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How Darktrace Stopped an Account Hijack Fast

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28
Sep 2023
28
Sep 2023
Learn how Darktrace detected an account hijack within days of deployment. Discover the strategies used to protect against cyber threats.

Cloud Migration Expanding the Attack Surface

Cloud migration is here to stay – accelerated by pandemic lockdowns, there has been an ongoing increase in the use of public cloud services, and Gartner has forecasted worldwide public cloud spending to grow around 20%, or by almost USD 600 billion [1], in 2023. With more and more organizations utilizing cloud services and moving their operations to the cloud, there has also been a corresponding shift in malicious activity targeting cloud-based software and services, including Microsoft 365, a prominent and oft-used Software-as-a-Service (SaaS).

With the adoption and implementation of more SaaS products, the overall attack surface of an organization increases – this gives malicious actors additional opportunities to exploit and compromise a network, necessitating proper controls to be in place. This increased attack surface can leave organization’s open to cyber risks like cloud misconfigurations, supply chain attacks and zero-day vulnerabilities [2]. In order to achieve full visibility over cloud activity and prevent SaaS compromise, it is paramount for security teams to deploy sophisticated security measures that are able to learn an organization’s SaaS environment and detect suspicious activity at the earliest stage.

Darktrace Immediately Detects Hijacked Account

In May 2023, Darktrace observed a chain of suspicious SaaS activity on the network of a customer who was about to begin their trial of Darktrace/Apps™ and Darktrace/Email™. Despite being deployed on the network for less than a week, Darktrace DETECT™ recognized that the legitimate SaaS account, belonging to an executive at the organization, had been hijacked. Darktrace/Email was able to provide full visibility over inbound and outbound mail and identified that the compromised account was subsequently used to launch an internal spear-phishing campaign.

If Darktrace RESPOND™ were enabled in autonomous response mode at the time of this compromise, it would have been able to take swift preventative action to disrupt the account compromise and prevent the ensuing phishing attack.

Account Hijack Attack Overview

Unusual External Sources for SaaS Credentials

On May 9, 2023, Darktrace/Apps detected the first in a series of anomalous activities performed by a Microsoft 365 user account that was indicative of compromise, namely a failed login from an external IP address located in Virginia.

Figure 1: The failed login notice, as seen in Darktrace/Apps. The notice includes additional context about the failed login attempt to the SaaS account.

Just a few minutes later, Darktrace observed the same user credential being used to successfully login from the same unusual IP address, with multi-factor authentication (MFA) requirements satisfied.

Figure 2: The “Unusual External Source for SaaS Credential Use” model breach summary, showing the successful login to the SaaS user account (with MFA), from the rare external IP address.

A few hours after this, the user credential was once again used to login from a different city in the state of Virginia, with MFA requirements successfully met again. Around the time of this activity, the SaaS user account was also observed previewing various business-related files hosted on Microsoft SharePoint, behavior that, taken in isolation, did not appear to be out of the ordinary and could have represented legitimate activity.

The following day, May 10, however, there were additional login attempts observed from two different states within the US, namely Texas and Florida. Darktrace understood that this activity was extremely suspicious, as it was highly improbable that the legitimate user would be able to travel over 2,500 miles in such a short period of time. Both login attempts were successful and passed MFA requirements, suggesting that the malicious actor was employing techniques to bypass MFA. Such MFA bypass techniques could include inserting malicious infrastructure between the user and the application and intercepting user credentials and tokens, or by compromising browser cookies to bypass authentication controls [3]. There have also been high-profile cases in the recent years of legitimate users mistakenly (and perhaps even instinctively) accepting MFA prompts on their token or mobile device, believing it to be a legitimate process despite not having performed the login themselves.

New Email Rule

On the evening of May 10, following the successful logins from multiple US states, Darktrace observed the Microsoft 365 user creating a new inbox rule, named “.’, in Microsoft Outlook from an IP located in Florida. Threat actors are often observed naming new email rules with single characters, likely to evade detection, but also for the sake of expediency so as to not expend any additional time creating meaningful labels.

In this case the newly created email rules included several suspicious properties, including ‘AlwaysDeleteOutlookRulesBlob’, ‘StopProcessingRules’ and “MoveToFolder”.

Firstly, ‘AlwaysDeleteOutlookRulesBlob’ suppresses or hides warning messages that typically appear if modifications to email rules are made [4]. In this case, it is likely the malicious actor was attempting to implement this property to obfuscate the creation of new email rules.

The ‘StopProcessingRules’ rule meant that any subsequent email rules created by the legitimate user would be overridden by the email rule created by the malicious actor [5]. Finally, the implementation of “MoveToFolder” would allow the malicious actor to automatically move all outgoing emails from the “Sent” folder to the “Deleted Items” folder, for example, further obfuscating their malicious activities [6]. The utilization of these email rule properties is frequently observed during account hijackings as it allows attackers to delete and/or forward key emails, delete evidence of exploitation and launch phishing campaigns [7].

In this incident, the new email rule would likely have enabled the malicious actor to evade the detection of traditional security measures and achieve greater persistence using the Microsoft 365 account.

Figure 3: Screenshot of the “New Email Rule” model breach. The Office365 properties associated with the newly modified Microsoft Outlook inbox rule, “.”, are highlighted in red.

Account Update

A few hours after the creation of the new email rule, Darktrace observed the threat actor successfully changing the Microsoft 365 user’s account password, this time from a new IP address in Texas. As a result of this action, the attacker would have locked out the legitimate user, effectively gaining full access over the SaaS account.

Figure 4: The model breach event log showing the user password and token change updates performed by the compromised SaaS account.

Phishing Emails

The compromised SaaS account was then observed sending a high volume of suspicious emails to both internal and external email addresses. Darktrace was able to identify that the emails attempting to impersonate the legitimate service DocuSign and contained a malicious link prompting users to click on the text “Review Document”. Upon clicking this link, users would be redirected to a site hosted on Adobe Express, namely hxxps://express.adobe[.]com/page/A9ZKVObdXhN4p/.

Adobe Express is a free service that allows users to create web pages which can be hosted and shared publicly; it is likely that the threat actor here leveraged the service to use in their phishing campaign. When clicked, such links could result in a device unwittingly downloading malware hosted on the site, or direct unsuspecting users to a spoofed login page attempting to harvest user credentials by imitating legitimate companies like Microsoft.

Figure 5: Screenshot of the phishing email, containing a malicious link hidden behind the “Review Document” text. The embedded link directs to a now-defunct page that was hosted on Adobe Express.

The malicious site hosted on Adobe Express was subsequently taken down by Adobe, possibly in response to user reports of maliciousness. Unfortunately though, platforms like this that offer free webhosting services can easily and repeatedly be abused by malicious actors. Simply by creating new pages hosted on different IP addresses, actors are able to continue to carry out such phishing attacks against unsuspecting users.

In addition to the suspicious SaaS and email activity that took place between May 9 and May 10, Darktrace/Email also detected the compromised account sending and receiving suspicious emails starting on May 4, just two days after Darktrace’s initial deployment on the customer’s environment. It is probable that the SaaS account was compromised around this time, or even prior to Darktrace’s deployment on May 2, likely via a phishing and credential harvesting campaign similar to the one detailed above.

Figure 6: Event logs of the compromised SaaS user, here seen breaching several Darktrace/Email model breaches on 4th May.

Darktrace Coverage

As the customer was soon to begin their trial period, Darktrace RESPOND was set in “human confirmation” mode, meaning that any preventative RESPOND actions required manual application by the customer’s security team.

If Darktrace RESPOND had been enabled in autonomous response mode during this incident, it would have taken swift mitigative action by logging the suspicious user out of the SaaS account and disabling the account for a defined period of time, in doing so disrupting the attack at the earliest possible stage and giving the customer the necessary time to perform remediation steps.  As it was, however, these RESPOND actions were suggested to the customer’s security team for them to manually apply.

Figure 7: Example of Darktrace RESPOND notices, in response to the anomalous user activity.

Nevertheless, with Darktrace/Apps in place, visibility over the anomalous cloud-based activities was significantly increased, enabling the swift identification of the chain of suspicious activities involved in this compromise.

In this case, the prospective customer reached out to Darktrace directly through the Ask the Expert (ATE) service. Darktrace’s expert analyst team then conducted a timely and comprehensive investigation into the suspicious activity surrounding this SaaS compromise, and shared these findings with the customer’s security team.

Conclusion

Ultimately, this example of SaaS account compromise highlights Darktrace’s unique ability to learn an organization’s digital environment and recognize activity that is deemed to be unexpected, within a matter of days.

Due to the lack of obvious or known indicators of compromise (IoCs) associated with the malicious activity in this incident, this account hijack would likely have gone unnoticed by traditional security tools that rely on a rules and signatures-based approach to threat detection. However, Darktrace’s Self-Learning AI enables it to detect the subtle deviations in a device’s behavior that could be indicative of an ongoing compromise.

Despite being newly deployed on a prospective customer’s network, Darktrace DETECT was able to identify unusual login attempts from geographically improbable locations, suspicious email rule updates, password changes, as well as the subsequent mounting of a phishing campaign, all before the customer’s trial of Darktrace had even begun.

When enabled in autonomous response mode, Darktrace RESPOND would be able to take swift preventative action against such activity as soon as it is detected, effectively shutting down the compromise and mitigating any subsequent phishing attacks.

With the full deployment of Darktrace’s suite of products, including Darktrace/Apps and Darktrace/Email, customers can rest assured their critical data and systems are protected, even in the case of hybrid and multi-cloud environments.

Credit: Samuel Wee, Senior Analyst Consultant & Model Developer

Appendices

References

[1] https://www.gartner.com/en/newsroom/press-releases/2022-10-31-gartner-forecasts-worldwide-public-cloud-end-user-spending-to-reach-nearly-600-billion-in-2023

[2] https://www.upguard.com/blog/saas-security-risks

[3] https://www.microsoft.com/en-us/security/blog/2022/11/16/token-tactics-how-to-prevent-detect-and-respond-to-cloud-token-theft/

[4] https://learn.microsoft.com/en-us/powershell/module/exchange/disable-inboxrule?view=exchange-ps

[5] https://learn.microsoft.com/en-us/dotnet/api/microsoft.exchange.webservices.data.ruleactions.stopprocessingrules?view=exchange-ews-api

[6] https://learn.microsoft.com/en-us/dotnet/api/microsoft.exchange.webservices.data.ruleactions.movetofolder?view=exchange-ews-api

[7] https://blog.knowbe4.com/check-your-email-rules-for-maliciousness

Darktrace Model Detections

Darktrace DETECT and RESPOND Models Breached:

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Unusual Activity / Multiple Unusual External Sources for SaaS Credential

Antigena / SaaS / Antigena Unusual Activity Block (RESPOND Model)

SaaS / Compliance / New Email Rule

Antigena / SaaS / Antigena Significant Compliance Activity Block

SaaS / Compromise / Unusual Login and New Email Rule (Enhanced Monitoring Model)

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (RESPOND Model)

SaaS / Compromise / SaaS Anomaly Following Anomalous Login (Enhanced Monitoring Model)

SaaS / Compromise / Unusual Login and Account Update

Antigena / SaaS / Antigena Suspicious SaaS Activity Block (RESPOND Model)

IoC – Type – Description & Confidence

hxxps://express.adobe[.]com/page/A9ZKVObdXhN4p/ - Domain – Probable Phishing Page (Now Defunct)

37.19.221[.]142 – IP Address – Unusual Login Source

35.174.4[.]92 – IP Address – Unusual Login Source

MITRE ATT&CK Mapping

Tactic - Techniques

INITIAL ACCESS, PRIVILEGE ESCALATION, DEFENSE EVASION, PERSISTENCE

T1078.004 – Cloud Accounts

DISCOVERY

T1538 – Cloud Service Dashboards

CREDENTIAL ACCESS

T1539 – Steal Web Session Cookie

RESOURCE DEVELOPMENT

T1586 – Compromise Accounts

PERSISTENCE

T1137.005 – Outlook Rules

Probability yardstick used to communicate the probability that statements or explanations given are correct.
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.
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Min Kim
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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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Inside the SOC

Jupyter Ascending: Darktrace’s Investigation of the Adaptive Jupyter Information Stealer

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18
Jul 2024

What is Malware as a Service (MaaS)?

Malware as a Service (MaaS) is a model where cybercriminals develop and sell or lease malware to other attackers.

This approach allows individuals or groups with limited technical skills to launch sophisticated cyberattacks by purchasing or renting malware tools and services. MaaS is often provided through online marketplaces on the dark web, where sellers offer various types of malware, including ransomware, spyware, and trojans, along with support services such as updates and customer support.

The Growing MaaS Marketplace

The Malware-as-a-Service (MaaS) marketplace is rapidly expanding, with new strains of malware being regularly introduced and attracting waves of new and previous attackers. The low barrier for entry, combined with the subscription-like accessibility and lucrative business model, has made MaaS a prevalent tool for cybercriminals. As a result, MaaS has become a significant concern for organizations and their security teams, necessitating heightened vigilance and advanced defense strategies.

Examples of Malware as a Service

  • Ransomware as a Service (RaaS): Providers offer ransomware kits that allow users to launch ransomware attacks and share the ransom payments with the service provider.
  • Phishing as a Service: Services that provide phishing kits, including templates and email lists, to facilitate phishing campaigns.
  • Botnet as a Service: Renting out botnets to perform distributed denial-of-service (DDoS) attacks or other malicious activities.
  • Information Stealer: Information stealers are a type of malware specifically designed to collect sensitive data from infected systems, such as login credentials, credit card numbers, personal identification information, and other valuable data.

How does information stealer malware work?

Information stealers are an often-discussed type MaaS tool used to harvest personal and proprietary information such as administrative credentials, banking information, and cryptocurrency wallet details. This information is then exfiltrated from target networks via command-and-control (C2) communication, allowing threat actors to monetize the data. Information stealers have also increasingly been used as an initial access vector for high impact breaches including ransomware attacks, employing both double and triple extortion tactics.

After investigating several prominent information stealers in recent years, the Darktrace Threat Research team launched an investigation into indicators of compromise (IoCs) associated with another variant in late 2023, namely the Jupyter information stealer.

What is Jupyter information stealer and how does it work?

The Jupyter information stealer (also known as Yellow Cockatoo, SolarMarker, and Polazert) was first observed in the wild in late 2020. Multiple variants have since become part of the wider threat landscape, however, towards the end of 2023 a new variant was observed. This latest variant achieved greater stealth and updated its delivery method, targeting browser extensions such as Edge, Firefox, and Chrome via search engine optimization (SEO) poisoning and malvertising. This then redirects users to download malicious files that typically impersonate legitimate software, and finally initiates the infection and the attack chain for Jupyter [3][4]. In recently noted cases, users download malicious executables for Jupyter via installer packages created using InnoSetup – an open-source compiler used to create installation packages in the Windows OS.

The latest release of Jupyter reportedly takes advantage of signed digital certificates to add credibility to downloaded executables, further supplementing its already existing tactics, techniques and procedures (TTPs) for detection evasion and sophistication [4]. Jupyter does this while still maintaining features observed in other iterations, such as dropping files into the %TEMP% folder of a system and using PowerShell to decrypt and load content into memory [4]. Another reported feature includes backdoor functionality such as:

  • C2 infrastructure
  • Ability to download and execute malware
  • Execution of PowerShell scripts and commands
  • Injecting shellcode into legitimate windows applications

Darktrace Coverage of Jupyter information stealer

In September 2023, Darktrace’s Threat Research team first investigated Jupyter and discovered multiple IoCs and TTPs associated with the info-stealer across the customer base. Across most investigated networks during this time, Darktrace observed the following activity:

  • HTTP POST requests over destination port 80 to rare external IP addresses (some of these connections were also made via port 8089 and 8090 with no prior hostname lookup).
  • HTTP POST requests specifically to the root directory of a rare external endpoint.
  • Data streams being sent to unusual external endpoints
  • Anomalous PowerShell execution was observed on numerous affected networks.

Taking a further look at the activity patterns detected, Darktrace identified a series of HTTP POST requests within one customer’s environment on December 7, 2023. The HTTP POST requests were made to the root directory of an external IP address, namely 146.70.71[.]135, which had never previously been observed on the network. This IP address was later reported to be malicious and associated with Jupyter (SolarMarker) by open-source intelligence (OSINT) [5].

Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.
Figure 1: Device Event Log indicating several connections from the source device to the rare external IP address 146.70.71[.]135 over port 80.

This activity triggered the Darktrace / NETWORK model, ‘Anomalous Connection / Posting HTTP to IP Without Hostname’. This model alerts for devices that have been seen posting data out of the network to rare external endpoints without a hostname. Further investigation into the offending device revealed a significant increase in external data transfers around the time Darktrace alerted the activity.

This External Data Transfer graph demonstrates a spike in external data transfer from the internal device indicated at the top of the graph on December 7, 2023, with a time lapse shown of one week prior.
Figure 2: This External Data Transfer graph demonstrates a spike in external data transfer from the internal device indicated at the top of the graph on December 7, 2023, with a time lapse shown of one week prior.

Packet capture (PCAP) analysis of this activity also demonstrates possible external data transfer, with the device observed making a POST request to the root directory of the malicious endpoint, 146.70.71[.]135.

PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.
Figure 3: PCAP of a HTTP POST request showing streams of data being sent to the endpoint, 146.70.71[.]135.

In other cases investigated by the Darktrace Threat Research team, connections to the rare external endpoint 67.43.235[.]218 were detected on port 8089 and 8090. This endpoint was also linked to Jupyter information stealer by OSINT sources [6].

Darktrace recognized that such suspicious connections represented unusual activity and raised several model alerts on multiple customer environments, including ‘Compromise / Large Number of Suspicious Successful Connections’ and ‘Anomalous Connection / Multiple Connections to New External TCP Port’.

In one instance, a device that was observed performing many suspicious connections to 67.43.235[.]218 was later observed making suspicious HTTP POST connections to other malicious IP addresses. This included 2.58.14[.]246, 91.206.178[.]109, and 78.135.73[.]176, all of which had been linked to Jupyter information stealer by OSINT sources [7] [8] [9].

Darktrace further observed activity likely indicative of data streams being exfiltrated to Jupyter information stealer C2 endpoints.

Graph displaying the significant increase in the number of HTTP POST requests with No Get made by an affected device, likely indicative of Jupyter information stealer C2 activity.
Figure 4: Graph displaying the significant increase in the number of HTTP POST requests with No Get made by an affected device, likely indicative of Jupyter information stealer C2 activity.

In several cases, Darktrace was able to leverage customer integrations with other security vendors to add additional context to its own model alerts. For example, numerous customers who had integrated Darktrace with Microsoft Defender received security integration alerts that enriched Darktrace’s model alerts with additional intelligence, linking suspicious activity to Jupyter information stealer actors.

The security integration model alerts ‘Security Integration / Low Severity Integration Detection’ and (right image) ‘Security Integration / High Severity Integration Detection’, linking suspicious activity observed by Darktrace with Jupyter information stealer (SolarMarker).
Figure 5: The security integration model alerts ‘Security Integration / Low Severity Integration Detection’ and (right image) ‘Security Integration / High Severity Integration Detection’, linking suspicious activity observed by Darktrace with Jupyter information stealer (SolarMarker).

Conclusion

The MaaS ecosystems continue to dominate the current threat landscape and the increasing sophistication of MaaS variants, featuring advanced defense evasion techniques, poses significant risks once deployed on target networks.

Leveraging anomaly-based detections is crucial for staying ahead of evolving MaaS threats like Jupyter information stealer. By adopting AI-driven security tools like Darktrace / NETWORK, organizations can more quickly identify and effectively detect and respond to potential threats as soon as they emerge. This is especially crucial given the rise of stealthy information stealing malware strains like Jupyter which cannot only harvest and steal sensitive data, but also serve as a gateway to potentially disruptive ransomware attacks.

Credit to Nahisha Nobregas (Senior Cyber Analyst), Vivek Rajan (Cyber Analyst)

References

1.     https://www.paloaltonetworks.com/cyberpedia/what-is-multi-extortion-ransomware

2.     https://flashpoint.io/blog/evolution-stealer-malware/

3.     https://blogs.vmware.com/security/2023/11/jupyter-rising-an-update-on-jupyter-infostealer.html

4.     https://www.morphisec.com/hubfs/eBooks_and_Whitepapers/Jupyter%20Infostealer%20WEB.pdf

5.     https://www.virustotal.com/gui/ip-address/146.70.71.135

6.     https://www.virustotal.com/gui/ip-address/67.43.235.218/community

7.     https://www.virustotal.com/gui/ip-address/2.58.14.246/community

8.     https://www.virustotal.com/gui/ip-address/91.206.178.109/community

9.     https://www.virustotal.com/gui/ip-address/78.135.73.176/community

Appendices

Darktrace Model Detections

  • Anomalous Connection / Posting HTTP to IP Without Hostname
  • Compromise / HTTP Beaconing to Rare Destination
  • Unusual Activity / Unusual External Data to New Endpoints
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / Large Number of Suspicious Successful Connections
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Excessive Posts to Root
  • Compromise / Sustained SSL or HTTP Increase
  • Security Integration / High Severity Integration Detection
  • Security Integration / Low Severity Integration Detection
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Unusual Activity / Unusual External Data Transfer

AI Analyst Incidents:

  • Unusual Repeated Connections
  • Possible HTTP Command and Control to Multiple Endpoints
  • Possible HTTP Command and Control

List of IoCs

Indicators – Type – Description

146.70.71[.]135

IP Address

Jupyter info-stealer C2 Endpoint

91.206.178[.]109

IP Address

Jupyter info-stealer C2 Endpoint

146.70.92[.]153

IP Address

Jupyter info-stealer C2 Endpoint

2.58.14[.]246

IP Address

Jupyter info-stealer C2 Endpoint

78.135.73[.]176

IP Address

Jupyter info-stealer C2 Endpoint

217.138.215[.]105

IP Address

Jupyter info-stealer C2 Endpoint

185.243.115[.]88

IP Address

Jupyter info-stealer C2 Endpoint

146.70.80[.]66

IP Address

Jupyter info-stealer C2 Endpoint

23.29.115[.]186

IP Address

Jupyter info-stealer C2 Endpoint

67.43.235[.]218

IP Address

Jupyter info-stealer C2 Endpoint

217.138.215[.]85

IP Address

Jupyter info-stealer C2 Endpoint

193.29.104[.]25

IP Address

Jupyter info-stealer C2 Endpoint

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About the author
Nahisha Nobregas
SOC Analyst
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