How Autonomous Cyber AI Scaled to Protect Arrow McLaren SP
25
May 2021
25
May 2021
Darktrace's Cyber AI has seamlessly scaled, extended, and adapted to protect Arrow McLaren's Formula 1 and IndyCar teams from machine-speed cyberattacks.
It’s been decades since a racing team has had the ambition to compete at the highest level simultaneously in Formula 1 and in the NTT INDYCAR Series. And why would they? With hectic schedules, tight timelines, and the finest margins between victory and defeat, one series alone may seem a daunting enough feat. But to compete across both requires cross-continent collaboration, creativity, and innovation at every turn.
Rather than being daunted by the challenge, McLaren has embraced the unique advantages of racing in both series simultaneously. Arrow McLaren SP, in a strategic partnership with McLaren Racing, communicates on a daily basis with the McLaren Technology Centre in the UK, gathering and sharing data including car telemetry, on-board video and audio files, timing and scoring information, car set-ups, and reporting.
The security of this critical and highly sensitive data is paramount to the performance of AMSP, and after the success of Darktrace’s AI in protecting the cloud, email, and network environments of McLaren F1, AMSP recently announced they would be seamlessly extending the coverage to the US.
How adaptive, scalable AI drives success
Darktrace’s autonomous Cyber AI has protected the McLaren F1 team since early 2020, shielding their workforce and critical systems during a time of fundamental digital change, with conditions arising from COVID-19 forcing many employees to work from home. During this time the organization had a heightened reliance on cloud collaboration platforms, including video conferencing and file sharing.
Cyber AI technology adapted to these unforeseen changes, protecting the McLaren F1 team from novel and advanced attacks targeting their dynamic workforce. While traditional tools bound by set rules and playbooks had to be reconfigured, a self-learning approach allowed continuous protection of McLaren’s email systems, cloud services, and network traffic, with little maintenance required from busy human teams.
Now, the technology is being put to the test again. McLaren has extended Darktrace’s AI technology to protect the large volumes of sensitive data that travels back and forth between Arrow McLaren SP and the MTC.
Responding to machine-speed ransomware with Autonomous Response
As a wave of ransomware attacks brings fresh concerns to the cyber security industry, AMSP is turning to a technology fundamental to stopping these machine-speed attacks. With Autonomous Response, Darktrace not only detects emerging threats, but responds in real time, stopping attackers in their tracks without human teams having to lift a finger.
The response is surgical and proportionate: only the malicious activity is contained, whilst normal operations are allowed to continue. This will be a crucial capability for the AMSP team, as any unnecessary downtime severely undermines their ability to get access to the right data at the right time – ultimately having an impact on performance on race day.
With Darktrace’s autonomous Cyber AI protecting both its F1 and INDYCAR teams, McLaren’s human IT resources are augmented with real-time protection and Autonomous Response working across different time zones and providing that 24/7 overwatch they need.
Indianapolis 500: The toughest test yet
The month of May is a busy and critical period of the season for AMSP, with three races culminating in the 105th Running of the Indianapolis 500, which will be held in front of a reduced crowd of 135,000 spectators. The IT team has been busy preparing a temporary trackside data centre for this event, and the sheer volume of data circulating between that and the MTC is ramping up.
All of this data must be gathered, organized, and transferred securely back and forth between the two hubs. The speed of that transfer is absolutely vital, as speed of analysis and real-time decision-making is critical to race performance. AMSP’s engineering team and McLaren’s engineering team operate as if they’re sitting next to each other, despite being thousands of miles away.
Moreover, some data files can be extremely large, and reliable connectivity is key in ensuring that all files, no matter the size, can be transferred and downloaded as quickly as possible. Lack or loss of any data gathered trackside would prevent AMSP’s abilities to accurately recap on-track sessions.
This clearly represents an incredibly busy time for the security personnel on the ground both at Indianapolis and in the UK. Leaning on AI to facilitate the secure and reliable movement of highly sensitive data empowers AMSP’s IT team to stay proactive, rather than being reactive and playing catch up in the case of a security incident.
Facing the future with Cyber AI
AMSP is in a unique position in the INDYCAR paddock – no other team transmits so much data, so often or over so far of a distance. Darktrace’s Cyber AI technology is helping to protect AMSP’s at-track engineering crews, remote engineering teams, data transfer processes and cloud infrastructure, from an increasingly hostile cyber-threat landscape.
Relying on Darktrace to safeguard and protect its sensitive data and digital assets will become critical in securing AMSP’s overall approach to race weekend activation. As the collaboration between McLaren Racing and Arrow McLaren SP continues to drive success on the track, the targeted actions of Darktrace’s Autonomous Response capability ensures both sides of the technical partnership stay protected across different time zones, around the clock, no matter what threat is waiting round the corner.
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.
AUTHOR
ABOUT ThE AUTHOR
Justin Fier
SVP, Red Team Operations
Justin is one of the US’s leading cyber intelligence experts, and holds the position of SVP, Red Team Operations at Darktrace. His insights on cyber security and artificial intelligence have been widely reported in leading media outlets, including the Wall Street Journal, CNN, The Washington Post, and VICELAND. With over 10 years’ experience in cyber defense, Justin has supported various elements in the US intelligence community, holding mission-critical security roles with Lockheed Martin, Northrop Grumman Mission Systems and Abraxas. Justin is also a highly-skilled technical specialist, and works with Darktrace’s strategic global customers on threat analysis, defensive cyber operations, protecting IoT, and machine learning.
Nick Snyder
Performance Director, Arrow McLaren SP
Nick Snyder has been with Arrow McLaren SP for more than 10 years and is an essential part of AMSP’s competition and trackside engineering teams. Beginning in 2011, Nick has worked his way up the ladder at Arrow McLaren SP, assuming his current role as Performance Director in November 2019. In his role, Nick has oversight to AMSP’s engineering group and the team’s entire digital footprint at each INDYCAR race event.
Disarming the WarmCookie Backdoor: Darktrace’s Oven-Ready Solution
26
Jul 2024
What is WarmCookie malware?
WarmCookie, also known as BadSpace [2], is a two-stage backdoor tool that provides functionality for threat actors to retrieve victim information and launch additional payloads. The malware is primarily distributed via phishing campaigns according to multiple open-source intelligence (OSINT) providers.
Backdoor malware: A backdoor tool is a piece of software used by attackers to gain and maintain unauthorized access to a system. It bypasses standard authentication and security mechanisms, allowing the attacker to control the system remotely.
Two-stage backdoor malware: This means the backdoor operates in two distinct phases:
1. Initial Stage: The first stage involves the initial infection and establishment of a foothold within the victim's system. This stage is often designed to be small and stealthy to avoid detection.
2. Secondary Stage: Once the initial stage has successfully compromised the system, it retrieves or activates the second stage payload. This stage provides more advanced functionalities for the attacker, such as extensive data exfiltration, deeper system control, or the deployment of additional malicious payloads.
How does WarmCookie malware work?
Reported attack patterns include emails attempting to impersonate recruitment firms such as PageGroup, Michael Page, and Hays. These emails likely represented social engineering tactics, with attackers attempting to manipulate jobseekers into engaging with the emails and following malicious links embedded within [3].
This backdoor tool also adopts stealth and evasion tactics to avoid the detection of traditional security tools. Reported evasion tactics included custom string decryption algorithms, as well as dynamic API loading to prevent researchers from analyzing and identifying the core functionalities of WarmCookie [1].
Before this backdoor makes an outbound network request, it is known to capture details from the target machine, which can be used for fingerprinting and identification [1], this includes:
- Computer name
- Username
- DNS domain of the machine
- Volume serial number
WarmCookie samples investigated by external researchers were observed communicating communicated over HTTP to a hardcoded IP address using a combination of RC4 and Base64 to protect its network traffic [1]. Ultimately, threat actors could use this backdoor to deploy further malicious payloads on targeted networks, such as ransomware.
Darktrace Coverage of WarmCookie
Between April and June 2024, Darktrace’s Threat Research team investigated suspicious activity across multiple customer networks indicating that threat actors were utilizing the WarmCookie backdoor tool. Observed cases across customer environments all included the download of unusual executable (.exe) files and suspicious outbound connectivity.
Affected devices were all observed making external HTTP requests to the German-based external IP, 185.49.69[.]41, and the URI, /data/2849d40ade47af8edfd4e08352dd2cc8.
Less than a minute later, the same device was observed making HTTP requests to the rare external IP address: 185.49.69[.]41, which had never previously been observed on the network, for the URI /data/b834116823f01aeceed215e592dfcba7. The device then proceeded to download masqueraded executable file from this endpoint. Darktrace recognized that these connections to an unknown endpoint, coupled with the download of a masqueraded file, likely represented malicious activity.
Following this download, the device began beaconing back to the same IP, 185.49.69[.]41, with a large number of external connections observed over port 80. This beaconing related behavior could further indicate malicious software communicating with command-and-control (C2) servers.
Darktrace’s model alert coverage included the following details:
[Model Alert: Device / Unusual BITS Activity]
- Associated device type: desktop
- Time of alert: 2024-04-23T14:10:23 UTC
- ASN: AS28753 Leaseweb Deutschland GmbH
- User agent: Microsoft BITS/7.8
[Model Alert: Anomalous File / EXE from Rare External Location]
[Model Alert: Compromise / Beaconing Activity To External Rare]
- Associated device type: desktop
- Time of alert: 2024-04-23T14:15:24 UTC
- Destination IP: 185.49.69[.]41
- Destination port: 80
- Protocol: TCP
- Application protocol: HTTP
- ASN: AS28753 Leaseweb Deutschland GmbH
- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705)
Between May 7 and June 4, Darktrace identified a wide range of suspicious external connectivity on another customer’s environment. Darktrace’s Threat Research team further investigated this activity and assessed it was likely indicative of WarmCookie exploitation on customer devices.
Similar to the initial use case, BITS activity was observed on affected devices, which is utilized to download WarmCookie [1]. This initial behavior was observed with the device after triggering the model: Device / Unusual BITS Activity on May 7.
Just moments later, the same device was observed making HTTP requests to the aforementioned German IP address, 185.49.69[.]41 using the same URI /data/2849d40ade47af8edfd4e08352dd2cc8, before downloading a suspicious executable file.
Just like the first use case, this device followed up this suspicious download with a series of beaconing connections to 185.49.69[.]41, again with a large number of connections via port 80.
Similar outgoing connections to 185.49.69[.]41 and model alerts were observed on additional devices during the same timeframe, indicating that numerous customer devices had been compromised.
Darktrace’s model alert coverage included the following details:
[Model Alert: Device / Unusual BITS Activity]
- Associated device type: desktop
- Time of alert: 2024-05-07T09:03:23 UTC
- ASN: AS28753 Leaseweb Deutschland GmbH
- User agent: Microsoft BITS/7.8
[Model Alert: Anomalous File / EXE from Rare External Location]
[Model Alert: Anomalous Connection / New User Agent to IP Without Hostname]
- Associated device type: desktop
- Time of alert: 2024-05-07T09:04:14 UTC
- Destination IP: 185.49.69[.]41
- Application protocol: HTTP
- URI: /data/2849d40ade47af8edfd4e08352dd2cc8
- User agent: Microsoft BITS/7.8
[Model Alert: Compromise / HTTP Beaconing to New Endpoint]
- Associated device type: desktop
- Time of alert: 2024-05-07T09:08:47 UTC
- Destination IP: 185.49.69[.]41
- Protocol: TCP
- Application protocol: HTTP
- ASN: AS28753 Leaseweb Deutschland GmbH
- URI: /
- User agent: Mozilla / 4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1;.NET CLR 1.0.3705) \
Figure 1: Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.
Figure 2: External Sites Summary verifying the geographical location of the external IP, 185.49.69[.]41’.
Fortunately, this particular customer was subscribed to Darktrace’s Proactive Threat Notification (PTN) service and the Darktrace Security Operation Center (SOC) promptly investigated the activity and alerted the customer. This allowed their security team to address the activity and begin their own remediation process.
In this instance, Darktrace’s Autonomous Response capability was configured in Human Confirmation mode, meaning any mitigative actions required manual application by the customer’s security team.
Despite this, Darktrace recommended two actions to contain the activity: blocking connections to the suspicious IP address 185.49.69[.]41 and any IP addresses ending with '69[.]41', as well as the ‘Enforce Pattern of Life’ action. By enforcing a pattern of life, Darktrace can restrict a device (or devices) to its learned behavior, allowing it to continue regular business activities uninterrupted while blocking any deviations from expected activity.
Figure 3: Actions suggested by Darktrace to contain the emerging activity, including blocking connections to the suspicious endpoint and restricting the device to its ‘pattern of life’.
Conclusion
Backdoor tools like WarmCookie enable threat actors to gather and leverage information from target systems to deploy additional malicious payloads, escalating their cyber attacks. Given that WarmCookie’s primary distribution method seems to be through phishing campaigns masquerading as trusted recruitments firms, it has the potential to affect a large number of organziations.
In the face of such threats, Darktrace’s behavioral analysis provides organizations with full visibility over anomalous activity on their digital estates, regardless of whether the threat bypasses by human security teams or email security tools. While threat actors seemingly managed to evade customers’ native email security and gain access to their networks in these cases, Darktrace identified the suspicious behavior associated with WarmCookie and swiftly notified customer security teams.
Had Darktrace’s Autonomous Response capability been fully enabled in these cases, it could have blocked any suspicious connections and subsequent activity in real-time, without the need of human intervention, effectively containing the attacks in the first instance.
Credit to Justin Torres, Cyber Security Analyst and Dylan Hinz, Senior Cyber Security Analyst
Appendices
Darktrace Model Detections
- Anomalous File / EXE from Rare External Location
- Anomalous File / Masqueraded File Transfer
- Compromise / Beacon to Young Endpoint
- Compromise / Beaconing Activity To External Rare
- Compromise / HTTP Beaconing to New Endpoint
- Compromise / HTTP Beaconing to Rare Destination
- Compromise / High Volume of Connections with Beacon Score
- Compromise / Large Number of Suspicious Successful Connections
- Compromise / Quick and Regular Windows HTTP Beaconing
- Compromise / SSL or HTTP Beacon
- Compromise / Slow Beaconing Activity To External Rare
- Compromise / Sustained SSL or HTTP Increase
- Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
- Anomalous Connection / Multiple Failed Connections to Rare Endpoint
- Anomalous Connection / New User Agent to IP Without Hostname
- Compromise / Sustained SSL or HTTP Increase
AI Analyst Incident Coverage:
- Unusual Repeated Connections
- Possible SSL Command and Control to Multiple Endpoints
The State of AI in Cybersecurity: Understanding AI Technologies
24
Jul 2024
About the State of AI Cybersecurity Report
Darktrace surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.
How familiar are security professionals with supervised machine learning
Just 31% of security professionals report that they are “very familiar” with supervised machine learning.
Many participants admitted unfamiliarity with various AI types. Less than one-third felt "very familiar" with the technologies surveyed: only 31% with supervised machine learning and 28% with natural language processing (NLP).
Most participants were "somewhat" familiar, ranging from 46% for supervised machine learning to 36% for generative adversarial networks (GANs). Executives and those in larger organizations reported the highest familiarity.
Combining "very" and "somewhat" familiar responses, 77% had familiarity with supervised machine learning, 74% generative AI, and 73% NLP. With generative AI getting so much media attention, and NLP being the broader area of AI that encompasses generative AI, these results may indicate that stakeholders are understanding the topic on the basis of buzz, not hands-on work with the technologies.
If defenders hope to get ahead of attackers, they will need to go beyond supervised learning algorithms trained on known attack patterns and generative AI. Instead, they’ll need to adopt a comprehensive toolkit comprised of multiple, varied AI approaches—including unsupervised algorithms that continuously learn from an organization’s specific data rather than relying on big data generalizations.
Different types of AI
Different types of AI have different strengths and use cases in cyber security. It’s important to choose the right technique for what you’re trying to achieve.
Supervised machine learning: Applied more often than any other type of AI in cyber security. Trained on human attack patterns and historical threat intelligence.
Large language models (LLMs): Applies deep learning models trained on extremely large data sets to understand, summarize, and generate new content. Used in generative AI tools.
Natural language processing (NLP): Applies computational techniques to process and understand human language.
Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies.
What impact will generative AI have on the cybersecurity field?
More than half of security professionals (57%) believe that generative AI will have a bigger impact on their field over the next few years than other types of AI.
Figure 1: Chart from Darktrace's State of AI in Cybersecurity Report
Security stakeholders are highly aware of generative AI and LLMs, viewing them as pivotal to the field's future. Generative AI excels at abstracting information, automating tasks, and facilitating human-computer interaction. However, LLMs can "hallucinate" due to training data errors and are vulnerable to prompt injection attacks. Despite improvements in securing LLMs, the best cyber defenses use a mix of AI types for enhanced accuracy and capability.
AI education is crucial as industry expectations for generative AI grow. Leaders and practitioners need to understand where and how to use AI while managing risks. As they learn more, there will be a shift from generative AI to broader AI applications.
Do security professionals fully understand the different types of AI in security products?
Only 26% of security professionals report a full understanding of the different types of AI in use within security products.
Confusion is prevalent in today’s marketplace. Our survey found that only 26% of respondents fully understand the AI types in their security stack, while 31% are unsure or confused by vendor claims. Nearly 65% believe generative AI is mainly used in cybersecurity, though it’s only useful for identifying phishing emails. This highlights a gap between user expectations and vendor delivery, with too much focus on generative AI.
Key findings include:
Executives and managers report higher understanding than practitioners.
Larger organizations have better understanding due to greater specialization.
As AI evolves, vendors are rapidly introducing new solutions faster than practitioners can learn to use them. There's a strong need for greater vendor transparency and more education for users to maximize the technology's value.
To help ease confusion around AI technologies in cybersecurity, Darktrace has released the CISO’s Guide to Cyber AI. A comprehensive white paper that categorizes the different applications of AI in cybersecurity. Download the White Paper here.
Do security professionals believe generative AI alone is enough to stop zero-day threats?
No! 86% of survey participants believe generative AI alone is NOT enough to stop zero-day threats
This consensus spans all geographies, organization sizes, and roles, though executives are slightly less likely to agree. Asia-Pacific participants agree more, while U.S. participants agree less.
Despite expecting generative AI to have the most impact, respondents recognize its limited security use cases and its need to work alongside other AI types. This highlights the necessity for vendor transparency and varied AI approaches for effective security across threat prevention, detection, and response.
Stakeholders must understand how AI solutions work to ensure they offer advanced, rather than outdated, threat detection methods. The survey shows awareness that old methods are insufficient.
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