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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

Type

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

INSIDE THE SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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ABOUT ThE AUTHOR
Sam Lister
SOC 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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Inside the SOC

Sliver C2: How Darktrace Provided a Sliver of Hope in the Face of an Emerging C2 Framework

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17
Apr 2024

Offensive Security Tools

As organizations globally seek to for ways to bolster their digital defenses and safeguard their networks against ever-changing cyber threats, security teams are increasingly adopting offensive security tools to simulate cyber-attacks and assess the security posture of their networks. These legitimate tools, however, can sometimes be exploited by real threat actors and used as genuine actor vectors.

What is Sliver C2?

Sliver C2 is a legitimate open-source command-and-control (C2) framework that was released in 2020 by the security organization Bishop Fox. Silver C2 was originally intended for security teams and penetration testers to perform security tests on their digital environments [1] [2] [5]. In recent years, however, the Sliver C2 framework has become a popular alternative to Cobalt Strike and Metasploit for many attackers and Advanced Persistence Threat (APT) groups who adopt this C2 framework for unsolicited and ill-intentioned activities.

The use of Sliver C2 has been observed in conjunction with various strains of Rust-based malware, such as KrustyLoader, to provide backdoors enabling lines of communication between attackers and their malicious C2 severs [6]. It is unsurprising, then, that it has also been leveraged to exploit zero-day vulnerabilities, including critical vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

In early 2024, Darktrace observed the malicious use of Sliver C2 during an investigation into post-exploitation activity on customer networks affected by the Ivanti vulnerabilities. Fortunately for affected customers, Darktrace DETECT™ was able to recognize the suspicious network-based connectivity that emerged alongside Sliver C2 usage and promptly brought it to the attention of customer security teams for remediation.

How does Silver C2 work?

Given its open-source nature, the Sliver C2 framework is extremely easy to access and download and is designed to support multiple operating systems (OS), including MacOS, Windows, and Linux [4].

Sliver C2 generates implants (aptly referred to as ‘slivers’) that operate on a client-server architecture [1]. An implant contains malicious code used to remotely control a targeted device [5]. Once a ‘sliver’ is deployed on a compromised device, a line of communication is established between the target device and the central C2 server. These connections can then be managed over Mutual TLS (mTLS), WireGuard, HTTP(S), or DNS [1] [4]. Sliver C2 has a wide-range of features, which include dynamic code generation, compile-time obfuscation, multiplayer-mode, staged and stageless payloads, procedurally generated C2 over HTTP(S) and DNS canary blue team detection [4].

Why Do Attackers Use Sliver C2?

Amidst the multitude of reasons why malicious actors opt for Sliver C2 over its counterparts, one stands out: its relative obscurity. This lack of widespread recognition means that security teams may overlook the threat, failing to actively search for it within their networks [3] [5].

Although the presence of Sliver C2 activity could be representative of authorized and expected penetration testing behavior, it could also be indicative of a threat actor attempting to communicate with its malicious infrastructure, so it is crucial for organizations and their security teams to identify such activity at the earliest possible stage.

Darktrace’s Coverage of Sliver C2 Activity

Darktrace’s anomaly-based approach to threat detection means that it does not explicitly attempt to attribute or distinguish between specific C2 infrastructures. Despite this, Darktrace was able to connect Sliver C2 usage to phases of an ongoing attack chain related to the exploitation of zero-day vulnerabilities in Ivanti Connect Secure VPN appliances in January 2024.

Around the time that the zero-day Ivanti vulnerabilities were disclosed, Darktrace detected an internal server on one customer network deviating from its expected pattern of activity. The device was observed making regular connections to endpoints associated with Pulse Secure Cloud Licensing, indicating it was an Ivanti server. It was observed connecting to a string of anomalous hostnames, including ‘cmjk3d071amc01fu9e10ae5rt9jaatj6b.oast[.]live’ and ‘cmjft14b13vpn5vf9i90xdu6akt5k3pnx.oast[.]pro’, via HTTP using the user agent ‘curl/7.19.7 (i686-redhat-linux-gnu) libcurl/7.63.0 OpenSSL/1.0.2n zlib/1.2.7’.

Darktrace further identified that the URI requested during these connections was ‘/’ and the top-level domains (TLDs) of the endpoints in question were known Out-of-band Application Security Testing (OAST) server provider domains, namely ‘oast[.]live’ and ‘oast[.]pro’. OAST is a testing method that is used to verify the security posture of an application by testing it for vulnerabilities from outside of the network [7]. This activity triggered the DETECT model ‘Compromise / Possible Tunnelling to Bin Services’, which breaches when a device is observed sending DNS requests for, or connecting to, ‘request bin’ services. Malicious actors often abuse such services to tunnel data via DNS or HTTP requests. In this specific incident, only two connections were observed, and the total volume of data transferred was relatively low (2,302 bytes transferred externally). It is likely that the connections to OAST servers represented malicious actors testing whether target devices were vulnerable to the Ivanti exploits.

The device proceeded to make several SSL connections to the IP address 103.13.28[.]40, using the destination port 53, which is typically reserved for DNS requests. Darktrace recognized that this activity was unusual as the offending device had never previously been observed using port 53 for SSL connections.

Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.
Figure 1: Model Breach Event Log displaying the ‘Application Protocol on Uncommon Port’ DETECT model breaching in response to the unusual use of port 53.

Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.
Figure 2: Model Breach Event Log displaying details pertaining to the ‘Application Protocol on Uncommon Port’ DETECT model breach, including the 100% rarity of the port usage.

Further investigation into the suspicious IP address revealed that it had been flagged as malicious by multiple open-source intelligence (OSINT) vendors [8]. In addition, OSINT sources also identified that the JARM fingerprint of the service running on this IP and port (00000000000000000043d43d00043de2a97eabb398317329f027c66e4c1b01) was linked to the Sliver C2 framework and the mTLS protocol it is known to use [4] [5].

An Additional Example of Darktrace’s Detection of Sliver C2

However, it was not just during the January 2024 exploitation of Ivanti services that Darktrace observed cases of Sliver C2 usages across its customer base.  In March 2023, for example, Darktrace detected devices on multiple customer accounts making beaconing connections to malicious endpoints linked to Sliver C2 infrastructure, including 18.234.7[.]23 [10] [11] [12] [13].

Darktrace identified that the observed connections to this endpoint contained the unusual URI ‘/NIS-[REDACTED]’ which contained 125 characters, including numbers, lower and upper case letters, and special characters like “_”, “/”, and “-“, as well as various other URIs which suggested attempted data exfiltration:

‘/upload/api.html?c=[REDACTED] &fp=[REDACTED]’

  • ‘/samples.html?mx=[REDACTED] &s=[REDACTED]’
  • ‘/actions/samples.html?l=[REDACTED] &tc=[REDACTED]’
  • ‘/api.html?gf=[REDACTED] &x=[REDACTED]’
  • ‘/samples.html?c=[REDACTED] &zo=[REDACTED]’

This anomalous external connectivity was carried out through multiple destination ports, including the key ports 443 and 8888.

Darktrace additionally observed devices on affected customer networks performing TLS beaconing to the IP address 44.202.135[.]229 with the JA3 hash 19e29534fd49dd27d09234e639c4057e. According to OSINT sources, this JA3 hash is associated with the Golang TLS cipher suites in which the Sliver framework is developed [14].

Conclusion

Despite its relative novelty in the threat landscape and its lesser-known status compared to other C2 frameworks, Darktrace has demonstrated its ability effectively detect malicious use of Sliver C2 across numerous customer environments. This included instances where attackers exploited vulnerabilities in the Ivanti Connect Secure and Policy Secure services.

While human security teams may lack awareness of this framework, and traditional rules and signatured-based security tools might not be fully equipped and updated to detect Sliver C2 activity, Darktrace’s Self Learning AI understands its customer networks, users, and devices. As such, Darktrace is adept at identifying subtle deviations in device behavior that could indicate network compromise, including connections to new or unusual external locations, regardless of whether attackers use established or novel C2 frameworks, providing organizations with a sliver of hope in an ever-evolving threat landscape.

Credit to Natalia Sánchez Rocafort, Cyber Security Analyst, Paul Jennings, Principal Analyst Consultant

Appendices

DETECT Model Coverage

  • Compromise / Repeating Connections Over 4 Days
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Server Activity / Server Activity on New Non-Standard Port
  • Compromise / Sustained TCP Beaconing Activity To Rare Endpoint
  • Compromise / Quick and Regular Windows HTTP Beaconing
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / HTTP Beaconing to Rare Destination
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / Large Number of Suspicious Failed Connections
  • Compromise / SSL or HTTP Beacon
  • Compromise / Possible Malware HTTP Comms
  • Compromise / Possible Tunnelling to Bin Services
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Powershell to Rare External
  • Anomalous Server Activity / New Internet Facing System

List of Indicators of Compromise (IoCs)

18.234.7[.]23 - Destination IP - Likely C2 Server

103.13.28[.]40 - Destination IP - Likely C2 Server

44.202.135[.]229 - Destination IP - Likely C2 Server

References

[1] https://bishopfox.com/tools/sliver

[2] https://vk9-sec.com/how-to-set-up-use-c2-sliver/

[3] https://www.scmagazine.com/brief/sliver-c2-framework-gaining-traction-among-threat-actors

[4] https://github[.]com/BishopFox/sliver

[5] https://www.cybereason.com/blog/sliver-c2-leveraged-by-many-threat-actors

[6] https://securityaffairs.com/158393/malware/ivanti-connect-secure-vpn-deliver-krustyloader.html

[7] https://www.xenonstack.com/insights/out-of-band-application-security-testing

[8] https://www.virustotal.com/gui/ip-address/103.13.28.40/detection

[9] https://threatfox.abuse.ch/browse.php?search=ioc%3A107.174.78.227

[10] https://threatfox.abuse.ch/ioc/1074576/

[11] https://threatfox.abuse.ch/ioc/1093887/

[12] https://threatfox.abuse.ch/ioc/846889/

[13] https://threatfox.abuse.ch/ioc/1093889/

[14] https://github.com/projectdiscovery/nuclei/issues/3330

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About the author
Natalia Sánchez Rocafort
Cyber Security Analyst
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