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[Part 2] Typical Steps of a Raccoon Stealer v2 Infection

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08
Nov 2022
08
Nov 2022
Since the release of version 2 of Raccoon Stealer, Darktrace’s SOC has observed a surge in activity. See the typical steps used by this new threat!

Raccoon Stealer Malware

Since the release of version 2 of Raccoon Stealer in May 2022, Darktrace has observed huge volumes of Raccoon Stealer v2 infections across its client base. The info-stealer, which seeks to obtain and then exfiltrate sensitive data saved on users’ devices, displays a predictable pattern of network activity once it is executed. In this blog post, we will provide details of this pattern of activity, with the goal of helping security teams to recognize network-based signs of Raccoon Stealer v2 infection within their own networks. 

What is Raccoon Stealer?

Raccoon Stealer is a classic example of information-stealing malware, which cybercriminals typically use to gain possession of sensitive data saved in users’ browsers and cryptocurrency wallets. In the case of browsers, targeted data typically includes cookies, saved login details, and saved credit card details. In the case of cryptocurrency wallets (henceforth, ‘crypto-wallets’), targeted data typically includes public keys, private keys, and seed phrases [1]. Once sensitive browser and crypto-wallet data is in the hands of cybercriminals, it will likely be used to conduct harmful activities, such as identity theft, cryptocurrency theft, and credit card fraud.

How do you obtain Raccoon Stealer?

Like most info-stealers, Raccoon Stealer is purchasable. The operators of Raccoon Stealer sell Raccoon Stealer samples to their customers (called ‘affiliates’), who then use the info-stealer to gain possession of sensitive data saved on users’ devices. Raccoon Stealer affiliates typically distribute their samples via SEO-promoted websites providing free or cracked software. 

Is Raccoon Stealer Still Active?

On the 25th of March 2022, the operators of Raccoon Stealer announced that they would be suspending their operations because one of their core developers had been killed during the Russia-Ukraine conflict [2]. The presence of the hardcoded RC4 key ‘edinayarossiya’ (Russian for ‘United Russia’) within observed Raccoon Stealer v2 samples [3] provides potential evidence of the Raccoon Stealer operators’ allegiances.

Recent details shared by the US Department of Justice [4]/[5] indicate that it was in fact the arrest, rather than the death, of an operator which led the Raccoon Stealer team to suspend their operations [6]. As a result of the FBI, along with law enforcement partners in Italy and the Netherlands, dismantling Raccoon Stealer infrastructure in March 2022 [4], the Raccoon Stealer team was forced to build a new version of the info-stealer.  

On the 17th May 2022, the completion of v2 of the info-stealer was announced on the Raccoon Stealer Telegram channel [7].  Since its release in May 2022, Raccoon Stealer v2 has become extremely popular amongst cybercriminals. The prevalence of Raccoon Stealer v2 in the wider landscape has been reflected in Darktrace’s client base, with hundreds of infections being observed within client networks on a monthly basis.   

Since Darktrace’s SOC first saw a Raccoon Stealer v2 infection on the 22nd May 2022, the info-stealer has undergone several subtle changes. However, the info-stealer’s general pattern of network activity has remained essentially unchanged.  

How Does Raccoon Stealer v2 Infection Work?

A Raccoon Stealer v2 infection typically starts with a user attempting to download cracked or free software from an SEO-promoted website. Attempting to download software from one of these cracked/free software websites redirects the user’s browser (typically via several .xyz or .cfd endpoints) to a page providing download instructions. In May, June, and July, many of the patterns of download behavior observed by Darktrace’s SOC matched the pattern of behavior observed in a cracked software campaign reported by Avast in June [8].   

webpage whose download instructions led to a Raccoon Stealer v2
Figure 1: Above is a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on Discord CDN
example of a webpage whose download instructions led to a Raccoon Stealer v2
Figure 2: Above is an example of a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on Bitbucket
example of a webpage whose download instructions led to a Raccoon Stealer v2
Figure 3: Above is an example of a webpage whose download instructions led to a Raccoon Stealer v2 sample hosted on MediaFire

Following the instructions on the download instruction page causes the user’s device to download a password-protected RAR file from a file storage service such as ‘cdn.discordapp[.]com’, ‘mediafire[.]com’, ‘mega[.]nz’, or ‘bitbucket[.]org’. Opening the downloaded file causes the user’s device to execute Raccoon Stealer v2. 

The Event Log for an infected device,
Figure 4: The Event Log for an infected device, taken from Darktrace’s Threat Visualiser interface, shows a device contacting two cracked software websites (‘crackedkey[.]org’ and ‘crackedpc[.]co’) before contacting a webpage (‘premiumdownload[.]org) providing instructions to download Raccoon Stealer v2 from Bitbucket

Once Raccoon Stealer v2 is running on a device, it will make an HTTP POST request with the target URI ‘/’ and an unusual user-agent string (such as ‘record’, ‘mozzzzzzzzzzz’, or ‘TakeMyPainBack’) to a C2 server. This POST request consists of three strings: a machine GUID, a username, and a 128-bit RC4 key [9]. The posted data has the following form:

machineId=X | Y & configId=Z (where X is a machine GUID, Y is a username and Z is a 128-bit RC4 key) 

PCAP showing a device making an HTTP POST request with the User Agent header ‘record’ 
Figure 5:PCAP showing a device making an HTTP POST request with the User Agent header ‘record’ 
PCAP showing a device making an HTTP POST request with the User Agent header ‘mozzzzzzzzzzz’
Figure 6: PCAP showing a device making an HTTP POST request with the User Agent header ‘mozzzzzzzzzzz’
PCAP showing a device making an HTTP POST request with the User Agent header ‘TakeMyPainBack’
Figure 7: PCAP showing a device making an HTTP POST request with the User Agent header ‘TakeMyPainBack’

The C2 server responds to the info-stealer’s HTTP POST request with custom-formatted configuration details. These configuration details consist of fields which tell the info-stealer what files to download, what data to steal, and what target URI to use in its subsequent exfiltration POST requests. Below is a list of the fields Darktrace has observed in the configuration details retrieved by Raccoon Stealer v2 samples:

  • a ‘libs_mozglue’ field, which specifies a download address for a Firefox library named ‘mozglue.dll’
  • a ‘libs_nss3’ field, which specifies a download address for a Network System Services (NSS) library named ‘nss3.dll’ 
  • a ‘libs_freebl3’ field, which specifies a download address for a Network System Services (NSS) library named ‘freebl3.dll’
  • a ‘libs_softokn3’ field, which specifies a download address for a Network System Services (NSS) library named ‘softokn3.dll’
  • a ‘libs_nssdbm3’ field, which specifies a download address for a Network System Services (NSS) library named ‘nssdbm3.dll’
  • a ‘libs_sqlite3’ field, which specifies a download address for a SQLite command-line program named ‘sqlite3.dll’
  • a ‘libs_ msvcp140’ field, which specifies a download address for a Visual C++ runtime library named ‘msvcp140.dll’
  • a ‘libs_vcruntime140’ field, which specifies a download address for a Visual C++ runtime library named ‘vcruntime140.dll’
  • a ‘ldr_1’ field, which specifies the download address for a follow-up payload for the sample to download 
  • ‘wlts_X’ fields (where X is the name of a crypto-wallet application), which specify data for the sample to obtain from the specified crypto-wallet application
  • ‘ews_X’ fields (where X is the name of a crypto-wallet browser extension), which specify data for the sample to obtain from the specified browser extension
  • ‘xtntns_X’ fields (where X is the name of a password manager browser extension), which specify data for the sample to obtain from the specified browser extension
  • a ‘tlgrm_Telegram’ field, which specifies data for the sample to obtain from the Telegram Desktop application 
  • a ‘grbr_Desktop’ field, which specifies data within a local ‘Desktop’ folder for the sample to obtain 
  • a ‘grbr_Documents’ field, which specifies data within a local ‘Documents’ folder for the sample to obtain
  • a ‘grbr_Recent’ field, which specifies data within a local ‘Recent’ folder for the sample to obtain
  • a ‘grbr_Downloads’ field, which specifies data within a local ‘Downloads’ folder for the sample to obtain
  • a ‘sstmnfo_System Info.txt’ field, which specifies whether the sample should gather and exfiltrate a profile of the infected host 
  • a ‘scrnsht_Screenshot.jpeg’ field, which specifies whether the sample should take and exfiltrate screenshots of the infected host
  • a ‘token’ field, which specifies a 32-length string of hexadecimal digits for the sample to use as the target URI of its HTTP POST requests containing stolen data 

After retrieving its configuration data, Raccoon Stealer v2 downloads the library files specified in the ‘libs_’ fields. Unusual user-agent strings (such as ‘record’, ‘qwrqrwrqwrqwr’, and ‘TakeMyPainBack’) are used in the HTTP GET requests for these library files. In all Raccoon Stealer v2 infections seen by Darktrace, the paths of the URLs specified in the ‘libs_’ fields have the following form:

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/X (where X is the name of the targeted DLL file) 

Advanced Search logs for an infected host
Figure 8: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘record’ for DLL files
Advanced Search logs for an infected host
Figure 9: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘qwrqrwrqwrqwr’ for DLL files
Advanced Search logs for an infected host
Figure 10: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device making an HTTP POST request to retrieve configuration details, and then making HTTP GET requests with the User Agent header ‘TakeMyPainBack’ for DLL files

Raccoon Stealer v2 uses the DLLs which it downloads to gain access to sensitive data (such as cookies, credit card details, and login details) saved in browsers running on the infected host.  

Depending on the data provided in the configuration details, Raccoon Stealer v2 will typically seek to obtain, in addition to sensitive data saved in browsers, the following information:

  • Information about the Operating System and applications installed on the infected host
  • Data from specified crypto-wallet software
  • Data from specified crypto-wallet browser extensions
  • Data from specified local folders
  • Data from Telegram Desktop
  • Data from specified password manager browser extensions
  • Screenshots of the infected host 

Raccoon Stealer v2 exfiltrates the data which it obtains to its C2 server by making HTTP POST requests with unusual user-agent strings (such as ‘record’, ‘rc2.0/client’, ‘rqwrwqrqwrqw’, and ‘TakeMyPainBack’) and target URIs matching the 32-length string of hexadecimal digits specified in the ‘token’ field of the configuration details. The stolen data exfiltrated by Raccoon Stealer typically includes files named ‘System Info.txt’, ‘---Screenshot.jpeg’, ‘\cookies.txt’, and ‘\passwords.txt’. 

Advanced Search logs for an infected host
Figure 11: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating files named ‘System Info.txt’ and ‘---Screenshot.jpeg’
Advanced Search logs for an infected host
Figure 12: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating a file named ‘System Info.txt’ 
Advanced Search logs for an infected host
Figure 13: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating files named ‘System Info.txt’, ‘\cookies.txt’ and ‘\passwords.txt’
Advanced Search logs for an infected host
Figure 14: Advanced Search logs for an infected host, found on Darktrace’s Advanced Search interface, show a device retrieving configuration details via a POST request, downloading several DLLs, and then exfiltrating a file named ‘System Info.txt’

If a ‘ldr_1’ field is present in the retrieved configuration details, then Raccoon Stealer will complete its operation by downloading the binary file specified in the ‘ldr_1’ field. In all observed cases, the paths of the URLs specified in the ‘ldr_1’ field end in a sequence of digits, followed by ‘.bin’. The follow-up payload seems to vary between infections, likely due to this additional-payload feature being customizable by Raccoon Stealer affiliates. In many cases, the info-stealer, CryptBot, was delivered as the follow-up payload. 

Darktrace Coverage of Raccoon Stealer

Once a user’s device becomes infected with Raccoon Stealer v2, it will immediately start to communicate over HTTP with a C2 server. The HTTP requests made by the info-stealer have an empty Host header (although Host headers were used by early v2 samples) and highly unusual User Agent headers. When Raccoon Stealer v2 was first observed in May 2022, the user-agent string ‘record’ was used in its HTTP requests. Since then, it appears that the operators of Raccoon Stealer have made several changes to the user-agent strings used by the info-stealer,  likely in an attempt to evade signature-based detections. Below is a timeline of the changes to the info-stealer’s user-agent strings, as observed by Darktrace’s SOC:

  • 22nd May 2022: Samples seen using the user-agent string ‘record’
  • 2nd July 2022: Samples seen using the user-agent string ‘mozzzzzzzzzzz’
  • 29th July 2022: Samples seen using the user-agent string ‘rc2.0/client’
  • 10th August 2022: Samples seen using the user-agent strings ‘qwrqrwrqwrqwr’ and ‘rqwrwqrqwrqw’
  • 16th Sep 2022: Samples seen using the user-agent string ‘TakeMyPainBack’

The presence of these highly unusual user-agent strings within infected devices’ HTTP requests causes the following Darktrace DETECT/Network models to breach:

  • Device / New User Agent
  • Device / New User Agent and New IP
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Device / Three or More New User Agents

These DETECT models look for devices making HTTP requests with unusual user-agent strings, rather than specific user-agent strings which are known to be malicious. This method of detection enables the models to continually identify Raccoon Stealer v2 HTTP traffic, despite the changes made to the info-stealer’s user-agent strings.   

After retrieving configuration details from a C2 server, Raccoon Stealer v2 samples make HTTP GET requests for several DLL libraries. Since these GET requests are directed towards highly unusual IP addresses, the downloads of the DLLs cause the following DETECT models to breach:

  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Script from Rare External Location
  • Anomalous File / Multiple EXE from Rare External Locations

Raccoon Stealer v2 samples send data to their C2 server via HTTP POST requests with an absent Host header. Since these POST requests lack a Host header and have a highly unusual destination IP, their occurrence causes the following DETECT model to breach:

  • Anomalous Connection / Posting HTTP to IP Without Hostname

Certain Raccoon Stealer v2 samples download (over HTTP) a follow-up payload once they have exfiltrated data. Since the target URIs of the HTTP GET requests made by v2 samples end in a sequence of digits followed by ‘.bin’, the samples’ downloads of follow-up payloads cause the following DETECT model to breach:

  • Anomalous File / Numeric File Download

If Darktrace RESPOND/Network is configured within a customer’s environment, then Raccoon Stealer v2 activity should cause the following inhibitive actions to be autonomously taken on infected systems: 

  • Enforce pattern of life — This action results in a device only being able to make connections which are normal for it to make
  • Enforce group pattern of life — This action results in a device only being able to make connections which are normal for it or any of its peers to make
  • Block matching connections — This action results in a device being unable to make connections to particular IP/Port pairs
  • Block all outgoing traffic — This action results in a device being unable to make any connections 
The Event Log for an infected device
Figure 15: The Event Log for an infected device, taken from Darktrace’s Threat Visualiser interface, shows Darktrace RESPOND taking inhibitive actions in response to the HTTP activities of a Raccoon Stealer v2 sample downloaded from MediaFire

Given that Raccoon Stealer v2 infections move extremely fast, with the time between initial infection and data exfiltration sometimes less than a minute, the availability of Autonomous Response technology such as Darktrace RESPOND is vital for the containment of Raccoon Stealer v2 infections.  

Timeline of Darktrace stopping raccoon stealer.
Figure 16: Figure displaying the steps of a Raccoon Stealer v2 infection, along with the corresponding Darktrace detections

Conclusion

Since the release of Raccoon Stealer v2 back in 2022, the info-stealer has relentlessly infected the devices of unsuspecting users. Once the info-stealer infects a user’s device, it retrieves and then exfiltrates sensitive information within a matter of minutes. The distinctive pattern of network behavior displayed by Raccoon Stealer v2 makes the info-stealer easy to spot. However, the changes which the Raccoon Stealer operators make to the User Agent headers of the info-stealer’s HTTP requests make anomaly-based methods key for the detection of the info-stealer’s HTTP traffic. The operators of Raccoon Stealer can easily change the superficial features of their malware’s C2 traffic, however, they cannot easily change the fact that their malware causes highly unusual network behavior. Spotting this behavior, and then autonomously responding to it, is likely the best bet which organizations have at stopping a Raccoon once it gets inside their networks.  

Thanks to the Threat Research Team for its contributions to this blog.

References

[1] https://www.microsoft.com/security/blog/2022/05/17/in-hot-pursuit-of-cryware-defending-hot-wallets-from-attacks/

[2] https://twitter.com/3xp0rtblog/status/1507312171914461188

[3] https://www.esentire.com/blog/esentire-threat-intelligence-malware-analysis-raccoon-stealer-v2-0

[4] https://www.justice.gov/usao-wdtx/pr/newly-unsealed-indictment-charges-ukrainian-national-international-cybercrime-operation

[5] https://www.youtube.com/watch?v=Fsz6acw-ZJ

[6] https://riskybiznews.substack.com/p/raccoon-stealer-dev-didnt-die-in

[7] https://medium.com/s2wblog/raccoon-stealer-is-back-with-a-new-version-5f436e04b20d

[8] https://blog.avast.com/fakecrack-campaign

[9] https://blog.sekoia.io/raccoon-stealer-v2-part-2-in-depth-analysis/

Appendices

MITRE ATT&CK Mapping

Resource Development

• T1588.001 — Obtain Capabilities: Malware

• T1608.001 — Stage Capabilities: Upload Malware

• T1608.005 — Stage Capabilities: Link Target

• T1608.006 — Stage Capabilities: SEO Poisoning

Execution

•  T1204.002 — User Execution: Malicious File

Credential Access

• T1555.003 — Credentials from Password Stores:  Credentials from Web Browsers

• T1555.005 — Credentials from Password Stores:  Password Managers

• T1552.001 — Unsecured Credentials: Credentials  In Files

Command and Control

•  T1071.001 — Application Layer Protocol: Web Protocols

•  T1105 — Ingress Tool Transfer

IOCS

Tipo

IOC

Description

User-Agent String

record

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

mozzzzzzzzzzz

String used inUser Agent header of Raccoon Stealer v2’s HTTP requests

User-Agent String

rc2.0/client

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

qwrqrwrqwrqwr

String used in  User Agent header of Raccoon Stealer v2’s HTTP requests

User-Agent String

rqwrwqrqwrqw

String used in User Agent header of  Raccoon Stealer v2’s HTTP requests

User-Agent  String

TakeMyPainBack

String used in  User Agent header of Raccoon Stealer v2’s HTTP requests

Domain Name

brain-lover[.]xyz  

Raccoon Stealer v2 C2 infrastructure

Domain  Name

polar-gift[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

cool-story[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

fall2sleep[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

broke-bridge[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

use-freedom[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

just-trust[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

soft-viper[.]site

Raccoon Stealer  v2 C2 infrastructure

Domain Name

tech-lover[.]xyz

Raccoon Stealer v2 C2 infrastructure

Domain  Name

heal-brain[.]xyz

Raccoon Stealer  v2 C2 infrastructure

Domain Name

love-light[.]xyz

Raccoon Stealer v2 C2 infrastructure

IP  Address

104.21.80[.]14

Raccoon Stealer  v2 C2 infrastructure

IP Address

107.152.46[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

135.181.147[.]255

Raccoon Stealer  v2 C2 infrastructure

IP Address

135.181.168[.]157

Raccoon Stealer v2 C2 infrastructure

IP  Address

138.197.179[.]146

Raccoon Stealer  v2 C2 infrastructure

IP Address

141.98.169[.]33

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.170[.]100

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.170[.]175

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.170[.]98

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.173[.]33

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.173[.]72

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.19.247[.]175

Raccoon Stealer v2 C2 infrastructure

IP  Address

146.19.247[.]177

Raccoon Stealer  v2 C2 infrastructure

IP Address

146.70.125[.]95

Raccoon Stealer v2 C2 infrastructure

IP  Address

152.89.196[.]234

Raccoon Stealer  v2 C2 infrastructure

IP Address

165.225.120[.]25

Raccoon Stealer v2 C2 infrastructure

IP  Address

168.100.10[.]238

Raccoon Stealer  v2 C2 infrastructure

IP Address

168.100.11[.]23

Raccoon Stealer v2 C2 infrastructure

IP  Address

168.100.9[.]234

Raccoon Stealer  v2 C2 infrastructure

IP Address

170.75.168[.]118

Raccoon Stealer v2 C2 infrastructure

IP  Address

172.67.173[.]14

Raccoon Stealer  v2 C2 infrastructure

IP Address

172.86.75[.]189

Raccoon Stealer v2 C2 infrastructure

IP  Address

172.86.75[.]33

Raccoon Stealer  v2 C2 infrastructure

IP Address

174.138.15[.]216

Raccoon Stealer v2 C2 infrastructure

IP  Address

176.124.216[.]15

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.106.92[.]14

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.173.34[.]161

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.173.34[.]161  

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.225.17[.]198

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.225.19[.]190

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.225.19[.]229

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.53.46[.]103

Raccoon Stealer v2 C2 infrastructure

IP  Address

185.53.46[.]76

Raccoon Stealer  v2 C2 infrastructure

IP Address

185.53.46[.]77

Raccoon Stealer v2 C2 infrastructure

IP  Address

188.119.112[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

190.117.75[.]91

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.106.191[.]182

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.149.129[.]135

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.149.129[.]144

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.149.180[.]210

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.149.185[.]192

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.233.193[.]50

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]17

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]192

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]213

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]214

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]215

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.43.146[.]26

Raccoon Stealer  v2 C2 infrastructure

IP Address

193.43.146[.]45

Raccoon Stealer v2 C2 infrastructure

IP  Address

193.56.146[.]177

Raccoon Stealer  v2 C2 infrastructure

IP Address

194.180.174[.]180

Raccoon Stealer v2 C2 infrastructure

IP  Address

195.201.148[.]250

Raccoon Stealer  v2 C2 infrastructure

IP Address

206.166.251[.]156

Raccoon Stealer v2 C2 infrastructure

IP  Address

206.188.196[.]200

Raccoon Stealer  v2 C2 infrastructure

IP Address

206.53.53[.]18

Raccoon Stealer v2 C2 infrastructure

IP  Address

207.154.195[.]173

Raccoon Stealer  v2 C2 infrastructure

IP Address

213.252.244[.]2

Raccoon Stealer v2 C2 infrastructure

IP  Address

38.135.122[.]210

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.10.20[.]248

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.11.19[.]99

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]110

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.133.216[.]145

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]148

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.133.216[.]249

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.133.216[.]71

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.140.146[.]169

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.140.147[.]245

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.212[.]100

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.142.213[.]24

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.215[.]91

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.142.215[.]91  

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.142.215[.]92

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.144.29[.]18

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.144.29[.]243

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.15.156[.]11

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.15.156[.]2

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.15.156[.]31

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.15.156[.]31

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.150.67[.]156

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.153.230[.]183

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.153.230[.]228

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.159.251[.]163

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.159.251[.]164

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.61.136[.]67

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.61.138[.]162

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.67.228[.]8

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.67.231[.]202

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.67.34[.]152

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.67.34[.]234

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.144[.]187

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.144[.]54

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.144[.]55

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.145[.]174

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.145[.]83

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.8.147[.]39

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.8.147[.]79

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.84.0.152

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.86.86[.]78

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.54[.]110

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.54[.]110

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.54[.]95

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]115

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]117

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]193

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]198

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.89.55[.]20

Raccoon Stealer  v2 C2 infrastructure

IP Address

45.89.55[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

45.92.156[.]150

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]154

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.36[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]231

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.36[.]232

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.36[.]233

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.39[.]34

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.39[.]74

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.182.39[.]75

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.182.39[.]77

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.118[.]33

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.176[.]62

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]217

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]234

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]43

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]47

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.177[.]92

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.177[.]98

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.22[.]142

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.23[.]100

Raccoon Stealer v2 C2 infrastructure

IP  Address

5.252.23[.]25

Raccoon Stealer  v2 C2 infrastructure

IP Address

5.252.23[.]76

Raccoon Stealer v2 C2 infrastructure

IP  Address

51.195.166[.]175

Raccoon Stealer  v2 C2 infrastructure

IP Address

51.195.166[.]176

Raccoon Stealer v2 C2 infrastructure

IP  Address

51.195.166[.]194

Raccoon Stealer  v2 C2 infrastructure

IP Address

51.81.143[.]169

Raccoon Stealer v2 C2 infrastructure

IP  Address

62.113.255[.]110

Raccoon Stealer  v2 C2 infrastructure

IP Address

65.109.3[.]107

Raccoon Stealer v2 C2 infrastructure

IP  Address

74.119.192[.]56

Raccoon Stealer  v2 C2 infrastructure

IP Address

74.119.192[.]73

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.232.39[.]101

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.73.133[.]0

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.73.133[.]4

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.73.134[.]45

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.75.230[.]25

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.75.230[.]39

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.75.230[.]70

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.75.230[.]93

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.100[.]101

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]12

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.102[.]230

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]44

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.102[.]57

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.102[.]84

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.103[.]31

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.73[.]154

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.73[.]213

Raccoon Stealer  v2 C2 infrastructure

IP Address

77.91.73[.]32

Raccoon Stealer v2 C2 infrastructure

IP  Address

77.91.74[.]67

Raccoon Stealer  v2 C2 infrastructure

IP Address

78.159.103[.]195

Raccoon Stealer v2 C2 infrastructure

IP  Address

78.159.103[.]196

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.66.87[.]23

Raccoon Stealer v2 C2 infrastructure

IP  Address

80.66.87[.]28

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.71.157[.]112

Raccoon Stealer v2 C2 infrastructure

IP  Address

80.71.157[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

80.92.204[.]202

Raccoon Stealer v2 C2 infrastructure

IP  Address

87.121.52[.]10

Raccoon Stealer  v2 C2 infrastructure

IP Address

88.119.175[.]187

Raccoon Stealer v2 C2 infrastructure

IP  Address

89.185.85[.]53

Raccoon Stealer  v2 C2 infrastructure

IP Address

89.208.107[.]42

Raccoon Stealer v2 C2 infrastructure

IP  Address

89.39.106[.]78

Raccoon Stealer  v2 C2 infrastructure

IP Address

91.234.254[.]126

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.104[.]16

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.104[.]17

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.104[.]18

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.106[.]116

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.106[.]224

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.107[.]132

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.107[.]138

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.96[.]109

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.97[.]129

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.97[.]53

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.97[.]56

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.131.97[.]57

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.131.98[.]5

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.244[.]114

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.244[.]119

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.244[.]21

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.247[.]24

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.247[.]26

Raccoon Stealer v2 C2 infrastructure

IP  Address

94.158.247[.]30

Raccoon Stealer  v2 C2 infrastructure

IP Address

94.158.247[.]44

Raccoon Stealer v2 C2 infrastructure

IP  Address

95.216.109[.]16

Raccoon Stealer  v2 C2 infrastructure

IP Address

95.217.124[.]179

Raccoon Stealer v2 C2 infrastructure

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/mozglue.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/nss3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/freebl3.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/softokn3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/nssdbm3.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/sqlite3.dll

URI used in download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/msvcp140.dll

URI used in  download of library file

URI

/aN7jD0qO6kT5bK5bQ4eR8fE1xP7hL2vK/vcruntime140.dll

URI used in download of library file

URI

/C9S2G1K6I3G8T3X7/56296373798691245143.bin

URI used in  download of follow-up payload

URI

/O6K3E4G6N9S8S1/91787438215733789009.bin

URI used in download of follow-up  payload

URI

/Z2J8J3N2S2Z6X2V3S0B5/45637662345462341.bin

URI used in  download of follow-up payload

URI

/rgd4rgrtrje62iuty/19658963328526236.bin

URI used in download of follow-up  payload

URI

/sd325dt25ddgd523/81852849956384.bin

URI used in  download of follow-up payload

URI

/B0L1N2H4R1N5I5S6/40055385413647326168.bin

URI used in download of follow-up  payload

URI

/F5Q8W3O3O8I2A4A4B8S8/31427748106757922101.bin

URI used in  download of follow-up payload

URI

/36141266339446703039.bin

URI used in download of follow-up  payload

URI

/wH0nP0qH9eJ6aA9zH1mN/1.bin

URI used in  download of follow-up payload

URI

/K2X2R1K4C6Z3G8L0R1H0/68515718711529966786.bin

URI used in download of follow-up  payload

URI

/C3J7N6F6X3P8I0I0M/17819203282122080878.bin

URI used in  download of follow-up payload

URI

/W9H1B8P3F2J2H2K7U1Y7G5N4C0Z4B/18027641.bin

URI used in download of follow-up  payload

URI

/P2T9T1Q6P7Y5J3D2T0N0O8V/73239348388512240560937.bin

URI used in  download of follow-up payload

URI

/W5H6O5P0E4Y6P8O1B9D9G0P9Y9G4/671837571800893555497.bin

URI used in download of follow-up  payload

URI

/U8P2N0T5R0F7G2J0/898040207002934180145349.bin

URI used in  download of follow-up payload

URI

/AXEXNKPSBCKSLMPNOMNRLUEPR/3145102300913020.bin

URI used in download of follow-up  payload

URI

/wK6nO2iM9lE7pN7e/7788926473349244.bin

URI used in  download of follow-up payload

URI

/U4N9B5X5F5K2A0L4L4T5/84897964387342609301.bin

URI used in download of follow-up  payload

DENTRO DEL SOC
Darktrace son expertos de talla mundial en inteligencia de amenazas, caza de amenazas y respuesta a incidentes, y proporcionan apoyo al SOC las 24 horas del día a miles de clientes de Darktrace en todo el mundo. Inside the SOC está redactado exclusivamente por estos expertos y ofrece un análisis de los ciberincidentes y las tendencias de las amenazas, basado en la experiencia real sobre el terreno.
AUTOR
SOBRE EL AUTOR
Sam Lister
Analista SOC
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Dentro del SOC

A Thorn in Attackers’ Sides: How Darktrace Uncovered a CACTUS Ransomware Infection

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24
Apr 2024

What is CACTUS Ransomware?

In May 2023, Kroll Cyber Threat Intelligence Analysts identified CACTUS as a new ransomware strain that had been actively targeting large commercial organizations since March 2023 [1]. CACTUS ransomware gets its name from the filename of the ransom note, “cAcTuS.readme.txt”. Encrypted files are appended with the extension “.cts”, followed by a number which varies between attacks, e.g. “.cts1” and “.cts2”.

As the cyber threat landscape adapts to ever-present fast-paced technological change, ransomware affiliates are employing progressively sophisticated techniques to enter networks, evade detection and achieve their nefarious goals.

How does CACTUS Ransomware work?

In the case of CACTUS, threat actors have been seen gaining initial network access by exploiting Virtual Private Network (VPN) services. Once inside the network, they may conduct internal scanning using tools like SoftPerfect Network Scanner, and PowerShell commands to enumerate endpoints, identify user accounts, and ping remote endpoints. Persistence is maintained by the deployment of various remote access methods, including legitimate remote access tools like Splashtop, AnyDesk, and SuperOps RMM in order to evade detection, along with malicious tools like Cobalt Strike and Chisel. Such tools, as well as custom scripts like TotalExec, have been used to disable security software to distribute the ransomware binary. CACTUS ransomware is unique in that it adopts a double-extortion tactic, stealing data from target networks and then encrypting it on compromised systems [2].

At the end of November 2023, cybersecurity firm Arctic Wolf reported instances of CACTUS attacks exploiting vulnerabilities on the Windows version of the business analytics platform Qlik, specifically CVE-2023-41266, CVE-2023-41265, and CVE-2023-48365, to gain initial access to target networks [3]. The vulnerability tracked as CVE-2023-41266 can be exploited to generate anonymous sessions and perform HTTP requests to unauthorized endpoints, whilst CVE-2023-41265 does not require authentication and can be leveraged to elevate privileges and execute HTTP requests on the backend server that hosts the application [2].

Darktrace’s Coverage of CACTUS Ransomware

In November 2023, Darktrace observed malicious actors leveraging the aforementioned method of exploiting Qlik to gain access to the network of a customer in the US, more than a week before the vulnerability was reported by external researchers.

Here, Qlik vulnerabilities were successfully exploited, and a malicious executable (.exe) was detonated on the network, which was followed by network scanning and failed Kerberos login attempts. The attack culminated in the encryption of numerous files with extensions such as “.cts1”, and SMB writes of the ransom note “cAcTuS.readme.txt” to multiple internal devices, all of which was promptly identified by Darktrace DETECT™.

While traditional rules and signature-based detection tools may struggle to identify the malicious use of a legitimate business platform like Qlik, Darktrace’s Self-Learning AI was able to confidently identify anomalous use of the tool in a CACTUS ransomware attack by examining the rarity of the offending device’s surrounding activity and comparing it to the learned behavior of the device and its peers.

Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled in autonomous response mode during their encounter with CACTUS ransomware meaning that attackers were able to successfully escalate their attack to the point of ransomware detonation and file encryption. Had RESPOND been configured to autonomously act on any unusual activity, Darktrace could have prevented the attack from progressing, stopping the download of any harmful files, or the encryption of legitimate ones.

Cactus Ransomware Attack Overview

Holiday periods have increasingly become one of the favoured times for malicious actors to launch their attacks, as they can take advantage of the festive downtime of organizations and their security teams, and the typically more relaxed mindset of employees during this period [4].

Following this trend, in late November 2023, Darktrace began detecting anomalous connections on the network of a customer in the US, which presented multiple indicators of compromise (IoCs) and tactics, techniques and procedures (TTPs) associated with CACTUS ransomware. The threat actors in this case set their attack in motion by exploiting the Qlik vulnerabilities on one of the customer’s critical servers.

Darktrace observed the server device making beaconing connections to the endpoint “zohoservice[.]net” (IP address: 45.61.147.176) over the course of three days. This endpoint is known to host a malicious payload, namely a .zip file containing the command line connection tool PuttyLink [5].

Darktrace’s Cyber AI Analyst was able to autonomously identify over 1,000 beaconing connections taking place on the customer’s network and group them together, in this case joining the dots in an ongoing ransomware attack. AI Analyst recognized that these repeated connections to highly suspicious locations were indicative of malicious command-and-control (C2) activity.

Cyber AI Analyst Incident Log showing the offending device making over 1,000 connections to the suspicious hostname “zohoservice[.]net” over port 8383, within a specific period.
Figure 1: Cyber AI Analyst Incident Log showing the offending device making over 1,000 connections to the suspicious hostname “zohoservice[.]net” over port 8383, within a specific period.

The infected device was then observed downloading the file “putty.zip” over a HTTP connection using a PowerShell user agent. Despite being labelled as a .zip file, Darktrace’s detection capabilities were able to identify this as a masqueraded PuttyLink executable file. This activity resulted in multiple Darktrace DETECT models being triggered. These models are designed to look for suspicious file downloads from endpoints not usually visited by devices on the network, and files whose types are masqueraded, as well as the anomalous use of PowerShell. This behavior resembled previously observed activity with regards to the exploitation of Qlik Sense as an intrusion technique prior to the deployment of CACTUS ransomware [5].

The downloaded file’s URI highlighting that the file type (.exe) does not match the file's extension (.zip). Information about the observed PowerShell user agent is also featured.
Figure 2: The downloaded file’s URI highlighting that the file type (.exe) does not match the file's extension (.zip). Information about the observed PowerShell user agent is also featured.

Following the download of the masqueraded file, Darktrace observed the initial infected device engaging in unusual network scanning activity over the SMB, RDP and LDAP protocols. During this activity, the credential, “service_qlik” was observed, further indicating that Qlik was exploited by threat actors attempting to evade detection. Connections to other internal devices were made as part of this scanning activity as the attackers attempted to move laterally across the network.

Numerous failed connections from the affected server to multiple other internal devices over port 445, indicating SMB scanning activity.
Figure 3: Numerous failed connections from the affected server to multiple other internal devices over port 445, indicating SMB scanning activity.

The compromised server was then seen initiating multiple sessions over the RDP protocol to another device on the customer’s network, namely an internal DNS server. External researchers had previously observed this technique in CACTUS ransomware attacks where an RDP tunnel was established via Plink [5].

A few days later, on November 24, Darktrace identified over 20,000 failed Kerberos authentication attempts for the username “service_qlik” being made to the internal DNS server, clearly representing a brute-force login attack. There is currently a lack of open-source intelligence (OSINT) material definitively listing Kerberos login failures as part of a CACTUS ransomware attack that exploits the Qlik vulnerabilities. This highlights Darktrace’s ability to identify ongoing threats amongst unusual network activity without relying on existing threat intelligence, emphasizing its advantage over traditional security detection tools.

Kerberos login failures being carried out by the initial infected device. The destination device detected was an internal DNS server.
Figure 4: Kerberos login failures being carried out by the initial infected device. The destination device detected was an internal DNS server.

In the month following these failed Kerberos login attempts, between November 26 and December 22, Darktrace observed multiple internal devices encrypting files within the customer’s environment with the extensions “.cts1” and “.cts7”. Devices were also seen writing ransom notes with the file name “cAcTuS.readme.txt” to two additional internal devices, as well as files likely associated with Qlik, such as “QlikSense.pdf”. This activity detected by Darktrace confirmed the presence of a CACTUS ransomware infection that was spreading across the customer’s network.

The model, 'Ransom or Offensive Words Written to SMB', triggered in response to SMB file writes of the ransom note, ‘cAcTuS.readme.txt’, that was observed on the customer’s network.
Figure 5: The model, 'Ransom or Offensive Words Written to SMB', triggered in response to SMB file writes of the ransom note, ‘cAcTuS.readme.txt’, that was observed on the customer’s network.
CACTUS ransomware extensions, “.cts1” and “.cts7”, being appended to files on the customer’s network.
Figure 6: CACTUS ransomware extensions, “.cts1” and “.cts7”, being appended to files on the customer’s network.

Following this initial encryption activity, two affected devices were observed attempting to remove evidence of this activity by deleting the encrypted files.

Attackers attempting to remove evidence of their activity by deleting files with appendage “.cts1”.
Figure 7: Attackers attempting to remove evidence of their activity by deleting files with appendage “.cts1”.

Conclusion

In the face of this CACTUS ransomware attack, Darktrace’s anomaly-based approach to threat detection enabled it to quickly identify multiple stages of the cyber kill chain occurring in the customer’s environment. These stages ranged from ‘initial access’ by exploiting Qlik vulnerabilities, which Darktrace was able to detect before the method had been reported by external researchers, to ‘actions on objectives’ by encrypting files. Darktrace’s Self-Learning AI was also able to detect a previously unreported stage of the attack: multiple Kerberos brute force login attempts.

If Darktrace’s autonomous response capability, RESPOND, had been active and enabled in autonomous response mode at the time of this attack, it would have been able to take swift mitigative action to shut down such suspicious activity as soon as it was identified by DETECT, effectively containing the ransomware attack at the earliest possible stage.

Learning a network’s ‘normal’ to identify deviations from established patterns of behaviour enables Darktrace’s identify a potential compromise, even one that uses common and often legitimately used administrative tools. This allows Darktrace to stay one step ahead of the increasingly sophisticated TTPs used by ransomware actors.

Credit to Tiana Kelly, Cyber Analyst & Analyst Team Lead, Anna Gilbertson, Cyber Analyst

Appendices

References

[1] https://www.kroll.com/en/insights/publications/cyber/cactus-ransomware-prickly-new-variant-evades-detection

[2] https://www.bleepingcomputer.com/news/security/cactus-ransomware-exploiting-qlik-sense-flaws-to-breach-networks/

[3] https://explore.avertium.com/resource/new-ransomware-strains-cactus-and-3am

[4] https://www.soitron.com/cyber-attackers-abuse-holidays/

[5] https://arcticwolf.com/resources/blog/qlik-sense-exploited-in-cactus-ransomware-campaign/

Darktrace DETECT Models

Compromise / Agent Beacon (Long Period)

Anomalous Connection / PowerShell to Rare External

Device / New PowerShell User Agent

Device / Suspicious SMB Scanning Activity

Anomalous File / EXE from Rare External Location

Anomalous Connection / Unusual Internal Remote Desktop

User / Kerberos Password Brute Force

Compromise / Ransomware / Ransom or Offensive Words Written to SMB

Unusual Activity / Anomalous SMB Delete Volume

Anomalous Connection / Multiple Connections to New External TCP Port

Compromise / Slow Beaconing Activity To External Rare  

Compromise / SSL Beaconing to Rare Destination  

Anomalous Server Activity / Rare External from Server  

Compliance / Remote Management Tool On Server

Compromise / Agent Beacon (Long Period)  

Compromise / Suspicious File and C2  

Device / Internet Facing Device with High Priority Alert  

Device / Large Number of Model Breaches  

Anomalous File / Masqueraded File Transfer

Anomalous File / Internet facing System File Download  

Anomalous Server Activity / Outgoing from Server

Device / Initial Breach Chain Compromise  

Compromise / Agent Beacon (Medium Period)  

Compromise / Agent Beacon (Long Period)  

List of IoCs

IoC - Type - Description

zohoservice[.]net: 45.61.147[.]176 - Domain name: IP Address - Hosting payload over HTTP

Mozilla/5.0 (Windows NT; Windows NT 10.0; en-US) WindowsPowerShell/5.1.17763.2183 - User agent -PowerShell user agent

.cts1 - File extension - Malicious appendage

.cts7- File extension - Malicious appendage

cAcTuS.readme.txt - Filename -Ransom note

putty.zip – Filename - Initial payload: ZIP containing PuTTY Link

MITRE ATT&CK Mapping

Tactic - Technique  - SubTechnique

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

Powershell: EXECUTION - T1059 - T1059.001

Exploitation of Remote Services: LATERAL MOVEMENT - T1210 – N/A

Vulnerability Scanning: RECONAISSANCE     - T1595 - T1595.002

Network Service Scanning: DISCOVERY - T1046 - N/A

Malware: RESOURCE DEVELOPMENT - T1588 - T1588.001

Drive-by Compromise: INITIAL ACCESS - T1189 - N/A

Remote Desktop Protocol: LATERAL MOVEMENT – 1021 -T1021.001

Brute Force: CREDENTIAL ACCESS        T – 1110 - N/A

Data Encrypted for Impact: IMPACT - T1486 - N/A

Data Destruction: IMPACT - T1485 - N/A

File Deletion: DEFENSE EVASION - T1070 - T1070.004

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About the author
Tiana Kelly
Deputy Team Lead, London & Cyber Analyst

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The State of AI in Cybersecurity: How AI will impact the cyber threat landscape in 2024

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22
Apr 2024

About the AI Cybersecurity Report

We 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 is continuing the conversation from our last blog post “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on the cyber threat landscape.

To access the full report click here.

Are organizations feeling the impact of AI-powered cyber threats?

Nearly three-quarters (74%) state AI-powered threats are now a significant issue. Almost nine in ten (89%) agree that AI-powered threats will remain a major challenge into the foreseeable future, not just for the next one to two years.

However, only a slight majority (56%) thought AI-powered threats were a separate issue from traditional/non AI-powered threats. This could be the case because there are few, if any, reliable methods to determine whether an attack is AI-powered.

Identifying exactly when and where AI is being applied may not ever be possible. However, it is possible for AI to affect every stage of the attack lifecycle. As such, defenders will likely need to focus on preparing for a world where threats are unique and are coming faster than ever before.

a hypothetical cyber attack augmented by AI at every stage

Are security stakeholders concerned about AI’s impact on cyber threats and risks?

The results from our survey showed that security practitioners are concerned that AI will impact organizations in a variety of ways. There was equal concern associated across the board – from volume and sophistication of malware to internal risks like leakage of proprietary information from employees using generative AI tools.

What this tells us is that defenders need to prepare for a greater volume of sophisticated attacks and balance this with a focus on cyber hygiene to manage internal risks.

One example of a growing internal risks is shadow AI. It takes little effort for employees to adopt publicly-available text-based generative AI systems to increase their productivity. This opens the door to “shadow AI”, which is the use of popular AI tools without organizational approval or oversight. Resulting security risks such as inadvertent exposure of sensitive information or intellectual property are an ever-growing concern.

Are organizations taking strides to reduce risks associated with adoption of AI in their application and computing environment?

71.2% of survey participants say their organization has taken steps specifically to reduce the risk of using AI within its application and computing environment.

16.3% of survey participants claim their organization has not taken these steps.

These findings are good news. Even as enterprises compete to get as much value from AI as they can, as quickly as possible, they’re tempering their eager embrace of new tools with sensible caution.

Still, responses varied across roles. Security analysts, operators, administrators, and incident responders are less likely to have said their organizations had taken AI risk mitigation steps than respondents in other roles. In fact, 79% of executives said steps had been taken, and only 54% of respondents in hands-on roles agreed. It seems that leaders believe their organizations are taking the needed steps, but practitioners are seeing a gap.

Do security professionals feel confident in their preparedness for the next generation of threats?

A majority of respondents (six out of every ten) believe their organizations are inadequately prepared to face the next generation of AI-powered threats.

The survey findings reveal contrasting perceptions of organizational preparedness for cybersecurity threats across different regions and job roles. Security administrators, due to their hands-on experience, express the highest level of skepticism, with 72% feeling their organizations are inadequately prepared. Notably, respondents in mid-sized organizations feel the least prepared, while those in the largest companies feel the most prepared.

Regionally, participants in Asia-Pacific are most likely to believe their organizations are unprepared, while those in Latin America feel the most prepared. This aligns with the observation that Asia-Pacific has been the most impacted region by cybersecurity threats in recent years, according to the IBM X-Force Threat Intelligence Index.

The optimism among Latin American respondents could be attributed to lower threat volumes experienced in the region, but it's cautioned that this could change suddenly (1).

What are biggest barriers to defending against AI-powered threats?

The top-ranked inhibitors center on knowledge and personnel. However, issues are alluded to almost equally across the board including concerns around budget, tool integration, lack of attention to AI-powered threats, and poor cyber hygiene.

The cybersecurity industry is facing a significant shortage of skilled professionals, with a global deficit of approximately 4 million experts (2). As organizations struggle to manage their security tools and alerts, the challenge intensifies with the increasing adoption of AI by attackers. This shift has altered the demands on security teams, requiring practitioners to possess broad and deep knowledge across rapidly evolving solution stacks.

Educating end users about AI-driven defenses becomes paramount as organizations grapple with the shortage of professionals proficient in managing AI-powered security tools. Operationalizing machine learning models for effectiveness and accuracy emerges as a crucial skill set in high demand. However, our survey highlights a concerning lack of understanding among cybersecurity professionals regarding AI-driven threats and the use of AI-driven countermeasures indicating a gap in keeping pace with evolving attacker tactics.

The integration of security solutions remains a notable problem, hindering effective defense strategies. While budget constraints are not a primary inhibitor, organizations must prioritize addressing these challenges to bolster their cybersecurity posture. It's imperative for stakeholders to recognize the importance of investing in skilled professionals and integrated security solutions to mitigate emerging threats effectively.

To access the full report click here.

References

1. IBM, X-Force Threat Intelligence Index 2024, Available at: https://www.ibm.com/downloads/cas/L0GKXDWJ

2. ISC2, Cybersecurity Workforce Study 2023, Available at: https://media.isc2.org/-/media/Project/ISC2/Main/Media/ documents/research/ISC2_Cybersecurity_Workforce_Study_2023.pdf?rev=28b46de71ce24e6ab7705f6e3da8637e

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