensitive data over SMB from a specially selected server. The data gathering stage of the threat sequence indicates that the threat actor had a comprehensive understanding of the organization’s system architecture and had precise objectives for the information they sought to extract.
Immediately after collecting data from the targeted server, devices went on to exfiltrate stolen data to multiple sites. Several other likely compromised sites appear to have been used as general C2 infrastructure for this intrusion activity. The sites used by the threat actor for C2 and data exfiltration purport to be sites for companies offering a variety of service, ranging from consultancy to web design.
Figure 6: Screenshotof one of the likely compromised sites used in the intrusion.
At least 16 sites were identified as being likely data exfiltration or C2 sites used by this threat actor in their operation against this organization. The fact that the actor had such a wide web of compromised sites at their disposal suggests that they were well-resourced and highly prepared.
Figure 7: Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.
Figure 8: Darktrace model alert highlighting an internal device downloading nearly 1 GB of data from an internal system just before uploading a similar volume of data to another suspicious endpoint, www.tunemmuhendislik[.]com
Cyber AI Analyst spotlight
Figure 9: Cyber AI Analyst identifying and piecing together the various steps of a ShadowPad intrusion.
Figure 10: Cyber AI Analyst Incident identifying and piecing together the various steps of the data theft activity.
As shown in the above figures, Cyber AI Analyst’s ability to thread together the different steps of these attack chains are worth highlighting.
In the ShadowPad attack chains, Cyber AI Analyst was able to identify SMB writes from the VPN subnet to the DC, and the C2 connections from the DC. It was also able to weave together this activity into a single thread representing the attacker’s progression.
Similarly, in the data exfiltration attack chain, Cyber AI Analyst identified and connected multiple types of lateral movement over SMB and WMI and external C2 communication to various external endpoints, linking them in a single, connected incident.
These Cyber AI Analyst actions enabled a quicker understanding of the threat actor sequence of events and, in some cases, faster containment.
Attribution puzzle
Publicly shared research into ShadowPad indicates that it is predominantly used as a backdoor in People’s Republic of China (PRC)-sponsored espionage operations [5][6][7][8][9][10]. Most publicly reported intrusions involving ShadowPad are attributed to the China-based threat actor, APT41 [11][12]. Furthermore, Google Threat Intelligence Group (GTIG) recently shared their assessment that ShadowPad usage is restricted to clusters associated with APT41 [13]. Interestingly, however, there have also been public reports of ShadowPad usage in unattributed intrusions [5].
The data theft activity that later occurred in the same Darktrace customer network as one of these ShadowPad compromises appeared to be the targeted collection and exfiltration of sensitive data. Such an objective indicates the activity may have been part of a state-sponsored operation. The tactics, techniques, and procedures (TTPs), artifacts, and C2 infrastructure observed in the data theft thread appear to resemble activity seen in previous Democratic People’s Republic of Korea (DPRK)-linked intrusion activities [15] [16] [17] [18] [19].
The distribution of payloads to the following directory locations appears to be a relatively common behavior in DPRK-sponsored intrusions.
Observed examples:
C:\ProgramData\Oracle\Java\
C:\ProgramData\Adobe\ARM\
C:\ProgramData\Microsoft\DRM\
C:\ProgramData\Abletech\Client\
C:\ProgramData\IDMComp\UltraCompare\
C:\ProgramData\3Dconnexion\3DxWare\
Additionally, the likely compromised websites observed in the data theft thread, along with some of the target URI patterns seen in the C2 communications to these sites, resemble those seen in previously reported DPRK-linked intrusion activities.
No clear evidence was found to link the ShadowPad compromise to the subsequent data theft activity that was observed on the network of the manufacturing customer. It should be noted, however, that no clear signs of initial access were found for the data theft thread – this could suggest the ShadowPad intrusion itself represents the initial point of entry that ultimately led to data exfiltration.
Motivation-wise, it seems plausible for the data theft thread to have been part of a DPRK-sponsored operation. DPRK is known to pursue targets that could potentially fulfil its national security goals and had been publicly reported as being active in months prior to this intrusion [21]. Furthermore, the timing of the data theft aligns with the ratification of the mutual defense treaty between DPRK and Russia and the subsequent accused activities [20].
Darktrace assesses with medium confidence that a nation-state, likely DPRK, was responsible, based on our investigation, the threat actor applied resources, patience, obfuscation, and evasiveness combined with external reporting, collaboration with the cyber community, assessing the attacker’s motivation and world geopolitical timeline, and undisclosed intelligence.
Conclusion
When state-linked cyber activity occurs within an organization’s environment, previously unseen C2 infrastructure and advanced evasion techniques will likely be used. State-linked cyber actors, through their resources and patience, are able to bypass most detection methods, leaving anomaly-based methods as a last line of defense.
Two threads of activity were observed within Darktrace’s customer base over the last year: The first operation involved the abuse of Check Point VPN credentials to log in remotely to organizations’ networks, followed by the distribution of ShadowPad to an internal domain controller. The second operation involved highly targeted data exfiltration from the network of one of the customers impacted by the previously mentioned ShadowPad activity.
Despite definitive attribution remaining unresolved, both the ShadowPad and data exfiltration activities were detected by Darktrace’s Self-Learning AI, with Cyber AI Analyst playing a significant role in identifying and piecing together the various steps of the intrusion activities.
Credit to Sam Lister (R&D Detection Analyst), Emma Foulger (Principal Cyber Analyst), Nathaniel Jones (VP), and the Darktrace Threat Research team.
Appendices
Darktrace / NETWORK model alerts
User / New Admin Credentials on Client
Anomalous Connection / Unusual Admin SMB Session
Compliance / SMB Drive Write
Device / Anomalous SMB Followed By Multiple Model Breaches
Survey findings: AI Cyber Threats are a Reality, the People are Acting Now
Artificial intelligence is changing the cybersecurity field as fast as any other, both on the offensive and defensive side. We surveyed over 1,500 cybersecurity professionals from around the world to uncover their attitudes, understanding, and priorities when it comes to AI cybersecurity in 2025. Our full report, unearthing some telling trends, is out now.
Nearly 74% of participants say AI-powered threats are a major challenge for their organization and 90% expect these threats to have a significant impact over the next one to two years, a slight increase from last year. These statistics highlight that AI is not just an emerging risk but a present and evolving one.
As attackers harness AI to automate and scale their operations, security teams must adapt just as quickly. Organizations that fail to prioritize AI-specific security measures risk falling behind, making proactive defense strategies more critical than ever.
Some of the most pressing AI-driven cyber threats include:
AI-powered social engineering: Attackers are leveraging AI to craft highly personalized and convincing phishing emails, making them harder to detect and more likely to bypass traditional defenses.
More advanced attacks at speed and scale: AI lowers the barrier for less skilled threat actors, allowing them to launch sophisticated attacks with minimal effort.
Attacks targeting AI systems: Cybercriminals are increasingly going after AI itself, compromising machine learning models, tampering with training data, and exploiting vulnerabilities in AI-driven applications and APIs.
Safe and secure use of AI
AI is having an effect on the cyber-threat landscape, but it also is starting to impact every aspect of a business – from marketing to HR to operations. The accessibility of AI tools for employees improves workflows, but also poses risks like data privacy violations, shadow AI, and violation of industry regulations.
How are security practitioners accommodating for this uptick in AI use across business?
Among survey participants 45% of security practitioners say they had already established a policy on the safe and secure use of AI and around 50% are in discussions to do so.
While almost all participants acknowledge that this is a topic that needs to be addressed, the gap between discussion and execution could underscore a need for greater insight, stronger leadership commitment, and adaptable security frameworks to keep pace with AI advancements in the workplace. The most popular actions taken are:
Implemented security controls to prevent unwanted exposure of corporate data when using AI technology (67%)
Implemented security controls to protect against other threats/risks associated with using AI technology (62%)
This year specifically, we see further action being taken with the implementation of security controls, training, and oversight.
For a more detailed breakdown that includes results based on industry and organizational size, download the full report here.
AI threats are rising, but security teams still face major challenges
78% of CISOs say AI-powered cyber-threats are already having a significant impact on their organization, a 5% increase from last year.
While cyber professionals feel more prepared for AI powered threats than they did 12 months ago, 45% still say their organization is not adequately prepared—down from 60% last year.
Despite this optimism, key challenges remain, including:
A shortage of personnel to manage tools and alerts
Gaps in knowledge and skills related to AI-driven countermeasures
Confidence in traditional security tools vs. new AI based tools
This year, 73% of survey participants expressed confidence in their security team’s proficiency in using AI within their tool stack, marking an increase from the previous year.
However, only 50% of participants have confidence in traditional cybersecurity tools to detect and block AI-powered threats. In contrast, 75% of participants are confident in AI-powered security solutions for detecting and blocking such threats and attacks.
As leading organizations continue to implement and optimize their use of AI, they are incorporating it into an increasing number of workflows. This growing familiarity with AI is likely to boost the confidence levels of practitioners even further.
The data indicates a clear trend towards greater reliance on AI-powered security solutions over traditional tools. As organizations become more adept at integrating AI into their operations, their confidence in these advanced technologies grows.
This shift underscores the importance of staying current with AI advancements and ensuring that security teams are well-trained in utilizing these tools effectively. The increasing confidence in AI-driven solutions reflects their potential to enhance cybersecurity measures and better protect against sophisticated threats.
The full report for Darktrace’s State of AI Cybersecurity is out now. Download the paper to dig deeper into these trends, and see how results differ by industry, region, organization size, and job title.
![Event Log data showing a DC making DNS queries for subdomains of ‘cbaq.chtq[.]net’ to 158.247.199[.]185 after receiving SMB and RDP connections from the VPN-connected device, DESKTOP-O82ILGG.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dbda2020b168dc1d83c2_Screenshot%202025-03-12%20at%2012.07.41%E2%80%AFPM.png)
![Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dd09fe9cf54a67c48f13_Screenshot%202025-03-12%20at%2012.14.04%E2%80%AFPM.png)
![Darktrace model alert highlighting an internal device downloading nearly 1 GB of data from an internal system just before uploading a similar volume of data to another suspicious endpoint, www.tunemmuhendislik[.]com](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dd45f3743ebe06d81d89_Screenshot%202025-03-12%20at%2012.14.46%E2%80%AFPM.png)
