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Inside the SOC

Cyber Tactics in the Russo-Ukrainian Conflict

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09
Aug 2022
09
Aug 2022
The conflict between Russia and Ukraine has led to fears of a full-scale cyberwar. Learn the cyber attack tactics used, hacking groups involved, and more!

Introduction

Since the beginning of the Russian invasion of Ukraine in February 2022, cyber communities around the world have been witnessing what can be called a ‘renaissance of cyberwarfare' [1]. Rather than being financially motivated, threat actors are being guided by political convictions to defend allies or attack their enemies. This blog reviews some of the main threat actors involved in this conflict and their ongoing tactics, and advises on how organizations can best protect themselves. Darktrace’s preliminary assessments predicted that attacks would be observed globally with a focus on pro-Ukrainian nations such as North Atlantic Treaty Organization (NATO) members and that identified Advanced Persistent Threat (APT) groups would develop new and complex malware deployed through increasingly sophisticated attack vectors. This blog will show that many of these assessments had unexpected outcomes.

Context for Conflict 

Cyber confrontation between Russia and Ukraine dates back to 2013, when Viktor Yanukovych, (former President of Ukraine) rejected an EU trade pact in favour of an agreement with Russia. This sparked mass protests leading to his overthrow, and shortly after, Russian troops annexed Crimea and initiated the beginning of Russian-Ukrainian ground and cyber warfare. Since then, Russian threat actors have been periodically targeting Ukrainian infrastructure. One of the most notable examples of this, an attack against their national power grid in December 2015, resulted in power outages for approximately 255,000 people in Ukraine and was later attributed to the Russian hacking group Sandworm [2 & 3]. 

Another well-known attack in June 2017 overwhelmed the websites of hundreds of Ukrainian organizations using the infamous NotPetya malware. This attack is still considered the most damaging cyberattack in history, with more than €10 billion euros in financial damage [4]. In February 2022, countries witnessed the next stage of cyberwar against Ukraine with both new and familiar actors deploying various techniques to target their rival’s critical infrastructure. 

Tactic 1: Ransomware

Although some sources suggest US ransomware incidents and expectations of ransom may have declined during the conflict, ransomware still remained a significant tactic deployed globally across this period [5] [6] [7]. A Ukrainian hacking group, Network Battalion 65 (NB65), used ransomware to attack the Russian state-owned television and radio broadcasting network VGTRK. NB65 managed to steal 900,000 emails and 4000 files, and later demanded a ransom which they promised to donate to the Ukrainian army. This attack was unique because the group used the previously leaked source code of Conti, another infamous hacker group that had pledged its support to the Russian government earlier in the conflict. NB65 modified the leaked code to make unique ransomware for each of its targets [5]. 

Against expectations, Darktrace’s customer base appeared to deviate from these ransom trends. Analysts have seen relatively unsophisticated ransomware attacks during the conflict period, with limited evidence to suggest they were connected to any APT activity. Between November 2021 and June 2022, there were 51 confirmed ransomware compromises across the Darktrace customer base. This represents an increase of 43.16% compared to the same period the year before, accounting for relative customer growth. Whilst this suggests an overall growth in ransom cases, many of these confirmed incidents were unattributed and did not appear to be targeting any particular verticals or regions. While there was an increase in the energy sector, this could not be explicitly linked to the conflict. 

The Darktrace DETECT family has a variety of models related to ransomware visibility:

Darktrace Detections for T1486 (Data Encrypted for Impact):

- Compromise / Ransomware / Ransom or Offensive Words Written to SMB

- Compromise / Ransomware / Suspicious SMB Activity

- Anomalous Connection / Sustained MIME Type Conversion

- Unusual Activity / Sustained Anomalous SMB Activity

- Compromise / Ransomware / Suspicious SMB File Extension

- Unusual Activity / Anomalous SMB Read & Write

- Unusual Activity / Anomalous SMB Read & Write from New Device

- SaaS / Resource / SaaS Resources with Additional Extensions

- Compromise / Ransomware / Possible Ransom Note Read

- [If RESPOND is enabled] Antigena / Network / External Threat / Antigena Ransomware Block

Tactic 2: Wipers

One of the largest groups of executables seen during the conflict were wipers. On the eve of the invasion, Ukrainian organizations were targeted by a new wiper malware given the name “HermeticWiper”. Hermetic refers to the name of the Cyprian company “Hermetica Digital Ltd.” which was used by attackers to request a code signing certificate [6]. Such a digital certificate is used to verify the ownership of the code and that it has not been altered. The 24-year-old owner of Hermetica Digital says he had no idea that his company was abused to retrieve a code signing certificate [7]. 

HermeticWiper consists of three components: a worm, decoy ransomware and the wiper malware. The custom worm designed for HermeticWiper was used to spread the malware across the network of its infected machines. ESET researchers discovered that the decoy ransomware and the wiper were released at the same time [8]. The decoy ransomware was used to make it look like the machine was hit by ransomware, when in reality the wiper was already permanently wiping data from the machines. In the attack’s initial stage, it bypasses Windows security features designed to prevent overwriting boot records by installing a separate driver. After wiping data from the machine, HermeticWiper prevents that data from being re-fragmented and overwrites the files to fragment it further. This is done to make it more challenging to reconstruct data for post-compromise forensics [9]. Overall, the function and purpose of HermeticWiper seems similar to that of NotPetya ransomware. 

HermeticWiper is not the only conflict-associated wiper malware which has been observed. In January 2022, Microsoft warned Ukrainian customers that they detected wiper intrusion activity against several European organizations. One example of this was the MBR (Master Boot Record) wiper. This type of wiper overwrites the MBR, the disk sector that instructs a computer on how to load its operating system, with a ransomware note. In reality, the note is a misdirection and the malware destroys the MBR and targeted files [10].  

One of the most notable groups that used wiper malware was Sandworm. Sandworm is an APT attributed to Russia’s foreign military intelligence agency, GRU. The group has been active since 2009 and has used a variety of TTPs within their attacks. They have a history of targeting Ukraine including attacks in 2015 on Ukraine’s energy distribution companies and in 2017 when they used the aforementioned NotPetya malware against several Ukrainian organizations [11]. Another Russian (or pro-Russian) group using wiper malware to target Ukraine is DEV-0586. This group targeted various Ukrainian organizations in January 2022 with Whispergate wiper malware. This type of wiper malware presents itself as ransomware by displaying a file instructing the victim to pay Bitcoin to have their files decrypted [12].  

Darktrace did not observe any confirmed cases of HermeticWiper nor other conflict-associated wipers (e.g IsaacWiper and CaddyWiper) within the customer base over this period. Despite this, Darktrace DETECT has a variety of models related to wipers and data destruction:

Darktrace Detections for T1485 (Data Destruction)- this is the main technique exploited during wiper attacks

- Unusual Activity / Anomalous SMB Delete Volume

- IaaS / Unusual Activity / Anomalous AWS Resources Deleted

- IaaS / Storage / S3 Bucket Delete

- SaaS / Resource / Mass Email Deletes from Rare Location

- SaaS / Resource / Anomalous SaaS Resources Deleted

- SaaS / Resource / Resource Permanent Delete

- [If RESPOND is enabled] Antigena / Network / Manual / Enforce Pattern of Life

- [If RESPOND is enabled] Antigena / SaaS / Antigena Unusual Activity Block

Tactic 3: Spear-Phishing

Another strategy that some threat actors employ is spear-phishing. Targeting can be done using email, social media, messaging, or other platforms.

The hacking group Armageddon (also known as Gamaredon) has been responsible for several spear-phishing attacks during the crisis, primarily targeting individuals involved in the Ukrainian Government [13]. Since the beginning of the war, the group has been sending out a large volume of emails containing an HTML file which, if opened, downloads and launches a RAR payload. Those who click the attached link download an HTA with a PowerShell script which obtains the final Armageddon payload. Using the same strategy, the group is also targeting governmental agencies in the European Union [14]. With high-value targets, the need to improve teaching around phishing identification to minimize the chance of being caught in an attacker's net is higher than ever. 

In comparison to the wider trends, Darktrace analysts again saw little-to-no evidence of conflict-associated phishing campaigns affecting customers. Those phishing attempts which did target customers were largely not conflict-related. In some cases, the conflict was used opportunistically, such as when one customer was targeted with a phishing email referencing Russian bank exclusions from the SWIFT payment system (Figures 1 and 2). The email was identified by Darktrace/Email as a probable attempt at financial extortion and inducement - in this case the company received a spoofed email from a major bank’s remittance department.  

Figure 1- Screencap of targeted phishing email sent to Darktrace customer
Figure 2- Attached file contains soliciting reference to SWIFT, a money payment system which select Russian banks were removed from because of the conflict [15]

 Although the conflict was used as a reference in some examples, in most of Darktrace’s observed phishing cases during the conflict period there was little-to-no evidence to suggest that the company being targeted nor the threat actor behind the phishing attempt was associated with or attributable to the Russia-Ukraine conflict.

However, Darktrace/Email has several model categories which pick up phishing related threats:

Sample of Darktrace for Email Detections for T1566 (Phishing)- this is the overarching technique exploited during spear-phishing events

Model Categories:

- Inducement

- Internal / External User Spoofing

- Internal / External Domain Spoofing

- Fake Support

- Link to Rare Domains

- Link to File Storage

- Redirect Links

- Anomalous / Malicious Attachments

- Compromised Known Sender

Specific models can be located on the Email Console

 

Tactic 4: Distributed-Denial-of-Service (DDoS)

Another tactic employed by both pro-Russian and pro-Ukrainian threat actors was DDoS (Distributed Denial of Service) attacks. Both pro-Russia and pro-Ukraine actors were seen targeting critical infrastructure, information resources, and governmental platforms with mass DDoS attacks. The Ukrainian Minister of Digital Transformation, Mykhailo Fedorov, called on an IT Army of underground Ukrainian hackers and volunteers to protect Ukraine's critical infrastructure and conduct DDoS attacks against Russia [16]. As of 1 August 2022, more than two hundred thousand people are subscribed to the group's official Telegram channel, where potential DDoS targets are announced [17].

Darktrace observed similar pro-Ukraine DDoS behaviors within a variety of customer environments. These DDoS campaigns appeared to involve low-volume individual support combined with crowd-sourced DDoS activity. They were hosted on a range of public-sourced DDoS sites and seemed to share sentiments of groups such as the IT Army of Ukraine (Figure 3).

Figure 3- Example DDoS outsource domain with unusual TLD 

From the Russian side, one of the prominent newly emerged groups, Killnet, is striking back, launching several massive DDoS attacks against the critical infrastructure of countries that provide weaponry to Ukraine [18 & 19]. Today, the number of supporters of Killnet has grown to eighty-four thousand on their Telegram channel. The group has already launched a number of mass attacks on several NATO states, including Germany, Poland, Italy, Lithuania and Norway. This shows the conflict has attracted new and fast-growing groups with large backing and the capacity to undertake widespread attacks. 

DETECT has several models to identify anomalous DoS/DDoS activity:

Darktrace Detection for T1498 (Network Denial of Service)- this is the main technique exploited during DDoS attacks

- Device / Anomaly Indicators / Denial of Service Activity Indicator

- Anomalous Server Activity / Possible Denial of Service Activity

- [If RESPOND is enabled] Antigena / Network / External Threat / Antigena Suspicious Activity Block

What did Darktrace observe?

Darktrace’s cross-fleet detections were largely contrary to expectations. Analysts did not see large-scale complex conflict-linked attacks utilizing either conflict-associated ransomware, malware, or other TTPs. Instead, cyber incidents observed were largely opportunistic, using malware that could be purchased through Malware-as-a-Service models and other widely available toolkits, (rather than APT or conflict-attributable attacks). Overall, this is not to say there have been no repercussions from the conflict or that opportunistic attacks will cease, but evidence suggests that there were fewer wider cyber consequences beyond the initial APT-based attacks seen in the public forum. 

Another trend expected since the beginning of the conflict was targeted responses to sanction announcements focusing on NATO businesses and governments. Analysts, however, saw the limited reactive actions, with little-to-no direct impact from sanction announcements. Although cyber-attacks on some NATO organizations did take place, they were not as widespread or impactful as expected. Lastly, it was thought that exposure to new and sophisticated exploits would increase and be used to weaken NATO nations - especially corporations in critical industries. However, analysts observed relatively common exploits deployed indiscriminately and opportunistically. Overall, with the wider industry expecting chaos, Darktrace analysts did not see the crisis taken advantage of to target wider businesses outside of Ukraine. Based on this comparison between expectations and reality, the conflict has demonstrated the danger of  falling prey to confirmation bias and the need to remain vigilant and expect the unexpected. It may be possible to say that cyberwar is ‘cold’ right now, however the element of surprise is always present, and it is better to be prepared to protect yourself and your organization.    

What to Expect from the Future

As cyberattacks continue to become less monetarily and physically costly, it is to be expected that they will increase in frequency. Even after a political ceasefire is established, hacking groups can harbour resentment and continue their attacks, though possibly on a smaller scale.  

Additionally, the longer this conflict continues, the more sophisticated hacking groups’s attacks may become. In one of their publications, Killnet shared with subscribers that they had created ‘network weaponry’ powerful enough to simultaneously take down five European countries (Figure 4) [20]. Whether or not this claim is true, it is vital to be prepared. The European Union and the United States have supported Ukraine since the start of the invasion, and the EU has also stated that it is considering providing further assistance to help Ukraine in cyberspace [21].

Figure 4- Snapshot of Killnet Telegram announcement

How to Protect Against these Attacks

In the face of wider conflict and cybersecurity tensions, it is crucial that organizations evaluate their security stack and practise the following: 

·       Know what your critical assets are and what software is running on them. 

·       Keep your software up to date. Prioritize patching critical and high vulnerabilities that allow remote code execution. 

·       Enforce Multifactor Authentication (MFA) to the greatest extent possible. 

·       Require the use of a password manager to generate strong and unique passwords for each separate account. 

·       Backup all the essential files on the cloud and external drives and regularly maintain them. 

·       Train your employees to recognize phishing emails, suspicious websites, infected links or other abnormalities to prevent successful compromise of email accounts. 

In order to prevent an organization from suffering damage due to one of the attacks mentioned above, a full-circle approach is needed. This defence starts with a thorough understanding of the attack surface to provide timely mitigation. This can be supported by Darktrace products: 

·       As shown throughout this blog, Darktrace DETECT and Darktrace/Email have several models relating to conflict-associated TTPs and attacks. These help to quickly alert security teams and provide visibility of anomalous behaviors.

·       Darktrace PREVENT/ASM helps to identify vulnerable external-facing assets. By patching and securing these devices, the risk of exploit is drastically reduced.

·       Darktrace RESPOND and RESPOND/Email can make targeted actions to a range of threats such as blocking incoming DDoS connections or locking malicious email links.

Thanks to the Darktrace Threat Intelligence Unit for their contributions to this blog.

Appendices 

Reference List

[1] https://www.atlanticcouncil.org/blogs/ukrainealert/vladimir-putins-ukraine-invasion-is-the-worlds-first-full-scale-cyberwar/ 

[2] https://www.reuters.com/article/us-ukraine-cybersecurity-idUSKCN0VY30K

[3] https://www.reuters.com/article/us-ukraine-cybersecurity-sandworm-idUSKBN0UM00N20160108

[4 & 11] https://www.wired.com/story/notpetya-cyberattack-ukraine-russia-code-crashed-the-world/ 

[5] https://www.scmagazine.com/analysis/ransomware/despite-hopes-for-decline-ransomware-attacks-increased-during-russia-ukraine-conflict

[6] https://ransomware.org/blog/has-the-ukraine-conflict-disrupted-ransomware-attacks/

[7] https://www.cfr.org/blog/financial-incentives-may-explain-perceived-lack-ransomware-russias-latest-assault-ukraine

[8] https://www.bleepingcomputer.com/news/security/hackers-use-contis-leaked-ransomware-to-attack-russian-companies/ 

[9] https://voi.id/en/technology/138937/hermetica-owner-from-cyprus-didnt-know-his-server-was-used-in-malicious-malware-attack-in-ukraine 

[10] https://www.reuters.com/article/ukraine-crisis-cyber-cyprus-idCAKBN2KT2QI 

[11] https://www.eset.com/int/about/newsroom/press-releases/research/eset-research-ukraine-hit-by-destructive-attacks-before-and-during-the-russian-invasion-with-hermet/ 

[12] https://blog.malwarebytes.com/threat-intelligence/2022/03/hermeticwiper-a-detailed-analysis-of-the-destructive-malware-that-targeted-ukraine/ 

[13] https://www.microsoft.com/security/blog/2022/01/15/destructive-malware-targeting-ukrainian-organizations/ 

[15] https://www.cisa.gov/uscert/ncas/alerts/aa22-057a 

[16] https://attack.mitre.org/groups/G0047/ 

[17] https://cyware.com/news/ukraine-cert-warns-of-increasing-attacks-by-armageddon-group-850081f8 

[18] https://www.bbc.co.uk/news/business-60521822

[19] https://foreignpolicy.com/2022/04/11/russia-cyberwarfare-us-ukraine-volunteer-hackers-it-army/

[20] https://t.me/itarmyofukraine2022

[21] https://www.csoonline.com/article/3664859/russian-ddos-attack-on-lithuania-was-planned-on-telegram-flashpoint-says.html

[19 & 20] https://flashpoint.io/blog/killnet-kaliningrad-and-lithuanias-transport-standoff-with-russia/ 

[21] https://presidence-francaise.consilium.europa.eu/en/news/member-states-united-in-supporting-ukraine-and-strengthening-the-eu-s-telecommunications-and-cybersecurity-resilience/ 

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
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Rosa Jong
OSINT Analyst
Taisiia Garkava
Security Analyst
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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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