Blog
Inside the SOC
The resurgence of the raccoon: Steps of a Raccoon Stealer v2 Infection (Part 2)


Introduction
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.
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.
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.
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.
Steps of a Raccoon Stealer v2 Infection
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
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

IOCS














Like this and want more?
Blog
Nuvola
Darktrace Integrates Self-Learning AI with Amazon Security Lake to Support Security Investigations
.jpg)


Darktrace has deepened its relationship with AWS by integrating its detection and response capabilities with Amazon Security Lake.
This development will allow mutual customers to seamlessly combine Darktrace AI’s bespoke understanding of their organization with the Threat Intelligence offered by other security tools, and investigate all of their alerts in one central location.
This integration will improve the value security teams get from both products, streamlining analyst workflows and improving their ability to detect and respond to the full spectrum of known and unknown cyber-threats.
How Darktrace and Amazon Security Lake augment security teams
Amazon Security Lake is a newly-released service that automatically centralizes an organization’s security data from cloud, on-premises, and custom sources into a customer owned purpose-built data lake. Both Darktrace and Amazon Security Lake support the Open Cybersecurity Schema Framework (OCSF), an open standard to simplify, combine, and analyze security logs.
Customers can store security logs, events, alerts, and other relevant data generated by various AWS services and security tools. By consolidating security data in a central lake, organizations can gain a holistic view of their security posture, perform advanced analytics, detect anomalies and open investigations to improve their security practices.
With Darktrace DETECT and RESPOND AI engines covering all assets across IT, OT, network, endpoint, IoT, email and cloud, organizations can augment the value of their security data lakes by feeding Darktrace’s rich and context-aware datapoints to Amazon Security Lake.
Amazon Security Lake empowers security teams to improve the protection of your digital estate:
- Quick and painless data normalization
- Fast-tracks ability to investigate, triage and respond to security events
- Broader visibility aids more effective decision-making
- Surfaces and prioritizes anomalies for further investigation
- Single interface for seamless data management
How will Darktrace customers benefit?
Across the Cyber AI Loop, all Darktrace solutions have been architected with AWS best practices in mind. With this integration, Darktrace is bringing together its understanding of ‘self’ for every organization with the centralized data visibility of the Amazon Security Lake. Darktrace’s unique approach to cyber security, powered by groundbreaking AI research, delivers a superior dataset based on a deep and interconnected understanding of the enterprise.
Where other cyber security solutions are trained to identify threats based on historical attack data and techniques, Darktrace DETECT gains a bespoke understanding of every digital environment, continuously analyzing users, assets, devices and the complex relationships between them. Our AI analyzes thousands of metrics to reveal subtle deviations that may signal an evolving issue – even unknown techniques and novel malware. It distinguishes between malicious and benign behavior, identifying harmful activity that typically goes unnoticed. This rich dataset is fed into RESPOND, which takes precise action to neutralize threats against any and every asset, no matter where data resides.
Both DETECT and RESPOND are supported by Darktrace Self-Learning AI, which provides full, real-time visibility into an organization’s systems and data. This always-on threat analysis already makes humans better at cyber security, improving decisions and outcomes based on total visibility of the digital ecosystem, supporting human performance with AI coverage and empowering security teams to proactively protect critical assets.
Converting Darktrace alerts to the Amazon Security Lake Open Cybersecurity Schema Framework (OCSF) supplies the Security Operations Center (SOC) and incident response team with contextualized data, empowering them to accelerate their investigation, triage and response to potential cyber threats.
Darktrace is available for purchase on the AWS Marketplace.
Learn more about how Darktrace provides full-coverage, AI-powered cloud security for AWS, or see how our customers use Darktrace in their AWS cloud environments.

Blog
Inside the SOC
Tracking the Hive: Darktrace’s Detection of a Hive Ransomware-as-Service


The threat of ransomware continues to be a constant concern for security teams across the cyber threat landscape. With the growing popularity of Ransomware-as-a-Service (RaaS), it is becoming more and more accessible for even inexperienced of would-be attackers. As a result of this low barrier to entry, the volume of ransomware attacks is expected to increase significantly.
What’s more, RaaS is a highly tailorable market in which buyers can choose from varied kits and features to use in their ransomware deployments meaning attacks will rarely behave the same. To effectively detect and safeguard against these differentiations, it is crucial to implement security measures that put the emphasis on detecting anomalies and focusing on deviations in expected behavior, rather than relying on depreciated indicators of compromise (IoC) lists or playbooks that focus on attack chains unable to keep pace with the increasing speed of ransomware evolution.
In early 2022, Darktrace DETECT/Network™ identified several instances of Hive ransomware on the networks of multiple customers. Using its anomaly-based detection, Darktrace was able to successfully detect the attacks and multiple stages of the kill chain, including command and control (C2) activity, lateral movement, data exfiltration, and ultimately data encryption and the writing of ransom notes.
Hive Ransomware
Hive ransomware is a relatively new strain that was first observed in the wild in June 2021. It is known to target a variety of industries including healthcare, energy providers, and retailers, and has reportedly attacked over 1,500 organizations, collecting more than USD 100m in ransom payments [1].
Hive is distributed via a RaaS model where its developers update and maintain the code, in return for a percentage of the eventual ransom payment, while users (or affiliates) are given the tools to carry out attacks using a highly sophisticated and complex malware they would otherwise be unable to use. Hive uses typical tactics, techniques and procedures (TTPs) associated with ransomware, though they do vary depending on the Hive affiliate carrying out the attack.
In most cases a double extortion attack is carried out, whereby data is first exfiltrated and then encrypted before a ransom demand is made. This gives attackers extra leverage as victims are at risk of having their sensitive data leaked to the public on websites such as the ‘HiveLeaks’ TOR website.
Attack Timeline
Owing to the highly customizable nature of RaaS, the tactics and methods employed by Hive actors are expected to differ on a case-by-case basis. Nonetheless in the majority of Hive ransomware incidents identified on Darktrace customer environments, Darktrace DETECT observed the following general attack stages and features. This is possibly indicative of the attacks originating from the same threat actor(s) or from a widely sold batch with a particular configuration to a variety of actors.

Initial Access
Although Hive actors are known to gain initial access to networks through multiple different vectors, the two primary methods reported by security researchers are the exploitation of Microsoft Exchange vulnerabilities, or the distribution of phishing emails with malicious attachments [2][3].
In the early stages of one Hive ransomware attack observed on the network of a Darktrace customer, for example, Darktrace detected a device connecting to the rare external location 23.81.246[.]84, with a PowerShell user agent via HTTP. During this connection, the device attempted to download an executable file named “file.exe”. It is possible that the file was initially accessed and delivered via a phishing email; however, as Darktrace/Email was not enabled at the time of the attack, this was outside of Darktrace’s purview. Fortunately, the connection failed the proxy authentication was thus blocked as seen in the packet capture (PCAP) in Figure 2.
Shortly after this attempted download, the same device started to receive a high volume of incoming SSL connections from a rare external endpoint, namely 146.70.87[.]132. Darktrace logged that this endpoint was using an SSL certificate signed by Go Daddy CA, an easily obtainable and accessible SSL certificate, and that the increase in incoming SSL connections from this endpoint was unusual behavior for this device.
It is likely that this highly anomalous activity detected by Darktrace indicates when the ransomware attack began, likely initial payload download.
Darktrace DETECT models:
- Anomalous Connection / Powershell to Rare External
- Anomalous Server Activity / New Internet Facing System

C2 Beaconing
Following the successful initial access, Hive actors begin to establish their C2 infrastructure on infected networks through numerous connections to C2 servers, and the download of additional stagers.
On customer networks infected by Hive ransomware, Darktrace identified devices initiating a high volume of connections to multiple rare endpoints. This very likely represented C2 beaconing to the attacker’s infrastructure. In one particular example, further open-source intelligence (OSINT) investigation revealed that these endpoints were associated with Cobalt Strike.
Darktrace DETECT models:
- Anomalous Connection / Multiple Connections to New External TCP
- Anomalous Server Activity / Anomalous External Activity from Critical Network Device
- Compromise / High Volume of Connections with Beacon Score
- Compromise / Sustained SSL or HTTP Increase
- Compromise / Suspicious HTTP Beacons to Dotted Quad
- Compromise / SSL or HTTP Beacon
- Device / Lateral Movement and C2 Activity
Internal Reconnaissance, Lateral Movement and Privilege Escalation
After C2 infrastructure has been established, Hive actors typically begin to uninstall antivirus products in an attempt to remain undetected on the network [3]. They also perform internal reconnaissance to look for vulnerabilities and open channels and attempt to move laterally throughout the network.
Amid the C2 connections, Darktrace was able to detect network scanning activity associated with the attack when a device on one customer network was observed initiating an unusually high volume of connections to other internal devices. A critical network device was also seen writing an executable file “mimikatz.exe” via SMB which appears to be the Mimikatz attack tool commonly used for credential harvesting.
There were also several detections of lateral movement attempts via RDP and DCE-RPC where the attackers successfully authenticated using an “Administrator” credential. In one instance, a device was also observed performing ITaskScheduler activity. This service is used to remotely control tasks running on machines and is commonly observed as part of malicious lateral movement activity. Darktrace DETECT understood that the above activity represented a deviation from the devices’ normal pattern of behavior and the following models were breached:
Darktrace DETECT models:
- Anomalous Connection / Anomalous DRSGetNCChanges Operation
- Anomalous Connection / New or Uncommon Service Control
- Anomalous Connection / Unusual Admin RDP Session
- Anomalous Connection / Unusual SMB Version 1 Connectivity
- Compliance / SMB Drive Write
- Device / Anomalous ITaskScheduler Activity
- Device / Attack and Recon Tools
- Device / Attack and Recon Tools In SMB
- Device / EXE Files Distributed to Multiple Devices
- Device / Suspicious Network Scan Activity
- Device / Increase in New RPC Services
- User / New Admin Credentials on Server
Estrazione dei dati
At this stage of the attack, Hive actors have been known to carry out data exfiltration activity on infected networks using a variety of different methods. The Cybersecurity & Infrastructure Security Agency (CISA) reported that “Hive actors exfiltrate data likely using a combination of Rclone and the cloud storage service Mega[.]nz” [4]. Darktrace DETECT identified an example of this when a device on one customer network was observed making HTTP connections to endpoints related to Mega, including “w.apa.mega.co[.]nz”, with the user agent “rclone/v1.57.0” with at least 3 GiB of data being transferred externally (Figure 3). The same device was also observed transferring at least 3.6 GiB of data via SSL to the rare external IP, 158.51.85[.]157.

In another case, a device was observed uploading over 16 GiB of data to a rare external endpoint 93.115.27[.]71 over SSH. The endpoint in question was seen in earlier beaconing activity suggesting that this was likely an exfiltration event.
However, Hive ransomware, like any other RaaS kit, can differ greatly in its techniques and features, and it is important to note that data exfiltration may not always be present in a Hive ransomware attack. In one incident detected by Darktrace, there were no signs of any data leaving the customer environment, indicating data exfiltration was not part of the Hive actor’s objectives.
Darktrace DETECT models:
- Anomalous Connection / Data Sent to Rare Domain
- Anomalous Connection / Lots of New Connections
- Anomalous Connection / Multiple HTTP POSTs to Rare Hostname
- Anomalous Connection / Suspicious Self-Signed SSL
- Anomalous Connection / Uncommon 1 GiB Outbound
- Device / New User Agent and New IP
- Unusual Activity / Unusual External Data to New Endpoints
- Unusual Activity / Unusual External Data Transfer
- Unusual Activity / Enhanced Unusual External Data Transfer
Ransomware Deployment
In the final stage of a typical Hive ransomware attack, the ransomware payload is deployed and begins to encrypt files on infected devices. On one customer network, Darktrace detected several devices connecting to domain controllers (DC) to read a file named “xxx.exe”. Several sources have linked this file name with the Hive ransomware payload [5].
In another example, Darktrace DETECT observed multiple devices downloading the executable files “nua64.exe” and “nua64.dll” from a rare external location, 194.156.90[.]25. OSINT investigation revealed that the files are associated with Hive ransomware.

Shortly after the download of this executable, multiple devices were observed performing an unusual amount of file encryption, appending randomly generated strings of characters to file extensions.
Although it has been reported that earlier versions of Hive ransomware encrypted files with a “.hive” extension [7], Darktrace observed across multiple customers that encrypted files had extensions that were partially-randomized, but consistently 20 characters long, matching the regular expression “[a-zA-Z0-9\-\_]{8}[\-\_]{1}[A-Za-z0-9\-\_]{11}”.

Following the successful encryption of files, Hive proceeds to drop a ransom note, named “HOW_TO_DECRYPT.txt”, into each affected directory. Typically, the ransom note will contain a link to Hive’s “sales department” and, in the event that exfiltration took place, a link to the “HiveLeaks” site, where attackers threaten to publish exfiltrated data if their demands are not met (Figure 6). In cases of Hive ransomware detected by Darktrace, multiple devices were observed attempting to contact “HiveLeaks” TOR domains, suggesting that endpoint users had followed links provided to them in ransom notes.

Examples of file extensions:
- 36C-AT9-_wm82GvBoCPC
- 36C-AT9--y6Z1G-RFHDT
- 36C-AT9-_x2x7FctFJ_q
- 36C-AT9-_zK16HRC3QiL
- 8KAIgoDP-wkQ5gnYGhrd
- kPemi_iF_11GRoa9vb29
- kPemi_iF_0RERIS1m7x8
- kPemi_iF_7u7e5zp6enp
- kPemi_iF_y4u7pB3d3f3
- U-9Xb0-k__T0U9NJPz-_
- U-9Xb0-k_6SkA8Njo5pa
- zm4RoSR1_5HMd_r4a5a9
Darktrace DETECT models:
- Anomalous Connection / SMB Enumeration
- Anomalous Connection / Sustained MIME Type Conversion
- Anomalous Connection / Unusual Admin SMB Session
- Anomalous File / Internal / Additional Extension Appended to SMB File
- Compliance / SMB Drive Write
- Compromise / Ransomware / Suspicious SMB Activity
- Compromise / Ransomware / Ransom or Offensive Words Written to SMB
- Compromise / Ransomware / Possible Ransom Note Write
- Compromise / High Priority Tor2Web
- Compromise / Tor2Web
- Device / EXE Files Distributed to Multiple Devices
Conclusion
As Hive ransomware attacks are carried out by different affiliates using varying deployment kits, the tactics employed tend to vary and new IoCs are regularly identified. Furthermore, in 2022 a new variant of Hive was written using the Rust programming language. This represented a major upgrade to Hive, improving its defense evasion techniques and making it even harder to detect [8].
Hive is just one of many RaaS offerings currently on the market, and this market is only expected to grow in usage and diversity of presentations. As ransomware becomes more accessible and easier to deploy it is essential for organizations to adopt efficient security measures to identify ransomware at the earliest possible stage.
Darktrace DETECT’s Self-Learning AI understands customer networks and learns the expected patterns of behavior across an organization’s digital estate. Using its anomaly-based detection Darktrace is able to identify emerging threats through the detection of unusual or unexpected behavior, without relying on rules and signatures, or known IoCs.
Credit to: Emily Megan Lim, Cyber Analyst, Hyeongyung Yeom, Senior Cyber Analyst & Analyst Team Lead.
Appendices
MITRE AT&CK Mapping
Reconnaissance
T1595.001 – Scanning IP Blocks
T1595.002 – Vulnerability Scanning
Resource Development
T1583.006 – Web Services
Initial Access
T1078 – Valid Accounts
T1190 – Exploit Public-Facing Application
T1200 – Hardware Additions
Execution
T1053.005 – Scheduled Task
T1059.001 – PowerShell
Persistence/Privilege Escalation
T1053.005 – Scheduled Task
T1078 – Valid Accounts
Defense Evasion
T1078 – Valid Accounts
T1207 – Rogue Domain Controller
T1550.002 – Pass the Hash
Discovery
T1018 – Remote System Discovery
T1046 – Network Service Discovery
T1083 – File and Directory Discovery
T1135 – Network Share Discovery
Movimento laterale
T1021.001 – Remote Desktop Protocol
T1021.002 – SMB/Windows Admin Shares
T1021.003 – Distributed Component Object Model
T1080 – Taint Shared Content
T1210 – Exploitation of Remote Services
T1550.002 – Pass the Hash
T1570 – Lateral Tool Transfer
Collection
T1185 – Man in the Browser
Command and Control
T1001 – Data Obfuscation
T1071 – Application Layer Protocol
T1071.001 – Web Protocols
T1090.003 – Multi-hop proxy
T1095 – Non-Application Layer Protocol
T1102.003 – One-Way Communication
T1571 – Non-Standard Port
Exfiltration
T1041 – Exfiltration Over C2 Channel
T1567.002 – Exfiltration to Cloud Storage
Impact
T1486 – Data Encrypted for Impact
T1489 – Service Stop
List of IoCs
23.81.246[.]84 - IP Address - Likely Malicious File Download Endpoint
146.70.87[.]132 - IP Address - Possible Ransomware Endpoint
5.199.162[.]220 - IP Address - C2 Endpoint
23.227.178[.]65 - IP Address - C2 Endpoint
46.166.161[.]68 - IP Address - C2 Endpoint
46.166.161[.]93 - IP Address - C2 Endpoint
93.115.25[.]139 - IP Address - C2 Endpoint
185.150.1117[.]189 - IP Address - C2 Endpoint
192.53.123[.]202 - IP Address - C2 Endpoint
209.133.223[.]164 - IP Address - Likely C2 Endpoint
cltrixworkspace1[.]com - Domain - C2 Endpoint
vpnupdaters[.]com - Domain - C2 Endpoint
93.115.27[.]71 - IP Address - Possible Exfiltration Endpoint
158.51.85[.]157 - IP Address - Possible Exfiltration Endpoint
w.api.mega.co[.]nz - Domain - Possible Exfiltration Endpoint
*.userstorage.mega.co[.]nz - Domain - Possible Exfiltration Endpoint
741cc67d2e75b6048e96db9d9e2e78bb9a327e87 - SHA1 Hash - Hive Ransomware File
2f9da37641b204ef2645661df9f075005e2295a5 - SHA1 Hash - Likely Hive Ransomware File
hiveleakdbtnp76ulyhi52eag6c6tyc3xw7ez7iqy6wc34gd2nekazyd[.]onion - TOR Domain - Likely Hive Endpoint
References
[1] https://www.justice.gov/opa/pr/us-department-justice-disrupts-hive-ransomware-variant
[2] https://www.varonis.com/blog/hive-ransomware-analysis
[3] https://www.trendmicro.com/vinfo/us/security/news/ransomware-spotlight/ransomware-spotlight-hive
[4]https://www.cisa.gov/news-events/cybersecurity-advisories/aa22-321a
[5] https://www.trendmicro.com/en_us/research/22/c/nokoyawa-ransomware-possibly-related-to-hive-.html
[6] https://www.virustotal.com/gui/file/60f6a63e366e6729e97949622abd9de6d7988bba66f85a4ac8a52f99d3cb4764/detection
[7] https://heimdalsecurity.com/blog/what-is-hive-ransomware/
[8] https://www.microsoft.com/en-us/security/blog/2022/07/05/hive-ransomware-gets-upgrades-in-rust/