Explore key insights on supply chain cyber attacks. Learn why IT and communication sectors are targeted and how to protect your business. Learn more!
In 2020, the financial services sector was the industry that experienced the most cyber-attacks. For years, attackers targeted these organizations because they were expectedly lucrative targets.
But in 2021, the financial services sector was no longer the most targeted. Instead, the IT and communications sector, including telecommunications providers, software developers, managed security service providers, and others faced the most attempted cyber-attacks.
This shift in priority target is not surprising for industry experts given the numerous high-profile software supply chain attacks in 2021, including those on SolarWinds, Kaseya and GitLab. Bad actors increasingly see software and developer infrastructure, platforms, and providers as entry vectors into governments, corporations, and critical infrastructure.
Darktrace’s researchers observed that its artificial intelligence (AI) autonomously interrupted around 150,000 threats each week against the sector in 2021. These research findings are developed based on Darktrace data generated by ‘early indicator analysis’ that looks at the breadcrumbs of potential cyber-attacks at several stages before attributing them to any actor and before they escalate into a full-blown crisis.
From this analysis, Darktrace predicts that, in 2022, we will see threat actors embed malicious software throughout the software supply chain, including proprietary source code, developer repositories, open-source libraries, and more. We will likely see further supply chain attacks against software platforms and additional publicized vulnerabilities.
Explaining the shift
This increase in attacks on this sector is likely because more companies rely on third-party trusted suppliers to handle their data while it’s in motion and at rest. This cyber-attack vector has proven substantially profitable for attackers who focused their efforts on related organizations to get to a target’s crown jewels. This shift means that small- and medium-sized companies are now more likely to experience an attack, even if they are not the end target.
Most recently, the uncovered vulnerability ‘Log4Shell’ embedded in a widely used software library left billions of devices exposed and prompted the Cybersecurity and Infrastructure Security Agency to provide formal guidance.
Unfortunately, many of these libraries are only updated and supported by volunteers, making it easy for vulnerabilities and intentional corruptions to slip through. DevSecOps will be a significant discussion point in 2022 as organizations begin to understand the importance of baking security into applications much earlier in the development process. Risks presented by the dependence on open source will put dev teams front and center.
Email phishing persists as a reliable method for attackers
Despite this relevant shift in targets, Darktrace found that the most widely used attack method on the IT sector continues to be phishing. Darktrace found that organizations in the industry faced an average of 600 unique email phishing campaigns a month in 2021. These campaigns also matured in sophistication, as most no longer contain a malicious link or attachment as in the typical ill-intended email.
In 2022, attackers will continue to advance their email attacks to hijack the communications chain more directly. We will see attackers hijack trusted supplier accounts to send spear phishing emails from genuine, trusted accounts, as we saw in the November 2021 FBI account takeover.
Top cyber-criminals will use ‘clean’ emails containing normal text, with messages carefully crafted to impersonate a trusted third party to induce recipients to reply and reveal sensitive information.
Facing the increase in attacks head-on
As the global software supply chain becomes increasingly interconnected, governments, corporations, and critical infrastructure organizations are all at risk of breach not only through their software and communications suppliers but via any security flaw in the extensive global software supply chain.
In the face of this cyber threat, organizations must focus on not only their own cyber resilience but also ensure they can hold their trusted suppliers accountable to best cyber practices. There is no magic solution to finding attacks embedded in your software suppliers, so the real challenge for organizations will be to operate while accepting this risk. This year, like 2021, it is increasingly unrealistic for these companies to hope to avoid breaches via their supply chains. Instead, they must have the ability to detect the presence of attackers after a breach and stop this malicious activity in the early stages.
If attackers can embed themselves at the beginning of the development process, organizations will have to detect and stop the attacker after they have gotten through. This problem calls for cyber defense technology that can spot vulnerabilities as threat actors exploit them.
This threat reinforces the need for security to be integrated earlier in the development process and the importance of quickly containing attacks to prevent business disruption. Since these are multi-stage attacks, organizations can use AI at every step to contain and remediate the threat.
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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.
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.
![Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/66a3e9519710d78dcbea06b5_66a3de28f7ac7a01b4b94b45_Screenshot%25202024-07-26%2520at%252010.33.48%2520AM.png)
![External Sites Summary verifying the geographical location of the external IP, 185.49.69[.]41’.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/66a3e9519710d78dcbea06ad_66a3de6808c6bac429c4520e_Screenshot%25202024-07-26%2520at%252010.34.08%2520AM.png)
