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Ivanti Sentry Vulnerability Analysis & Insights| Darktrace

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20

Dec 2023

20

Dec 2023

Darktrace observed a critical vulnerability in Ivanti Sentry's cybersecurity. Learn how this almost become a huge threat and how we stopped it in its tracks.

In an increasingly interconnected digital landscape, the prevalence of critical vulnerabilities in internet-facing systems stands as an open invitation to malicious actors. These vulnerabilities serve as a near limitless resource, granting attackers a continually array of entry points into targeted networks.

In the final week of August 2023, Darktrace observed malicious actors validating exploits for one such critical vulnerability, likely the critical RCE vulnerability, CVE-2023-38035, on Ivanti Sentry servers within multiple customer networks. Shortly after these successful tests were carried out, malicious actors were seen delivering crypto-mining and reconnaissance tools onto vulnerable Ivanti Sentry servers.

Fortunately, Darktrace DETECT™ was able to identify this post-exploitation activity on the compromised servers at the earliest possible stage, allowing the customer security teams to take action against affected devices. In environments where Darktrace RESPOND™ was enabled in autonomous response mode, Darktrace was further able inhibit the identified post-exploitation activity and stop malicious actors from progressing towards their end goals.

Exploitation of Vulnerabilities in Ivanti Products

The software provider, Ivanti, offers a variety of widely used endpoint management, service management, and security solutions. In July and August 2023, the Norwegian cybersecurity company, Mnemonic, disclosed three vulnerabilities in Ivanti products [1]/[2]/[3]; two in Ivanti's endpoint management solution, Ivanti Endpoint Manager Mobile (EPMM) (formerly called 'MobileIron Core'), and one in Ivanti’s security gateway solution, Ivanti Sentry (formerly called 'MobileIron Sentry'):

CVE-2023-35078

CVSS Score: 10.0
Affected Product: Ivanti EPMM
Details from Ivanti: [4]/[5]/[6]
Vulnerability type: Authentication bypass

CVE-2023-35081

CVSS Score: 7.2
Affected Product: Ivanti EPMM
Details from Ivanti: [7]/[8]/[9]
Vulnerability type: Directory traversal

CVE-2023-38035

CVSS Score:
Affected Product: Ivanti Sentry
Details from Ivanti: [10]/[11]/[12]
Vulnerability type: Authentication bypass

At the beginning of August 2023, the Cybersecurity and Infrastructure Security Agency (CISA) and the Norwegian National Cyber Security Centre (NCSC-NO) provided details of advanced persistent threat (APT) activity targeting EPMM systems within Norwegian private sector and government networks via exploitation of CVE-2023-35078 combined with suspected exploitation of CVE-2023-35081.

In an article published in August 2023 [12], Ivanti disclosed that a very limited number of their customers had been subjected to exploitation of the Ivanti Sentry vulnerability, CVE-2023-38035, and on the August 22, 2023, CISA added the Ivanti Sentry vulnerability, CVE-2023-38035 to its ‘Known Exploited Vulnerabilities Catalogue’. CVE-2023-38035 is a critical authentication bypass vulnerability affecting the System Manager Portal of Ivanti Sentry systems. The System Manager Portal, which is accessible by default on port 8433, is used for administration of the Ivanti Sentry system. Through exploitation of CVE-2023-38035, an unauthenticated actor with access to the System Manager Portal can achieve Remote Code Execution (RCE) on the underlying Ivanti Sentry system.

Observed Exploitation of CVE-2023-38035

On August 24, Darktrace observed Ivanti Sentry servers within several customer networks receiving successful SSL connections over port 8433 from the external endpoint, 34.77.65[.]112. The usage of port 8433 indicates that the System Manager Portal was accessed over the connections. Immediately after receiving these successful connections, Ivanti Sentry servers made GET requests over port 4444 to 34.77.65[.]112. The unusual string ‘Wget/1.14 (linux-gnu)’ appeared in the User-Agent headers of these requests, indicating that the command-line utility, wget, was abused to initiate the requests.

Figure 1: Event Log data for an Ivanti Sentry system showing the device breaching a range of DETECT models after contacting 34.77.65[.]112.The suspicious behavior highlighted by DETECT was subsequently investigated by Darktrace’s Cyber AI Analyst™, which was able to weave together these separate behaviors into single incidents representing the whole attack chain.

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Figure 2: AI Analyst Incident representing a chain of suspicious activities from an Ivanti Sentry server.

In cases where Darktrace RESPOND was enabled in autonomous response mode, RESPOND was able to automatically enforce the Ivanti Sentry server’s normal pattern of life, thus blocking further exploit testing.

Figure 3: Event Log for an Ivanti Sentry server showing the device receiving a RESPOND action immediately after trying to 34.77.65[.]112.

The GET requests to 34.77.65[.]112 were responded to with the following HTML document:

Figure 4: Snapshot of the HTML document returned by 34.77.65[.]112.

None of the links within this HTML document were functional. Furthermore, the devices’ downloads of these HTML documents do not appear to have elicited further malicious activities. These facts suggest that the observed 34.77.65[.]112 activities were representative of a malicious actor validating exploits (likely for CVE-2023-38035) on Ivanti Sentry systems.

Over the next 24 hours, these Ivanti Sentry systems received successful SSL connections over port 8433 from a variety of suspicious external endpoints, such as 122.161.66[.]161. These connections resulted in Ivanti Sentry systems making HTTP GET requests to subdomains of ‘oast[.]site’ and ‘oast[.]live’. Strings containing ‘curl’ appeared in the User-Agent headers of these requests, indicating that the command-line utility, cURL, was abused to initiate the requests.

These ‘oast[.]site’ and ‘oast[.]live’ domains are used by the out-of-band application security testing (OAST) service, Interactsh. Malicious actors are known to abuse this service to carry out out-of-band (OOB) exploit testing. It, therefore, seems likely that these activities were also representative of a malicious actor validating exploits for CVE-2023-38035 on Ivanti Sentry systems.

Figure 5: Event Log for Ivanti Sentry system showing the device contacting an 'oast[.]site' endpoint after receiving connections from the suspicious, external endpoint 122.161.66[.]161.

The actors seen validating exploits for CVE-2023-38035 may have been conducting such activities in preparation for their own subsequent malicious activities. However, given the variety of attack chains which ensued from these exploit validation activities, it is also possible that they were carried out by Initial Access Brokers (IABs) The activities which ensued from exploit validation activities identified by Darktrace fell into two categories: internal network reconnaissance and cryptocurrency mining.

Reconnaissance Activities

In one of the reconnaissance cases, immediately after receiving successful SSL connections over port 8443 from the external endpoints 190.2.131[.]204 and 45.159.248[.]179, an Ivanti Sentry system was seen making a long SSL connection over port 443 to 23.92.29[.]148, and making wget GET requests over port 4444 with the Target URIs '/ncat' and ‘/TxPortMap’ to the external endpoints, 45.86.162[.]147 and 195.123.240[.]183.

Figure 6: Event Log data for an Ivanti Sentry system showing the device making connections to the external endpoints, 45.86.162[.]147, 23.92.29[.]148, and 195.123.240[.]183, immediately after receiving connections from rare external endpoints.

The Ivanti Sentry system then went on to scan for open SMB ports on systems within the internal network. This activity likely resulted from an attacker dropping a port scanning utility on the vulnerable Ivanti Sentry system.

Figure 7: Event Log data for an Ivanti Sentry server showing the device breaching several DETECT models after downloading a port scanning tool from 195.123.240[.]183.

In another reconnaissance case, Darktrace observed multiple wget HTTP requests with Target URIs such as ‘/awp.tar.gz’ and ‘/resp.tar.gz’ to a suspicious, external server (78.128.113[.]130). Shortly after making these requests, the Ivanti Sentry system started to scan for open SMB ports and to respond to LLMNR queries from other internal devices. These behaviors indicate that the server may have installed an LLMNR poisoning tool, such as Responder. The Ivanti Sentry server also went on to conduct further information-gathering activities, such as LDAP reconnaissance, HTTP-based vulnerability scanning, HTTP-based password searching, and RDP port scanning.

Figure 8: Event Log data for an Ivanti Sentry system showing the device making connections to 78.128.113[.]130, scanning for an open SMB port on internal endpoints, and responding to LLMNR queries from internal endpoints.

In cases where Darktrace RESPOND was active, reconnaissance activities resulted in RESPOND enforcing the Ivanti Sentry server’s pattern of life.

Figure 9: Event Log data for an Ivanti Sentry system receiving a RESPOND action as a result of its SMB port scanning activity.

Figure 10: Event Log data for an Ivanti Sentry system receiving a RESPOND action as a result of its LDAP reconnaissance activity.

Crypto-Mining Activities

In one of the cryptomining cases, Darktrace detected an Ivanti Sentry server making SSL connections to aelix[.]xyz and mining pool endpoints after receiving successful SSL connections over port 8443 from the external endpoint, 140.228.24[.]160.

Figure 11: Event Log data for an Ivanti Sentry system showing the device contacting aelix[.]xyz and mining pool endpoints immediately after receiving connections from the external endpoint, 140.228.24[.]160.

In a cryptomining case on another customer’s network, an Ivanti Sentry server was seen making GET requests indicative of Kinsing malware infection. These requests included wget GET requests to 185.122.204[.]197 with the Target URIs ‘/unk.sh’ and ‘/se.sh’ and a combination of GET and POST requests to 185.221.154[.]208 with the User-Agent header ‘Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36’ and the Target URIs, ‘/mg’, ‘/ki’, ‘/get’, ‘/h2’, ‘/ms’, and ‘/mu’. These network-based artefacts have been observed in previous Kinsing infections [13].

Figure 12: Event Log data for an Ivanti Sentry system showing the device displaying likely Kinsing C2 activity.

On customer environments where RESPOND was active, Darktrace was able to take swift autonomous action by blocking cryptomining connection attempts to malicious command-and-control (C2) infrastructure, in this case Kinsing servers.

Figure 13: Event Log data for an Ivanti Sentry server showing the device receiving a RESPOND action after attempting to contact Kinsing C2 infrastructure.

Fortunately, due to Darktrace DETECT+RESPOND prompt identification and targeted actions against these emerging threats, coupled with remediating steps taken by affected customers’ security teams, neither the cryptocurrency mining activities nor the network reconnaissance activities led to significant disruption.

Figure 14: Timeline of observed malicious activities.

Conclusion The inevitable presence of critical vulnerabilities in internet-facing systems underscores the perpetual challenge of defending against malicious intrusions. The near inexhaustible supply of entry routes into organizations’ networks available to malicious actors necessitates a more proactive and vigilant approach to network security.

While it is, of course, essential for organizations to secure their digital environments through the regular patching of software and keeping abreast of developing vulnerabilities that could impact their network, it is equally important to have a safeguard in place to mitigate against attackers who do manage to exploit newly discovered vulnerabilities.

In the case of Ivanti Sentry, Darktrace observed malicious actors validating exploits against affected servers on customer networks just a few days after the public disclosure of the critical vulnerability. This activity was followed up by a variety of malicious and disruptive, activities including cryptocurrency mining and internal network reconnaissance.

Darktrace DETECT immediately detected post-exploitation activities on compromised Ivanti Sentry servers, enabling security teams to intervene at the earliest possible stage. Darktrace RESPOND, when active, autonomously inhibited detected post-exploitation activities. These DETECT detections, along with their accompanying RESPOND interventions, prevented malicious actors from being able to progress further towards their likely harmful objectives.

Credit to Sam Lister, Senior Cyber Analyst, and Trent Kessler, SOC Analyst

‍

Appendices

MITRE ATT&CK Mapping

Initial Access techniques:

Exploit Public-Facing Application (T1190)

Credential Access techniques:

Unsecured Credentials: Credentials In Files (T1552.001)
Adversary-in-the-Middle: LLMNR/NBT-NS Poisoning and SMB Relay (T1557.001)

Discovery

Network Service Discovery (T1046)
Remote System Discovery (T1018)
Account Discovery: Domain Account (T1087.002)

Command and Control techniques:

Application Layer Protocol: Web Protocols (T1071.001)
Ingress Tool Transfer (T1105)
Non-Standard Port (T1571)
Encrypted Channel: Asymmetric Cryptography (T1573.002)

Impact techniques

Resource Hijacking (T1496)

List of IoCs

Exploit testing IoCs:

· 34.77.65[.]112

· Wget/1.14 (linux-gnu)

· cjjovo7mhpt7geo8aqlgxp7ypod6dqaiz.oast[.]site • 178.128.16[.]97

· curl/7.19.7 (x86_64-redhat-linux-gnu) libcurl/7.19.7 NSS/3.27.1 zlib/1.2.3 libidn/1.18 libssh2/1.4.2

· cjk45q1chpqflh938kughtrfzgwiofns3.oast[.]site • 178.128.16[.]97

· curl/7.29.0

Kinsing-related IoCs:

· 185.122.204[.]197

· /unk.sh

· /se.sh

· 185.221.154[.]208

· 185.221.154[.]208

· 45.15.158[.]124

· Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/99.0.4844.51 Safari/537.36

· /mg

· /ki

· /get

· /h2

· /ms

· /mu

· vocaltube[.]ru • 185.154.53[.]140

· 92.255.110[.]4

· 194.87.254[.]160

Responder-related IoCs:

· 78.128.113[.]130

· 78.128.113[.]34

· /awp.tar.gz

· /ivanty

· /resp.tar.gz

Crypto-miner related IoCs:

· 140.228.24[.]160

· aelix[.]xyz • 104.21.60[.]147 / 172.67.197[.]200

· c8446f59cca2149cb5f56ced4b448c8d (JA3 client fingerprint)

· b5eefe582e146aed29a21747a572e11c (JA3 client fingerprint)

· pool.supportxmr[.]com

· xmr.2miners[.]com

· xmr.2miners[.]com

· monerooceans[.]stream

· xmr-eu2.nanopool[.]org

Port scanner-related IoCs:

· 122.161.66[.]161

· 192.241.235[.]32

· 45.86.162[.]147

· /ncat

· Wget/1.14 (linux-gnu)

· 45.159.248[.]179

· 142.93.115[.]146

· 23.92.29[.]148

· /TxPortMap

· 195.123.240.183

· 6935a8d379e086ea1aed159b8abcb0bc8acf220bd1cbc0a84fd806f14014bca7 (SHA256 hash of downloaded file)

Darktrace DETECT Model Breaches

· Anomalous Server Activity / New User Agent from Internet Facing System

· Device / New User Agent

· Anomalous Connection / New User Agent to IP Without Hostname

· Device / New User Agent and New IP

· Anomalous Connection / Application Protocol on Uncommon Port

· Anomalous Connection / Callback on Web Facing Device

· Compromise / High Volume of Connections with Beacon Score

· Compromise / Large Number of Suspicious Failed Connections

· Compromise / High Volume of Connections with Beacon Score

· Compromise / Beacon for 4 Days

· Compromise / Agent Beacon (Short Period)

· Device / Large Number of Model Breaches

· Anomalous Server Activity / Rare External from Server

· Compromise / Large Number of Suspicious Successful Connections

· Compromise / Monero Mining

· Compromise / High Priority Crypto Currency Mining

· Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

· Device / Internet Facing Device with High Priority Alert

· Device / Suspicious SMB Scanning Activity

· Device / Internet Facing Device with High Priority Alert

· Device / Network Scan

· Device / Unusual LDAP Bind and Search Activity

· Compliance / Vulnerable Name Resolution

· Device / Anomalous SMB Followed By Multiple Model Breaches

· Device / New User Agent To Internal Server

· Anomalous Connection / Suspicious HTTP Activity

· Anomalous Connection / Unusual Internal Connections

· Anomalous Connection / Suspicious HTTP Activity

· Device / RDP Scan

· Device / Large Number of Model Breaches

· Compromise / Beaconing Activity To External Rare

· Compromise / Beacon to Young Endpoint

· Anomalous Connection / Suspicious HTTP Activity

· Compromise / Suspicious Internal Use Of Web Protocol

· Anomalous File / EXE from Rare External Location

· Anomalous File / Internet Facing System File Download

· Device / Suspicious SMB Scanning Activity

· Device / Internet Facing Device with High Priority Alert

· Device / Network Scan

· Device / Initial Breach Chain Compromise

References

[1] https://www.mnemonic.io/resources/blog/ivanti-endpoint-manager-mobile-epmm-authentication-bypass-vulnerability/
‍[2] https://www.mnemonic.io/resources/blog/threat-advisory-remote-file-write-vulnerability-in-ivanti-epmm/
‍[3] https://www.mnemonic.io/resources/blog/threat-advisory-remote-code-execution-vulnerability-in-ivanti-sentry/
‍[4] https://www.ivanti.com/blog/cve-2023-35078-new-ivanti-epmm-vulnerability
‍[5] https://forums.ivanti.com/s/article/CVE-2023-35078-Remote-unauthenticated-API-access-vulnerability?language=en_US
‍[6] https://forums.ivanti.com/s/article/KB-Remote-unauthenticated-API-access-vulnerability-CVE-2023-35078?language=en_US
‍[7] https://www.ivanti.com/blog/cve-2023-35081-new-ivanti-epmm-vulnerability
‍[8] https://forums.ivanti.com/s/article/CVE-2023-35081-Arbitrary-File-Write?language=en_US
‍[9] https://forums.ivanti.com/s/article/KB-Arbitrary-File-Write-CVE-2023-35081?language=en_US
‍[10] https://www.ivanti.com/blog/cve-2023-38035-vulnerability-affecting-ivanti-sentry
‍[11] https://forums.ivanti.com/s/article/CVE-2023-38035-API-Authentication-Bypass-on-Sentry-Administrator-Interface?language=en_US
‍[12] https://forums.ivanti.com/s/article/KB-API-Authentication-Bypass-on-Sentry-Administrator-Interface-CVE-2023-38035?language=en_US
‍[13] https://isc.sans.edu/diary/Your+Business+Data+and+Machine+Learning+at+Risk+Attacks+Against+Apache+NiFi/29900

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

Sam Lister

SOC 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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