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February 24, 2021

LockBit Ransomware Analysis: Compromised Credentials

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24
Feb 2021
Darktrace examines how a LockBit ransomware attack that took place over just four hours was caused by one compromised credential. Read more here.

Lockbit ransomware found

LockBit ransomware was recently identified by Darktrace's Cyber AI during a trial with a retail company in the US. After an initial foothold was established via a compromised administrative credential, internal reconnaissance, lateral movement, and encryption of files occurred simultaneously, allowing the ransomware to steamroll through the digital system in just a few hours.

This incident serves as the latest reminder that ransomware campaigns now move through organizations at a speed that far outpaces human responders, demonstrating the need for machine-speed Autonomous Response to contain the threat before damage is done.

Lockbit ransomware defined

First discovered in 2019, LockBit is a relatively new family of ransomware that quickly exploits commonly available protocols and tools like SMB and PowerShell. It was originally known as ‘ABCD’ due the filename extension of the encrypted files, before it started using the current .lockbit extension. Since those early beginnings, it has evolved into one of the most calamitous strains of malware to date, asking for an average ransom of around $40,000 per organization.

As cyber-criminals level up the speed and scale of their attacks, ransomware remains a critical concern for organizations across every industry. In the past 12 months, Darktrace has observed an increase of over 20% in ransomware incidents across its customer base. Attackers are constantly developing new threat variants targeting exploits, utilizing off-the-shelf tools, and profiting from the burgeoning Ransomware-as-a-Service (RaaS) business model.

How does LockBit work?

In a typical attack, a threat actor will spend days or weeks inside a system, manually screening for the best way to grind the victim’s business to a halt. This phase tends to expose multiple indicators of compromise such as command and control (C2) beaconing, which Darktrace AI identifies in real time.

LockBit, however, only requires the presence of a human for a number of hours, after which it propagates through a system and infects other hosts on its own, without the need for human oversight. Crucially, the malware performs reconnaissance and continues to spread during the encryption phase. This allows it to cause maximal damage faster than other manual approaches.

AI-powered defense is essential in fighting back against these machine-driven attacks, which have the capacity to spread at speed and scale, and often go undetected by signature-based security tools. Cyber AI augments human teams by not only detecting the subtle signs of a threat, but autonomously responding in seconds, quicker than any human can be expected to react.

Ransomware analysis: Breaking down a LockBit attack with AI

Figure 1: Timeline of attack on the infected host and the encryption host. The infected host was the device initially infected with LockBit, which then spread to the encryption host, the device which performed the encryption.

Initial compromise

The attack commenced when a cyber-criminal gained access to a single privileged credential – either through a brute-force attack on an externally facing device, as seen in previous LockBit ransomware attacks, or simply with a phishing email. With the use of this credential, the device was able to spread and encrypt files within hours of the initial infection.

Had the method of infiltration been via phishing attack, a route that has become increasingly popular in recent months, Darktrace/Email would have withheld the email and stripped the malicious payloads, and so prevented the attack from the outset.

Limiting permissions, the use of strong passwords, and multi-factor authentication (MFA), are critical in preventing the exploitation of standard network protocols in such attacks.

Internal reconnaissance

At 14:19 local time, the first of many WMI commands (ExecMethod) to multiple internal destinations was performed by an internal IP address over DCE-RPC. This series of commands occurred throughout the encryption process. Given these commands were unusual in the context of the normal ‘pattern of life’ for the organization, Darktrace DETECT alerted the security team to each of these connections.

Within three minutes, the device had started to write executable files over SMB to hidden shares on multiple destinations – many of which were the same. File writes to hidden shares are ordinarily restricted. However, the unauthorized use of an administrative credential granted these privileges. The executable files were written to the Windows / Temp directory. Filenames had a similar formatting: .*eck[0-9]?.exe

Darktrace identified each of these SMB writes as a potential threat, since such administrative activity was unexpected from the compromised device.

The WMI commands and executable file writes continued to be made to multiple destinations. In less than two hours, the ExecMethod command was delivered to a critical device – the ‘encryption host’ – shortly followed by an executable file write (eck3.exe) to its hidden c$ share.

LockBit’s script has the capability to check its current privileges and, if non-administrative, it attempts to bypass using Windows User Account Control (UAC). This particular host did provide the required privileges to the process. Once this device was infected, encryption began.

File encryption

Only one second after encryption had started, Darktrace alerted on the unusual file extension appendage in addition to the previous, high-fidelity alerts for earlier stages of the attack lifecycle.

A recovery file – ‘Restore-My-Files.txt’ – was identified by Darktrace one second after the first encryption event. 8,998 recovery files were written, one to each encrypted folder.

Figure 2: An example of Darktrace’s Threat Visualizer showcasing anomalous SMB connections, with model breaches represented by dots.

The encryption host was a critical device that regularly utilized SMB. Exploiting SMB is a popular tactic for cyber-criminals. Such tools are so frequently used that it is difficult for signature-based detection methods to identify quickly whether their activity is malicious or not. In this case, Darktrace’s ‘Unusual Activity’ score for the device was elevated within two seconds of the first encryption, indicating that the device was deviating from its usual pattern of behavior.

Throughout the encryption process, Darktrace also detected the device performing network reconnaissance, enumerating shares on 55 devices (via srvsvc) and scanning over 1,000 internal IP addresses on nine critical TCP ports.

During this time, ‘Patient Zero’ – the initially infected device – continued to write executable files to hidden file shares. LockBit was using the initial device to spread the malware across the digital estate, while the ‘encryption host’ performed reconnaissance and encrypted the files simultaneously.

Despite Cyber AI detecting the threat even before the encryption had begun, the security team did not have eyes on Darktrace at the time of the attack. The intrusion was thus allowed to continue and over 300,000 files were encrypted and appended with the .lockbit extension. Four servers and 15 desktop devices were affected, before the attack was stopped by the administrators.

The rise of ‘hit and run’ ransomware

While most ransomware resides inside an organization for days or weeks, LockBit’s self-governing nature allows the attacker to ‘hit and run’, deploying the ransomware with minimal interaction required after the initial intrusion. The ability to detect anomalous activity across the entire digital infrastructure in real time is therefore crucial in LockBit’s prevention.

WMI and SMB are relied upon by the vast majority of companies around the world, and yet they were utilized in this attack to propagate through the system and encrypt hundreds of thousands of files. The prevalence and volume of these connections make them near-impossible to monitor with humans or signature-based detection techniques alone.

Moreover, the uniqueness of every enterprise’s digital estate impedes signature-based detection from effectively alerting on internal connections and the volume of such connections. Darktrace, however, uses machine learning to understand the individual pattern of behavior for each device, in this case allowing it to highlight the unusual internal activity as it occurred.

The organization involved did not have Darktrace RESPOND – Darktrace’s Autonomous Response technology – configured in active mode. If enabled, RESPOND would have surgically blocked the initial WMI operations and SMB drive writes that triggered the attack whilst allowing the critical network devices to continue standard operations. Even if the foothold had been established, RESPOND would have enforced the ‘pattern of life’ of the encryption host, preventing the cascade of encryption over SMB. This demonstrates the importance of meeting machine-speed attacks with autonomous cyber security, which reacts in real time to sophisticated threats when human security teams cannot.

LockBit has the ability to encrypt thousands of files in just seconds, even when targeting well-prepared organizations. This type of ransomware, with built-in worm-like functionality, is expected to become increasingly common over 2021. Such attacks can move at a speed which no human security team alone can match. Darktrace’s approach, which uses unsupervised machine learning, can respond in seconds to these rapid attacks and shut them down in their earliest stages.

Thanks to Darktrace analyst Isabel Finn for her insights on the above threat find.

Darktrace model detections:

  • Device / New or Uncommon WMI Activity
  • Compliance / SMB Drive Write
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Connection / SMB Enumeration
  • Device / Network Scan – Low Anomaly Score
  • Anomalous Connection / Sustained MIME Type Conversion
  • Anomalous Connection / Suspicious Read Write Ratio
  • Unusual Activity / Sustained Anomalous SMB Activity
  • Device / Large Number of Model Breaches

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Author
Max Heinemeyer
Chief Product Officer

Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.

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November 1, 2024

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Inside the SOC

Phishing and Persistence: Darktrace’s Role in Defending Against a Sophisticated Account Takeover

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The exploitation of SaaS platforms

As businesses continue to grow and evolve, the need for sharing ideas through productivity and cloud Software-as-a-Service (SaaS) platforms is becoming increasingly crucial. However, these platforms have also become prime targets for cyber attackers.

Threat actors often exploit these widely-used services to gain unauthorized access, steal sensitive information, and disrupt business operations. The growing reliance on SaaS platforms makes them attractive entry points for cybercriminals, who use sophisticated techniques such as phishing, social engineering, and malware to compromise these systems.

Services like Microsoft 365 are regularly targeted by threat actors looking for an entry point into an organization’s environment to carry out malicious activities. Securing these platforms is crucial to protect business data and ensure operational continuity.

Darktrace / EMAIL detection of the phishing attack

In a recent case, Darktrace observed a customer in the manufacturing sector receiving a phishing email that led to a threat actor logging in and creating an email rule. Threat actors often create email rules to move emails to their inbox, avoiding detection. Additionally, Darktrace detected a spoofed domain registered by the threat actor. Despite already having access to the customer’s SaaS account, the actor seemingly registered this domain to maintain persistence on the network, allowing them to communicate with the spoofed domain and conduct further malicious activity.

Darktrace / EMAIL can help prevent compromises like this one by blocking suspicious emails as soon as they are identified. Darktrace’s AI-driven email detection and response recognizes anomalies that might indicate phishing attempts and applies mitigative actions autonomously to prevent the escalation of an attack.

Unfortunately, in this case, Darktrace was not configured in Autonomous Response mode at the time of the attack, meaning actions had to be manually applied by the customer’s security team. Had it been fully enabled, it would have held the emails, preventing them from reaching the intended recipient and stopping the attack at its inception.

However, Darktrace’s Managed Threat Detection alerted the Security Operations Center (SOC) team to the compromise, enabling them to thoroughly investigate the incident and notify the customer before further damage could occur.

The Managed Threat Detection service continuously monitors customer networks for suspicious activities that may indicate an emerging threat. When such activities are detected, alerts are sent to Darktrace’s expert Cyber Analysts for triage, significantly speeding up the remediation process.

Attack Overview

On May 2, 2024, Darktrace detected a threat actor targeting a customer in the manufacturing sector then an unusual login to their SaaS environment was observed prior to the creation of a new email rule.

Darktrace immediately identified the login as suspicious due to the rarity of the source IP (31.222.254[.]27) and ASN, coupled with the absence of multi-factor authentication (MFA), which was typically required for this account.

The new email rule was intended to mark emails as read and moved to the ‘Conversation History’ folder for inbound emails from a specific domain. The rule was named “….,,,”, likely the attacker attempting to setup their new rule with an unnoteworthy name to ensure it would not be noticed by the account’s legitimate owner. Likewise, by moving emails from a specific domain to ‘Conversation History’, a folder that is rarely used by most users, any phishing emails sent by that domain would remain undetected by the user.

Darktrace’s detection of the unusual SaaS login and subsequent creation of the new email rule “….,,,”.
Figure 1: Darktrace’s detection of the unusual SaaS login and subsequent creation of the new email rule “….,,,”.

The domain in question was identified as being newly registered and an example of a typosquat domain. Typosquatting involves registering new domains with intentional misspelling designed to convince users to visit fake, and often malicious, websites. This technique is often used in phishing campaigns to create a sense of legitimacy and trust and deceive users into providing sensitive information. In this case, the suspicious domain closely resembled several of the customer’s internal domains, indicating an attempt to impersonate the organization’s legitimate internal sites to gain the target’s trust. Furthermore, the creation of this lookalike domain suggests that the attack was highly targeted at this specific customer.

Interestingly, the threat actor registered this spoofed domain despite already having account access. This was likely intended to ensure persistence on the network without having to launch additional phishing attacks. Such use of spoofed domain could allow an attacker to maintain a foothold in their target network and escalate their malicious activities without having to regain access to the account. This persistence can be used for various purposes, including data exfiltration, spreading malware, or launching further attacks.

Following this, Darktrace detected a highly anomalous email being sent to the customer’s account from the same location as the initial unsual SaaS login. Darktrace’s anomaly-based detection is able to identify threats that human security teams and traditional signature-based methods might miss. By analyzing the expected behavior of network users, Darktrace can recognize the subtle deviations from the norm that may indicate malicious activity. Unfortunately, in this instance, without Darktrace’s Autonomous Response capability enabled, the phishing email was able to successfully reach the recipient. While Darktrace / EMAIL did suggest that the email should be held from the recipients inbox, the customer was required to manually approve it.

Despite this, the Darktrace SOC team were still able to support the customer as they were subscribed to the Managed Threat Detection service. Following the detection of the highlight anomalous activity surrounding this compromise, namely the unusual SaaS login followed by a new email rule, an alert was sent to the Darktrace SOC for immediate triage, who then contacted the customer directly urging immediate action.

Conclusion

This case underscores the need to secure SaaS platforms like Microsoft 365 against sophisticated cyber threats. As businesses increasingly rely on these platforms, they become prime targets for attackers seeking unauthorized access and disruption.

Darktrace’s anomaly-based detection and response capabilities are crucial in identifying and mitigating such threats. In this instance, Darktrace detected a phishing email that led to a threat actor logging in and creating a suspicious email rule. The actor also registered a spoofed domain to maintain persistence on the network.

Darktrace / EMAIL, with its AI-driven detection and analysis, can block suspicious emails before they reach the intended recipient, preventing attacks at their inception. Meanwhile, Darktrace’s SOC team promptly investigated the activity and alerted the customer to the compromise, enabling them to take immediate action to remediate the issue and prevent any further damage.

Credit to Vivek Rajan (Cyber Security Analyst) and Ryan Traill (Threat Content Lead).

Appendices

Darktrace Model Detections

  • SaaS / Access / Unusual External Source for SaaS Credential Use
  • SaaS / Compromise / Login From Rare Endpoint While User Is Active
  • SaaS / Resource / Unusual Access to Delegated Resource by Non Owner
  • SaaS / Email Nexus / Unusual Login Location Following Sender Spoof
  • Compliance / Anomalous New Email Rule
  • SaaS / Compromise / Unusual Login and New Email Rule

Indicators of Compromise (IoCs)

IoC - Type - Description + Confidence

31.222.254[.]27 – IP -  Suspicious Login Endpoint

MITRE ATT&CK Mapping

Tactic – Technqiue – Sub-technique of (if applicable)

Cloud Accounts - DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - T1078.004 - T1078

Cloud Service Dashboard – DISCOVERY - T1538

Compromise Accounts - RESOURCE DEVELOPMENT - T1586

Steal Web Session Cookie - CREDENTIAL ACCESS - T1539

Outlook Rules – PERSISTENCE - T1137.005 - T1137

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About the author
Vivek Rajan
Cyber Analyst

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October 31, 2024

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Inside the SOC

Lifting the Fog: Darktrace’s Investigation into Fog Ransomware

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Introduction to Fog Ransomware

As ransomware attacks continue to be launched at an alarming rate, Darktrace’s Threat Research team has identified that familiar strains like Akira, LockBit, and BlackBasta remain among the most prevalent threats impacting its customers, as reported in the First 6: Half-Year Threat Report 2024. Despite efforts by law agencies, like dismantling the infrastructure of cybercriminals and shutting down their operations [2], these groups continue to adapt and evolve.

As such, it is unsurprising that new ransomware variants are regularly being created and launched to get round law enforcement agencies and increasingly adept security teams. One recent example of this is Fog ransomware.

What is Fog ransomware?

Fog ransomware is strain that first appeared in the wild in early May 2024 and has been observed actively using compromised virtual private network (VPN) credentials to gain access to organization networks in the education sector in the United States.

Darktrace's detection of Fog Ransomware

In June 2024, Darktrace observed instances of Fog ransomware across multiple customer environments. The shortest time observed from initial access to file encryption in these attacks was just 2 hours, underscoring the alarming speed with which these threat actors can achieve their objectives.

Darktrace identified key activities typical of a ransomware kill chain, including enumeration, lateral movement, encryption, and data exfiltration. In most cases, Darktrace was able to successfully halt the progression Fog attacks in their early stages by applying Autonomous Response actions such as quarantining affected devices and blocking suspicious external connections.

To effectively illustrate the typical kill chain of Fog ransomware, this blog focuses on customer environments that did not have Darktrace’s Autonomous Response enabled. In these cases, the attack progressed unchecked and reached its intended objectives until the customer received Darktrace’s alerts and intervened.

Darktrace’s Coverage of Fog Ransomware

Initial Intrusion

After actors had successfully gained initial access into customer networks by exploiting compromised VPN credentials, Darktrace observed a series of suspicious activities, including file shares, enumeration and extensive scanning. In one case, a compromised domain controller was detected making outgoing NTLM authentication attempts to another internal device, which was subsequently used to establish RDP connections to a Windows server running Hyper-V.

Given that the source was a domain controller, the attacker could potentially relay the NTLM hash to obtain a domain admin Kerberos Ticket Granting Ticket (TGT). Additionally, incoming NTLM authentication attempts could be triggered by tools like Responder, and NTLM hashes used to encrypt challenge response authentication could be abused by offline brute-force attacks.

Darktrace also observed the use of a new administrative credential on one affected device, indicating that malicious actors were likely using compromised privileged credentials to conduct relay attacks.

Establish Command-and-Control Communication (C2)

In many instances of Fog ransomware investigated by Darktrace’s Threat Research team, devices were observed making regular connections to the remote access tool AnyDesk. This was exemplified by consistent communication with the endpoint “download[.]anydesk[.]com” via the URI “/AnyDesk.exe”. In other cases, Darktrace identified the use of another remote management tool, namely SplashTop, on customer servers.

In ransomware attacks, threat actors often use such legitimate remote access tools to establish command-and-control (C2) communication. The use of such services not only complicates the identification of malicious activities but also enables attackers to leverage existing infrastructure, rather than having to implement their own.

Internal Reconnaissance

Affected devices were subsequently observed making an unusual number of failed internal connections to other internal locations over ports such as 80 (HTTP), 3389 (RDP), 139 (NetBIOS) and 445 (SMB). This pattern of activity strongly indicated reconnaissance scanning behavior within affected networks. A further investigation into these HTTP connections revealed the URIs “/nice ports”/Trinity.txt.bak”, commonly associated with the use of the Nmap attack and reconnaissance tool.

Simultaneously, some devices were observed engaging in SMB actions targeting the IPC$ share and the named pipe “srvsvc” on internal devices. Such activity aligns with the typical SMB enumeration tactics, whereby attackers query the list of services running on a remote host using a NULL session, a method often employed to gather information on network resources and vulnerabilities.

Lateral Movement

As attackers attempted to move laterally through affected networks, Darktrace observed suspicious RDP activity between infected devices. Multiple RDP connections were established to new clients, using devices as pivots to propagate deeper into the networks, Following this, devices on multiple networks exhibited a high volume of SMB read and write activity, with internal share drive file names being appended with the “.flocked” extension – a clear sign of ransomware encryption. Around the same time, multiple “readme.txt” files were detected being distributed across affected networks, which were later identified as ransom notes.

Further analysis of the ransom note revealed that it contained an introduction to the Fog ransomware group, a summary of the encryption activity that had been carried out, and detailed instructions on how to communicate with the attackers and pay the ransom.

Packet capture (PCAP) of the ransom note file titled “readme.txt”.
Figure 1: Packet capture (PCAP) of the ransom note file titled “readme.txt”.

Data Exfiltration

In one of the cases of Fog ransomware, Darktrace’s Threat Research team observed potential data exfiltration involving the transfer of internal files to an unusual endpoint associated with the MEGA file storage service, “gfs302n515[.]userstorage[.]mega[.]co[.]nz”.

This exfiltration attempt suggests the use of double extortion tactics, where threat actors not only encrypt victim’s data but also exfiltrate it to threaten public exposure unless a ransom is paid. This often increases pressure on organizations as they face the risk of both data loss and reputational damage caused by the release of sensitive information.

Darktrace’s Cyber AI Analyst autonomously investigated what initially appeared to be unrelated events, linking them together to build a full picture of the Fog ransomware attack for customers’ security teams. Specifically, on affected networks Cyber AI Analyst identified and correlated unusual scanning activities, SMB writes, and file appendages that ultimately suggested file encryption.

Cyber AI Analyst’s analysis of encryption activity on one customer network.
Figure 2: Cyber AI Analyst’s analysis of encryption activity on one customer network.
Figure 3: Cyber AI Analysts breakdown of the investigation process between the linked incident events on one customer network.

Safeguarding vulnerable sectors with real-time ransomware mitigation

As novel and fast-moving ransomware variants like Fog persist across the threat landscape, the time taken for from initial compromise to encryption has significantly decreased due to the enhanced skill craft and advanced malware of threat actors. This trend particularly impacts organizations in the education sector, who often have less robust cyber defenses and significant periods of time during which infrastructure is left unmanned, and are therefore more vulnerable to quick-profit attacks.

Traditional security methods may fall short against these sophisticated attacks, where stealthy actors evade detection by human-managed teams and tools. In these scenarios Darktrace’s AI-driven product suite is able to quickly detect and respond to the initial signs of compromise through autonomous analysis of any unusual emerging activity.

When Darktrace’s Autonomous Response capability was active, it swiftly mitigated emerging Fog ransomware threats by quarantining devices exhibiting malicious behavior to contain the attack and blocking the exfiltration of sensitive data, thus preventing customers from falling victim to double extortion attempts.

Insights from Darktrace’s First 6: Half-year threat report for 2024

First 6: half year threat report darktrace screenshot

Darktrace’s First 6: Half-Year Threat Report 2024 highlights the latest attack trends and key threats observed by the Darktrace Threat Research team in the first six months of 2024.

  • Focuses on anomaly detection and behavioral analysis to identify threats
  • Maps mitigated cases to known, publicly attributed threats for deeper context
  • Offers guidance on improving security posture to defend against persistent threats

Appendices

Credit to Qing Hong Kwa (Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore) and Ryan Traill (Threat Content Lead)

Darktrace Model Detections:

- Anomalous Server Activity::Anomalous External Activity from Critical Network Device

- Anomalous Connection::SMB Enumeration

- Anomalous Connection::Suspicious Read Write Ratio and Unusual SMB

- Anomalous Connection::Uncommon 1 GiB Outbound

- Anomalous File::Internal::Additional Extension Appended to SMB File

- Compliance::Possible Cleartext LDAP Authentication

- Compliance::Remote Management Tool On Server

- Compliance::SMB Drive Write

- Compromise::Ransomware::SMB Reads then Writes with Additional Extensions

- Compromise::Ransomware::Possible Ransom Note Write

- Compromise::Ransomware::Ransom or Offensive Words Written to SMB

- Device::Attack and Recon Tools

- User::New Admin Credentials on Client

- Unusual Activity::Anomalous SMB Move & Write

- Unusual Activity::Internal Data Transfer

- Unusual Activity::Unusual External Data Transfer

- Unusual Activity::Enhanced Unusual External Data Transfer

Darktrace Model Detections:

- Antigena::Network::External Threat::Antigena Suspicious File Block

- Antigena::Network::External Threat::Antigena Suspicious File Pattern of Life Block

- Antigena::Network::External Threat::Antigena File then New Outbound Block

- Antigena::Network::External Threat::Antigena Ransomware Block

- Antigena::Network::External Threat::Antigena Suspicious Activity Block

- Antigena::Network::Significant Anomaly::Antigena Controlled and Model Breach

- Antigena::Network::Significant Anomaly::Antigena Enhanced Monitoring from Server Block

- Antigena::Network::Significant Anomaly::Antigena Breaches Over Time Block

- Antigena::Network::Significant Anomaly::Antigena Significant Server Anomaly Block

- Antigena::Network::Insider Threat::Antigena Internal Data Transfer Block

- Antigena::Network::Insider Threat::Antigena Large Data Volume Outbound Block

- Antigena::Network::Insider Threat::Antigena SMB Enumeration Block

AI Analyst Incident Coverage

- Encryption of Files over SMB

- Scanning of Multiple Devices

- SMB Writes of Suspicious Files

MITRE ATT&CK Mapping

(Technique Name) – (Tactic) – (ID) – (Sub-Technique of)

Data Obfuscation - COMMAND AND CONTROL - T1001

Remote System Discovery - DISCOVERY - T1018

SMB/Windows Admin Shares - LATERAL MOVEMENT - T1021.002 - T1021

Rename System Utilities - DEFENSE EVASION - T1036.003 - T1036

Network Sniffing - CREDENTIAL ACCESS, DISCOVERY - T1040

Exfiltration Over C2 Channel - EXFILTRATION - T1041

Data Staged - COLLECTION - T1074

Valid Accounts - DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS - T1078

Taint Shared Content - LATERAL MOVEMENT - T1080

File and Directory Discovery - DISCOVERY - T1083

Email Collection - COLLECTION - T1114

Automated Collection - COLLECTION - T1119

Network Share Discovery - DISCOVERY - T1135

Exploit Public-Facing Application - INITIAL ACCESS - T1190

Hardware Additions - INITIAL ACCESS - T1200

Remote Access Software - COMMAND AND CONTROL - T1219

Data Encrypted for Impact - IMPACT - T1486

Pass the Hash - DEFENSE EVASION, LATERAL MOVEMENT - T1550.002 - T1550

Exfiltration to Cloud Storage - EXFILTRATION - T1567.002 - T1567

Lateral Tool Transfer - LATERAL MOVEMENT - T1570

List of Indicators of Compromise (IoCs)

IoC – Type – Description

/AnyDesk.exe - Executable File - Remote Access Management Tool

gfs302n515[.]userstorage[.]mega[.]co[.]nz- Domain - Exfiltration Domain

*.flocked - Filename Extension - Fog Ransomware Extension

readme.txt - Text File - Fog Ransom Note

xql562evsy7njcsngacphcerzjfecwotdkobn3m4uxu2gtqh26newid[.]onion - Onion Domain - Threat Actor’s Communication Channel

References

[1] https://arcticwolf.com/resources/blog/lost-in-the-fog-a-new-ransomware-threat/

[2] https://intel471.com/blog/assessing-the-disruptions-of-ransomware-gangs

[3] https://www.pcrisk.com/removal-guides/30167-fog-ransomware

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About the author
Qing Hong Kwa
Senior Cyber Analyst and Deputy Analyst Team Lead, Singapore
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