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Thought Leadership

Securing Networks After SolarWinds Breach

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09
May 2021
09
May 2021
Uncover strategies to protect your business after SolarWinds with Darktrace. Get actionable insights for enhanced cybersecurity."

Insights from City of Tyler CIO Benny Yazdanpanahi, who keeps several Texas cities safe in the rapidly evolving and unpredictable threat landscape of today’s world.

The City of Tyler is the largest city in Northwest Texas, with a population of more than 100,000, and home to several major financial, health, and educational institutions. As such, it is an attractive target for cyber-criminals.

Although we were not a full SolarWinds customer, the recent breach illuminated the fundamental flaws in a rules and signatures based security approach. Cyber security must be adaptive for you to mitigate ever-present risk. As IT professionals, I believe we have a responsibility to educate business leaders – and ultimately the SolarWinds attack should serve a wakeup call to all organizations, no matter what providers they use, to reevaluate and strategize security beyond signature-based tooling. Cities must protect constituents from threats that are impossible to predict, often on small budgets and with lean teams. Here is how we do it.

Cyber AI as the key layer to an organization’s security

In order to protect your organization, you have to understand your entire digital enterprise. You have to understand what is happening within your infrastructure. If someone is uploading videos all the time and that is normal, my security technology needs to understand that. Robust security must know that if something comes from a certain resource, in a certain way, then it is okay in that context. But if these parameters subtly shift, it might be an indicator of an active threat. This is where Cyber AI becomes critical – it looks at human and enterprise behavior, and learns and grows with the organization. When something is anomalous, it does that threat investigation and response automatically. And importantly – it’s not black and white.

We originally turned to AI because heuristic tools were limiting. Most security vulnerability comes from people’s behaviors. Although we train our staff about phishing and other vulnerabilities, having an intelligent security solution is necessary. AI has become popular, but Darktrace Cyber AI truly self-learns all the subtleties of our evolving digital infrastructure.

In our Proof of Value with Darktrace Cyber AI, it actually showed us information we didn’t see before with our existing tools, and this proved to be information that was critical to monitor to stay ahead of unseen threats. Armed with Darktrace, we now know what is happening across all our environments, down to if a new device is on the network that isn’t a part of our IT. If anything is deemed a significant deviation from the normal, it is flagged immediately. This is the power of Darktrace’s machine learning. It learns the DNA of your organization and allows you to see what is most pertinent. This is a crucial and necessary ability for every organization. You can buy all kinds of products, but you must understand your network to know which threats you need to pay most attention to.

Being proactive: Fighting threats with AI

At the City of Tyler, we believe that security is layered. It is not enough to just have a firewall. You have to be proactive and have multiple layers. You have to do the basics, like patching software and educating the workforce – but all of this is just one layer of security. What Darktrace does which makes it stand out from the pack, is that it gives us the ability to react and fight back with Autonomous Response. This is critical when working to mitigate risk.

Antigena Email scrubs our email, seamlessly complementing our existing tools and strategy. The AI actually looks at the way people write emails and knows if their style is different. Antigena Email learns the human behind the accounts on City of Tyler’s infrastructure, using Darktrace’s Cyber AI to learn a unique sense of ‘self’ for the city’s email communications. By understanding what is ‘normal’ for every internal user, external sender, and all the complex relationships between them, Antigena Email neutralizes threats before they even reach the city’s users. It is the only tool that intelligently actions threats in the inbox in this way.

Antigena Network provides this proactive protection to the entire infrastructure, no matter the environment. As our employees moved to remote working, understanding what is normal across contexts has been imperative to our cyber strategy. We have a 24-hour staff rotation and I like to be a hands-on CIO, but Antigena, a solution that actually acts on all the information we have, is crucial in securing our city day in and day out. It is our on-going goal to be proactive about threats – Antigena drives this forward.

Stronger security with collaboration

Municipalities generally don’t have large budgets for cyber security, and small cities may have the purchasing power to buy only a few products. With AI achieving so much in one solution, an organization can do a lot with a single technology, as opposed to piling on tools meant to mitigate particular known risks.

We work with other cities, and share our best practices, because we don’t operate on an island by ourselves. We live in a community, and IT is often the catalyst for collaboration. Other smaller cities might not have as many resources, but sharing our use cases and what we think works can help our sister cities. This community collaboration is how we can help. It makes everyone safer.

We are uniquely fortunate in that before the pandemic even happened, most of our staff were already educated on how to use remote working tools. We were always thinking about how we could make the office seamless, but in today’s world, it is becoming imperative to embrace dynamic collaboration. Many organizations are rapidly innovating to be effective whilst remote, and it has opened up a lot of new vulnerabilities very quickly. Cyber AI is perfectly positioned to support this given it is fundamentally adaptable. From virtual deployment to expert SOC handling from a mobile app, Cyber AI is the solution to secure major infrastructure shifts. With the Darktrace Mobile App, I have been able to monitor and protect the organization from anywhere and see what I need to see at the touch of a button.

I have been here for 19 years educating city leaders about new technologies. I believe cyber security resilience comes from three points: people, process, and technology. AI allows these three to work together in combination. With full visibility, you can pinpoint vulnerabilities before they are breached, whether they originate from the people, the process, or the technology. With the ability to respond to threats autonomously, you have a built-in process layer to protect your infrastructure when it counts the most.

Learn more about how Cyber AI protects municipalities from the full range of cyber-threats.

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.
AUTHOR
ABOUT ThE AUTHOR
Benny Yazdanpanahi
Chief Information Officer, City of Tyler
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Inside the SOC

Disarming the WarmCookie Backdoor: Darktrace’s Oven-Ready Solution

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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.

The first investigated instance occurred between April 23 and April 24, when Darktrace detected a a series of unusual file download and outbound connectivity on a customer network, indicating successful WarmCookie exploitation. As mentioned by Elastic labs, "The PowerShell script abuses the Background Intelligent Transfer Service (BITS) to download WarmCookie and run the DLL with the Start export" [1].

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: Anomalous File / Masqueraded File Transfer]

- Associated device type: desktop

- Time of alert: 2024-04-23T14:11:18 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)

- Event details: File: http[:]//185.49.69[.]41/data/b834116823f01aeceed215e592dfcba7, total seen size: 144384B, direction: Incoming

- SHA1 file hash: 4ddf0d9c750bfeaebdacc14152319e21305443ff

- MD5 file hash: b09beb0b584deee198ecd66976e96237

[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 File / Masqueraded File Transfer]

- Associated device type: desktop

- Time of alert: 2024-05-07T09:03:35 UTC  

- Destination IP: 185.49.69[.]41

- Protocol: TCP

- ASN: AS28753 Leaseweb Deutschland GmbH

- Event details: File: http[:]//185.49.69[.]41/data/2849d40ade47af8edfd4e08352dd2cc8, total seen size: 72704B, direction: Incoming

- SHA1 file hash: 5b0a35c574ee40c4bccb9b0b942f9a9084216816

- MD5 file hash: aa9a73083184e1309431b3c7a3e44427  

[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) \

Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.
Figure 1: Cyber AI Analyst Coverage Details around the external destination, ‘185.49.69[.]41’.
External Sites Summary verifying the geographical location of the external IP, 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.

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’.
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

- Possible HTTP Command and Control

- Suspicious File Download

Darktrace RESPOND Model Detections:

- Antigena / Network / External Threat / Antigena Suspicious File Block

- Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

List of IoCs

IoC - Type - Description + Confidence

185.49.69[.]41 – IP Address – WarmCookie C2 Endpoint

/data/2849d40ade47af8edfd4e08352dd2cc8 – URI – Likely WarmCookie URI

/data/b834116823f01aeceed215e592dfcba7 – URI – Likely WarmCookie URI

4ddf0d9c750bfeaebdacc14152319e21305443ff  - SHA1 Hash  – Possible Malicious File

5b0a35c574ee40c4bccb9b0b942f9a9084216816  - SHA1 Hash – Possiblem Malicious File

MITRE ATT&CK Mapping

(Technique Name) – (Tactic) – (ID) – (Sub-Technique of)

Drive-by Compromise - INITIAL ACCESS - T1189

Ingress Tool Transfer - COMMAND AND CONTROL - T1105

Malware - RESOURCE DEVELOPMENT - T1588.001 - T1588

Lateral Tool Transfer - LATERAL MOVEMENT - T1570

Web Protocols - COMMAND AND CONTROL - T1071.001 - T1071

Web Services - RESOURCE DEVELOPMENT - T1583.006 - T1583

Browser Extensions - PERSISTENCE - T1176

Application Layer Protocol - COMMAND AND CONTROL - T1071

Fallback Channels - COMMAND AND CONTROL - T1008

Multi-Stage Channels - COMMAND AND CONTROL - T1104

Non-Standard Port - COMMAND AND CONTROL - T1571

One-Way Communication - COMMAND AND CONTROL - T1102.003 - T1102

Encrypted Channel - COMMAND AND CONTROL - T1573

External Proxy - COMMAND AND CONTROL - T1090.002 - T1090

Non-Application Layer Protocol - COMMAND AND CONTROL - T1095

References

[1] https://www.elastic.co/security-labs/dipping-into-danger

[2] https://www.gdatasoftware.com/blog/2024/06/37947-badspace-backdoor

[3] https://thehackernews.com/2024/06/new-phishing-campaign-deploys.html

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Justin Torres
Cyber Analyst

Blog

Thought Leadership

The State of AI in Cybersecurity: Understanding AI Technologies

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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.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners”. This blog will focus on security professionals’ understanding of AI technologies in cybersecurity tools.

To access download the full report, click here.

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.

Chart showing the types of AI expected to impact security the most
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.

To access the full report, click here.

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