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Inside the SOC
The resurgence of the raccoon: Steps of a Raccoon Stealer v2 Infection (Part 2)
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].
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.
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)
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)
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’.
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
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.
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
[2] https://twitter.com/3xp0rtblog/status/1507312171914461188
[3] https://www.esentire.com/blog/esentire-threat-intelligence-malware-analysis-raccoon-stealer-v2-0
[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
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More in this series
Blog
Inside the SOC
Protecting Prospects: How Darktrace Detected an Account Hijack Within Days of Deployment
Cloud Migration Expanding the Attack Surface
Cloud migration is here to stay – accelerated by pandemic lockdowns, there has been an ongoing increase in the use of public cloud services, and Gartner has forecasted worldwide public cloud spending to grow around 20%, or by almost USD 600 billion [1], in 2023. With more and more organizations utilizing cloud services and moving their operations to the cloud, there has also been a corresponding shift in malicious activity targeting cloud-based software and services, including Microsoft 365, a prominent and oft-used Software-as-a-Service (SaaS).
With the adoption and implementation of more SaaS products, the overall attack surface of an organization increases – this gives malicious actors additional opportunities to exploit and compromise a network, necessitating proper controls to be in place. This increased attack surface can leave organization’s open to cyber risks like cloud misconfigurations, supply chain attacks and zero-day vulnerabilities [2]. In order to achieve full visibility over cloud activity and prevent SaaS compromise, it is paramount for security teams to deploy sophisticated security measures that are able to learn an organization’s SaaS environment and detect suspicious activity at the earliest stage.
Darktrace Immediately Detects Hijacked Account
In May 2023, Darktrace observed a chain of suspicious SaaS activity on the network of a customer who was about to begin their trial of Darktrace/Cloud™ and Darktrace/Email™. Despite being deployed on the network for less than a week, Darktrace DETECT™ recognized that the legitimate SaaS account, belonging to an executive at the organization, had been hijacked. Darktrace/Email was able to provide full visibility over inbound and outbound mail and identified that the compromised account was subsequently used to launch an internal spear-phishing campaign.
If Darktrace RESPOND™ were enabled in autonomous response mode at the time of this compromise, it would have been able to take swift preventative action to disrupt the account compromise and prevent the ensuing phishing attack.
Account Hijack Attack Overview
Unusual External Sources for SaaS Credentials
On May 9, 2023, Darktrace DETECT/Cloud detected the first in a series of anomalous activities performed by a Microsoft 365 user account that was indicative of compromise, namely a failed login from an external IP address located in Virginia.
Just a few minutes later, Darktrace observed the same user credential being used to successfully login from the same unusual IP address, with multi-factor authentication (MFA) requirements satisfied.
A few hours after this, the user credential was once again used to login from a different city in the state of Virginia, with MFA requirements successfully met again. Around the time of this activity, the SaaS user account was also observed previewing various business-related files hosted on Microsoft SharePoint, behavior that, taken in isolation, did not appear to be out of the ordinary and could have represented legitimate activity.
The following day, May 10, however, there were additional login attempts observed from two different states within the US, namely Texas and Florida. Darktrace understood that this activity was extremely suspicious, as it was highly improbable that the legitimate user would be able to travel over 2,500 miles in such a short period of time. Both login attempts were successful and passed MFA requirements, suggesting that the malicious actor was employing techniques to bypass MFA. Such MFA bypass techniques could include inserting malicious infrastructure between the user and the application and intercepting user credentials and tokens, or by compromising browser cookies to bypass authentication controls [3]. There have also been high-profile cases in the recent years of legitimate users mistakenly (and perhaps even instinctively) accepting MFA prompts on their token or mobile device, believing it to be a legitimate process despite not having performed the login themselves.
New Email Rule
On the evening of May 10, following the successful logins from multiple US states, Darktrace observed the Microsoft 365 user creating a new inbox rule, named “.’, in Microsoft Outlook from an IP located in Florida. Threat actors are often observed naming new email rules with single characters, likely to evade detection, but also for the sake of expediency so as to not expend any additional time creating meaningful labels.
In this case the newly created email rules included several suspicious properties, including ‘AlwaysDeleteOutlookRulesBlob’, ‘StopProcessingRules’ and “MoveToFolder”.
Firstly, ‘AlwaysDeleteOutlookRulesBlob’ suppresses or hides warning messages that typically appear if modifications to email rules are made [4]. In this case, it is likely the malicious actor was attempting to implement this property to obfuscate the creation of new email rules.
The ‘StopProcessingRules’ rule meant that any subsequent email rules created by the legitimate user would be overridden by the email rule created by the malicious actor [5]. Finally, the implementation of “MoveToFolder” would allow the malicious actor to automatically move all outgoing emails from the “Sent” folder to the “Deleted Items” folder, for example, further obfuscating their malicious activities [6]. The utilization of these email rule properties is frequently observed during account hijackings as it allows attackers to delete and/or forward key emails, delete evidence of exploitation and launch phishing campaigns [7].
In this incident, the new email rule would likely have enabled the malicious actor to evade the detection of traditional security measures and achieve greater persistence using the Microsoft 365 account.
Account Update
A few hours after the creation of the new email rule, Darktrace observed the threat actor successfully changing the Microsoft 365 user’s account password, this time from a new IP address in Texas. As a result of this action, the attacker would have locked out the legitimate user, effectively gaining full access over the SaaS account.
Phishing Emails
The compromised SaaS account was then observed sending a high volume of suspicious emails to both internal and external email addresses. Darktrace was able to identify that the emails attempting to impersonate the legitimate service DocuSign and contained a malicious link prompting users to click on the text “Review Document”. Upon clicking this link, users would be redirected to a site hosted on Adobe Express, namely hxxps://express.adobe[.]com/page/A9ZKVObdXhN4p/.
Adobe Express is a free service that allows users to create web pages which can be hosted and shared publicly; it is likely that the threat actor here leveraged the service to use in their phishing campaign. When clicked, such links could result in a device unwittingly downloading malware hosted on the site, or direct unsuspecting users to a spoofed login page attempting to harvest user credentials by imitating legitimate companies like Microsoft.
The malicious site hosted on Adobe Express was subsequently taken down by Adobe, possibly in response to user reports of maliciousness. Unfortunately though, platforms like this that offer free webhosting services can easily and repeatedly be abused by malicious actors. Simply by creating new pages hosted on different IP addresses, actors are able to continue to carry out such phishing attacks against unsuspecting users.
In addition to the suspicious SaaS and email activity that took place between May 9 and May 10, Darktrace/Email also detected the compromised account sending and receiving suspicious emails starting on May 4, just two days after Darktrace’s initial deployment on the customer’s environment. It is probable that the SaaS account was compromised around this time, or even prior to Darktrace’s deployment on May 2, likely via a phishing and credential harvesting campaign similar to the one detailed above.
Darktrace Coverage
As the customer was soon to begin their trial period, Darktrace RESPOND was set in “human confirmation” mode, meaning that any preventative RESPOND actions required manual application by the customer’s security team.
If Darktrace RESPOND had been enabled in autonomous response mode during this incident, it would have taken swift mitigative action by logging the suspicious user out of the SaaS account and disabling the account for a defined period of time, in doing so disrupting the attack at the earliest possible stage and giving the customer the necessary time to perform remediation steps. As it was, however, these RESPOND actions were suggested to the customer’s security team for them to manually apply.
Nevertheless, with Darktrace DETECT/Cloud in place, visibility over the anomalous cloud-based activities was significantly increased, enabling the swift identification of the chain of suspicious activities involved in this compromise.
In this case, the prospective customer reached out to Darktrace directly through the Ask the Expert (ATE) service. Darktrace’s expert analyst team then conducted a timely and comprehensive investigation into the suspicious activity surrounding this SaaS compromise, and shared these findings with the customer’s security team.
Conclusion
Ultimately, this example of SaaS account compromise highlights Darktrace’s unique ability to learn an organization’s digital environment and recognize activity that is deemed to be unexpected, within a matter of days.
Due to the lack of obvious or known indicators of compromise (IoCs) associated with the malicious activity in this incident, this account hijack would likely have gone unnoticed by traditional security tools that rely on a rules and signatures-based approach to threat detection. However, Darktrace’s Self-Learning AI enables it to detect the subtle deviations in a device’s behavior that could be indicative of an ongoing compromise.
Despite being newly deployed on a prospective customer’s network, Darktrace DETECT was able to identify unusual login attempts from geographically improbable locations, suspicious email rule updates, password changes, as well as the subsequent mounting of a phishing campaign, all before the customer’s trial of Darktrace had even begun.
When enabled in autonomous response mode, Darktrace RESPOND would be able to take swift preventative action against such activity as soon as it is detected, effectively shutting down the compromise and mitigating any subsequent phishing attacks.
With the full deployment of Darktrace’s suite of products, including Darktrace/Cloud and Darktrace/Email, customers can rest assured their critical data and systems are protected, even in the case of hybrid and multi-cloud environments.
Credit: Samuel Wee, Senior Analyst Consultant & Model Developer
Appendices
References
[2] https://www.upguard.com/blog/saas-security-risks
[4] https://learn.microsoft.com/en-us/powershell/module/exchange/disable-inboxrule?view=exchange-ps
[7] https://blog.knowbe4.com/check-your-email-rules-for-maliciousness
Darktrace Model Detections
Darktrace DETECT/Cloud and RESPOND Models Breached:
SaaS / Access / Unusual External Source for SaaS Credential Use
SaaS / Unusual Activity / Multiple Unusual External Sources for SaaS Credential
Antigena / SaaS / Antigena Unusual Activity Block (RESPOND Model)
SaaS / Compliance / New Email Rule
Antigena / SaaS / Antigena Significant Compliance Activity Block
SaaS / Compromise / Unusual Login and New Email Rule (Enhanced Monitoring Model)
Antigena / SaaS / Antigena Suspicious SaaS Activity Block (RESPOND Model)
SaaS / Compromise / SaaS Anomaly Following Anomalous Login (Enhanced Monitoring Model)
SaaS / Compromise / Unusual Login and Account Update
Antigena / SaaS / Antigena Suspicious SaaS Activity Block (RESPOND Model)
IoC – Type – Description & Confidence
hxxps://express.adobe[.]com/page/A9ZKVObdXhN4p/ - Domain – Probable Phishing Page (Now Defunct)
37.19.221[.]142 – IP Address – Unusual Login Source
35.174.4[.]92 – IP Address – Unusual Login Source
MITRE ATT&CK Mapping
Tactic - Techniques
INITIAL ACCESS, PRIVILEGE ESCALATION, DEFENSE EVASION, PERSISTENCE
T1078.004 – Cloud Accounts
DISCOVERY
T1538 – Cloud Service Dashboards
CREDENTIAL ACCESS
T1539 – Steal Web Session Cookie
RESOURCE DEVELOPMENT
T1586 – Compromise Accounts
PERSISTENCE
T1137.005 – Outlook Rules
Blog
Darktrace/Email in Action: Why AI-Driven Email Security is the Best Defense Against Sustained Phishing Campaigns
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Stopping the bad while allowing the good
Since its inception, email has been regarded as one of the most important tools for businesses, revolutionizing communication and allowing global teams to become even more connected. But besides organizations heavily relying on email for their daily operations, threat actors have also recognized that the inbox is one of the easiest ways to establish an initial foothold on the network.
Today, not only are phishing campaigns and social engineering attacks becoming more prevalent, but the level of sophistication of these attacks are also increasing with the help of generative AI tools that allow for the creation of hyper-realistic emails with minimal errors, effectively lowering the barrier to entry for threat actors. These diverse and stealthy types of attacks evade traditional email security tools based on rules and signatures, because they are less likely to contain the low-sophistication markers of a typical phishing attack.
In a situation where the sky is the limit for attackers and security teams are lean, how can teams equip themselves to tackle these threats? How can they accurately detect increasingly realistic malicious emails and neutralize these threats before it is too late? And importantly, how can email security block these threats while allowing legitimate emails to flow freely?
Instead of relying on past attack data, Darktrace’s Self-Learning AI detects the slightest deviation from a user’s pattern of life and responds autonomously to contain potential threats, stopping novel attacks in their tracks before damage is caused. It doesn’t define ‘good’ and ‘bad’ like traditional email tools, rather it understands each user and what is normal for them – and what’s not.
This blog outlines how Darktrace/Email™ used its understanding of ‘normal’ to accurately detect and respond to a sustained phishing campaign targeting a real-life company.
Responding to a sustained phishing attack
Over the course of 24 hours, Darktrace detected multiple emails containing different subjects, all from different senders to different recipients in one organization. These emails were sent from different IP addresses, but all came from the same autonomous system number (ASN).
The emails themselves had many suspicious indicators. All senders had no prior association with the recipient, and the emails generated a high general inducement score. This score is generated by structural and non-specific content analysis of the email – a high score indicates that the email is trying to induce the recipient into taking a particular action, which may lead to account compromise.
Additionally, each email contained a visually prominent link to a file storage service, hidden behind a shortened bit.ly link. The similarities across all these emails pointed to a sustained campaign targeting the organization by a single threat actor.
With all these suspicious indicators, many models were breached. This drove up the anomaly score, causing Darktrace/Email to hold all suspicious emails from the recipients’ inboxes, safeguarding the recipients from potential account compromise and disallowing the threats from taking hold in the network.
Imagining a phishing attack without Darktrace/Email
So what could have happened if Darktrace had not withheld these emails, and the recipients had clicked on the links? File storage sites have a wide variety of uses that allow attackers to be creative in their attack strategy. If the user had clicked on the shortened link, the possible consequences are numerous. The link could have led to a login page for unsuspecting victims to input their credentials, or it could have hosted malware that would automatically download if the link was clicked. With the compromised credentials, threat actors could even bypass MFA, change email rules, or gain privileged access to a network. The downloaded malware might also be a keylogger, leading to cryptojacking, or could open a back door for threat actors to return to at a later time.
The limits of traditional email security tools
Secure email gateways (SEGs) and static AI security tools may have found it challenging to detect this phishing campaign as malicious. While Darktrace was able to correlate these emails to determine that a sustained phishing campaign was taking place, the pattern among these emails is far too generic for specific rules as set in traditional security tools. If we take the characteristic of the freemail account sender as an example, setting a rule to block all emails from freemail accounts may lead to more legitimate emails being withheld, since these addresses have a variety of uses.
With these factors in mind, these emails could have easily slipped through traditional security filters and led to a devastating impact on the organization.
Conclusion
As threat actors step up their attacks in sophistication, prioritizing email security is more crucial than ever to preserving a safe digital environment. In response to these challenges, Darktrace/Email offers a set-and-forget solution that continuously learns and adapts to changes in the organization.
Through an evolving understanding of every environment in which it is deployed, its threat response becomes increasingly precise in neutralizing only the bad, while allowing the good – delivering email security that doesn’t come at the expense of business growth.