Blog
Eメール
Threat Finds
ゲートウェイをすり抜けたフィアウェア攻撃をAntigena Email がどのようにキャッチしたか
The cyber-criminals behind email attacks are well-researched and highly responsive to human behaviors and emotions, often seeking to evoke a specific reaction by leveraging topical information and current news. It’s therefore no surprise that attackers have attempted to latch onto COVID-19 in their latest effort to convince users to open their emails and click on seemingly benign links.
The latest email trend involves attackers who claim to be from the Center for Disease Control and Prevention, purporting to have emergency information about COVID-19. This is typical of a recent trend we’re calling ‘fearware’: cyber-criminals exploit a collective sense of fear and urgency, and coax users into clicking a malicious attachment or link. While the tactic is common, the actual campaigns contain terms and content that’s unique. There are a few patterns in the emails we’ve seen, but none reliably predictable enough to create hard and fast rules that will stop emails with new wording without causing false positives.
For example, looking for the presence of “CDC” in the email sender would easily fail when the emails begin to use new wording, like “WHO”. We’ve also seen a mismatch of links and their display text – with display text that reads “https://cdc.gov/[random-path]” while the actual link is a completely arbitrary URL. Looking for a pattern match on this would likely lead to false positives and would serve as a weak indicator at best.
The majority of these emails, especially the early ones, passed most of our customers’ existing defenses including Mimecast, Proofpoint, and Microsoft’s ATP, and were approved to be delivered directly to the end user’s inbox. Fortunately, these emails were immediately identified and actioned by Antigena Email, Darktrace’s Autonomous Response technology for the inbox.
Gateways: The Current Approach
Most organizations employ Secure Email Gateways (SEGs), like Mimecast or Proofpoint, which serve as an inline middleman between the email sender and the recipient’s email provider. SEGs have largely just become spam-detection engines, as these emails are obvious to spot when seen at scale. They can identify low-hanging fruit (i.e. emails easily detectable as malicious), but they fail to detect and respond when attacks become personalized or deviate even slightly from previously-seen attacks.
Figure 1: A high-level diagram depicting an Email Secure Gateway’s inline position.
SEGs tend to use lists of ‘known-bad’ IPs, domains, and file hashes to determine an email’s threat level – inherently failing to stop novel attacks when they use IPs, domains, or files which are new and have not yet been triaged or reported as malicious.
When advanced detection methods are used in gateway technologies, such as anomaly detection or machine learning, these are performed after the emails have been delivered, and require significant volumes of near-identical emails to trigger. The end result is very often to take an element from one of these emails and simply deny-list it.
When a SEG can’t make the determination on these factors, they may resort to a technique known as sandboxing, which creates an isolated environment for testing links and attachments seen in emails. Alternatively, they may turn to basic levels of anomaly detection that are inadequate due to their lack of context of data outside of emails. For sandboxing, most advanced threats now typically employ evasion techniques like an activation time that waits until a certain date before executing. When deployed, the sandboxing attempts see a harmless file, not recognizing the sleeping attack waiting within.
Figure 2: This email was registered only 2 hours prior to an email we processed.
Taking a sample COVID-19 email seen in a Darktrace customer’s environment, we saw a mix of domains used in what appears to be an attempt to avoid pattern detection. It would be improbable to have the domains used on a list of ‘known-bad’ domains anywhere at the time of the first email, as it was received a mere two hours after the domain was registered.
Figure 3: While other defenses failed to block these emails, Antigena Email immediately marked them as 100% unusual and held them back from delivery.
Antigena Email sits behind all other defenses, meaning we only see emails when those defenses fail to block a malicious email or deem an email is safe for delivery. In the above COVID-19 case, the first 5 emails were marked by MS ATP with a spam confidence score of 1, indicating Microsoft scanned the email and it was determined to be clean – so Microsoft took no action whatsoever.
The Cat and Mouse Game
Cyber-criminals are permanently in flux, quickly moving to outsmart security teams and bypass current defenses. Recognizing email as the easiest entry point into an organization, they are capitalizing on the inadequate detection of existing tools by mass-producing personalized emails through factory-style systems that machine-research, draft, and send with minimal human interaction.
Domains are cheap, proxies are cheap, and morphing files slightly to change the entire fingerprint of a file is easy – rendering any list of ‘known-bads’ as outdated within seconds.
Cyber AI: The New Approach
A new approach is required that relies on business context and an inside-out understanding of a corporation, rather than analyzing emails in isolation.
An Immune System Approach
Darktrace’s core technology uses AI to detect unusual patterns of behavior in the enterprise. The AI is able to do this successfully by following the human immune system’s core principles: develop an innate sense of ‘self’, and use that understanding to detect abnormal activity indicative of a threat.
In order to identify threats across the entire enterprise, the AI is able to understand normal patterns of behavior beyond just the network. This is crucial when working towards a goal of full business understanding. There’s a clear connection between activity in, for example, a SaaS application and a corresponding network event, or an event in the cloud and a corresponding event elsewhere within the business.
There’s an explicit relationship between what people do on their computers and the emails they send and receive. Having the context that a user has just visited a website before they receive an email from the same domain lends credibility to that email: it’s very common to visit a website, subscribe to a mailing list, and then receive an email within a few minutes. On the contrary, receiving an email from a brand-new sender, containing a link that nobody in the organization has ever been to, lends support to the fact that the link is likely no good and that perhaps the email should be removed from the user’s inbox.
Enterprise-Wide Context
Darktrace’s Antigena Email extends this interplay of data sources to the inbox, providing unique detection capabilities by leveraging full business context to inform email decisions.
The design of Antigena Email provides a fundamental shift in email security – from where the tool sits to how it understands and processes data. Unlike SEGs, which sit inline and process emails only as they first pass through and never again, Antigena Email sits passively, ingesting data that is journaled to it. The technology doesn’t need to wait until a domain is fingerprinted or sandboxed, or until it is associated with a campaign that has a famous name and all the buzz.
Antigena Email extends its unique position of not sitting inline to email re-assessment, processing emails millions of times instead of just once, enabling actions to be taken well after delivery. A seemingly benign email with popular links may become more interesting over time if there’s an event within the enterprise that was determined to have originated via an email, perhaps when a trusted site becomes compromised. While Antigena Network will mitigate the new threat on the network, Antigena Email will neutralize the emails that contain links associated with those found in the original email.
Figure 4: Antigena Email sits passively off email providers, continuously re-assessing and issuing updated actions as new data is introduced.
When an email first arrives, Antigena Email extracts its raw metadata, processes it multiple times at machine speed, and then many millions of times subsequently as new evidence is introduced (typically based on events seen throughout the business). The system corroborates what it is seeing with what it has previously understood to be normal throughout the corporate environment. For example, when domains are extracted from envelope information or links in the email body, they’re compared against the popularity of the domain on the company’s network.
Figure 5: The link above was determined to be 100% rare for the enterprise.
Dissecting the above COVID-19 linked email, we can extract some of the data made available in the Antigena Email user interface to see why Darktrace thought the email was so unusual. The domain in the ‘From’ address is rare, which is supplemental contextual information derived from data across the customer’s entire digital environment, not limited to just email but including network data as well. The emails’ KCE, KCD, and RCE indicate that it was the first time the sender had been seen in any email: there had been no correspondence with the sender in any way, and the email address had never been seen in the body of any email.
Figure 6: KCE, KCD, and RCE scores indicate no sender history with the organization.
Correlating the above, Antigena Email deemed these emails 100% anomalous to the business and immediately removed them from the recipients’ inboxes. The platform did this for the very first email, and every email thereafter – not a single COVID-19-based email got by Antigena Email.
結論
Cyber AI does not distinguish ‘good’ from ‘bad’; rather whether an event is likely to belong or not. The technology looks only to compare data with the learnt patterns of activity in the environment, incorporating the new email (alongside its own scoring of the email) into its understanding of day-to-day context for the organization.
By asking questions like “Does this email appear to belong?” or “Is there an existing relationship between the sender and recipient?”, the AI can accurately discern the threat posed by a given email, and incorporate these findings into future modelling. A model cannot be trained to think just because the corporation received a higher volume of emails from a specific sender, these emails are all of a sudden considered normal for the environment. By weighing human interaction with the emails or domains to make decisions on math-modeling reincorporation, Cyber AI avoids this assumption, unless there’s legitimate correspondence from within the corporation back out to the sender.
The inbox has traditionally been the easiest point of entry into an organization. But the fundamental differences in approach offered by Cyber AI drastically increase Antigena Email’s detection capability when compared with gateway tools. Customers with and without email gateways in place have therefore seen a noticeable curbing of their email problem. In the continuous cat-and-mouse game with their adversaries, security teams augmenting their defenses with Cyber AI are finally regaining the advantage.
Like this and want more?
Blog
クラウド
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
データ漏えい
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
結論
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
ラテラルムーブメント
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/