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February 27, 2025

New Threat on the Prowl: Investigating Lynx Ransomware

Lynx ransomware, emerging in 2024, targets finance, architecture, and manufacturing sectors with phishing and double extortion. Read on for Darktrace's findings.
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.
Written by
Justin Torres
Cyber Analyst
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27
Feb 2025

What is Lynx ransomware?

In mid-2024, a new ransomware actor named Lynx emerged in the threat landscape. This Ransomware-as-a-Service (RaaS) strain is known to target organizations in the finance, architecture, and manufacturing sectors [1] [2]. However, Darktrace’s Threat Research teams also identified Lynx incidents affecting energy and retail organizations in the Middle East and Asia-Pacific (APAC) regions. Despite being a relatively new actor, Lynx’s malware shares large portions of its source code with the INC ransomware variant, suggesting that the group may have acquired and repurposed the readily available INC code to develop its own strain [2].

What techniques does Lynx ransomware group use?

Lynx employs several common attack vectors, including phishing emails which result in the download and installation of ransomware onto systems upon user interaction. The group poses a sophisticated double extortion threat to organizations, exfiltrating sensitive data prior to encryption [1]. This tactic allows threat actors to pressure their targets by threatening to release sensitive information publicly or sell it if the ransom is not paid. The group has also been known to gradually release small batches of sensitive information (i.e., “drip” data) to increase pressure.

Once executed, the malware encrypts files and appends the extension ‘.LYNX’ to all encrypted files. It eventually drops a Base64 encoded text file as a ransom note (i.e., README.txt) [1]. Should initial file encryption attempts fail, the operators have been known to employ privilege escalation techniques to ensure full impact [2].

In the Annual Threat Report 2024, Darktrace’s Threat Research team identified Lynx ransomware as one of the top five most significant threats, impacting both its customers and the broader threat landscape.

Darktrace Coverage of Lynx Ransomware

In cases of Lynx ransomware observed across the Darktrace customer base, Darktrace / NETWORK identified and suggested Autonomous Response actions to contain network compromises from the onset of activity.  

Detection of lateral movement

One such Lynx compromise occurred in December 2024 when Darktrace observed multiple indicators of lateral movement on a customer network. The lateral movement activity started with a high volume of attempted binds to the service control endpoint of various destination devices, suggesting SMB file share enumeration. This activity also included repeated attempts to establish internal connections over destination port 445, as well as other privileged ports. Spikes in failed internal connectivity, such as those exhibited by the device in question, can indicate network scanning. Elements of the internal connectivity also suggested the use of the attack and reconnaissance tool, Nmap.

Indicators of compromised administrative credentials

Although an initial access point could not be confirmed, the widespread use of administrative credentials throughout the lateral movement process demonstrated the likely compromise of such privileged usernames and passwords. The operators of the malware frequently used both 'admin' and 'administrator' credentials throughout the incident, suggesting that attackers may have leveraged compromised default administrative credentials to gain access and escalate privileges. These credentials were observed on numerous devices across the network, triggering Darktrace models that detect unusual use of administrative usernames via methods like NTLM and Kerberos.

Data exfiltration

The lateral movement and reconnaissance behavior was then followed by unusual internal and external data transfers. One such device exhibited an unusual spike in internal data download activity, downloading around 150 GiB over port 3260 from internal network devices. The device then proceeded to upload large volumes of data to the external AWS S3 storage bucket: wt-prod-euwest1-storm.s3.eu-west-1.amazonaws[.]com. Usage of external cloud storage providers is a common tactic to avoid detection of exfiltration, given the added level of legitimacy afforded by cloud service provider domains.

Furthermore, Darktrace observed the device exhibiting behavior suggesting the use of the remote management tool AnyDesk when it made outbound TCP connections to hostnames such as:

relay-48ce591e[.]net[.]anydesk[.]com

relay-c9990d24[.]net[.]anydesk[.]com

relay-da1ad7b4[.]net[.]anydesk[.]com

Tools like AnyDesk can be used for legitimate administrative purposes. However, such tools are also commonly leveraged by threat actors to enable remote access and further compromise activity. The activity observed from the noted device during this time suggests the tool was used by the ransomware operators to advance their compromise goals.

The observed activity culminated in the encryption of thousands of files with the '.Lynx' extension. Darktrace detected devices performing uncommon SMB write and move operations on the drives of destination network devices, featuring the appending of the Lynx extension to local host files. Darktrace also identified similar levels of SMB read and write sizes originating from certain devices. Parallel volumes of SMB read and write activity strongly suggest encryption, as the malware opens, reads, and then encrypts local files on the hosted SMB disk share. This encryption activity frequently highlighted the use of the seemingly-default credential: "Administrator".

In this instance, Darktrace’s Autonomous Response capability was configured to only take action upon human confirmation, meaning the customer’s security team had to manually apply any suggested actions. Had the deployment been fully autonomous, Darktrace would have blocked connectivity to and from the affected devices, giving the customer additional time to contain the attack and enforce existing network behavior patterns while the IT team responded accordingly.

Conclusion

As reported by Darktrace’s Threat Research team in the Annual Threat Report 2024, both new and old ransomware strains were prominent across the threat landscape last year. Due to the continually improving security postures of organizations, ransomware actors are forced to constantly evolve and adopt new tactics to successfully carry out their attacks.

The Lynx group’s use of INC source code, for example, suggests a growing accessibility for threat actors to launch new ransomware strains based on existing code – reducing the cost, resources, and expertise required to build new malware and carry out an attack. This decreased barrier to entry will surely lead to an increased number of ransomware incidents, with attacks not being limited to experienced threat actors.

While Darktrace expects ransomware strains like Lynx to remain prominent in the threat landscape in 2025 and beyond, Darktrace’s ability to identify and respond to emerging ransomware incidents – as demonstrated here – ensures that customers can safeguard their networks and resume normal business operations as quickly as possible, even in an increasingly complex threat landscape.

Credit to Justin Torres (Senior Cyber Analyst) and Adam Potter (Senior Cyber Analyst).

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Appendices

References

1.     https://unit42.paloaltonetworks.com/inc-ransomware-rebrand-to-lynx/

2.     https://cybersecsentinel.com/lynx-ransomware-strikes-new-targets-unveiling-advanced-encryption-techniques/

Autonomous Response Model Alerts

·      Antigena::Network::Significant Anomaly::Antigena Alerts Over Time Block

·      Antigena::Network::Insider Threat::Antigena Active Threat SMB Write Block

·      Antigena::Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

·      Antigena::Network::Significant Anomaly::Antigena Significant Anomaly from Client Block

·      Antigena::Network::Insider Threat::Antigena Network Scan Block

·      Antigena::Network::Insider Threat::Antigena Internal Anomalous File Activity

·      Antigena::Network::Insider Threat::Antigena Unusual Privileged User Activities Block

·      Antigena::Network::Insider Threat::Antigena Unusual Privileged User Activities Pattern of Life Block

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

Darktrace / NETWORK Model Alerts

·      Device::Multiple Lateral Movement Model Alerts

·      Device::Suspicious Network Scan Activity

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

·      Device::SMB Lateral Movement

·      Compliance::SMB Drive Write

·      Compromise::Ransomware::Suspicious SMB Activity

·      Anomalous File::Internal::Unusual SMB Script Write

·      Device::Network Scan

·      Device::Suspicious SMB Scanning Activity

·      Device::RDP Scan

·      Unusual Activity::Anomalous SMB Move & Write

·      Anomalous Connection::Sustained MIME Type Conversion

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

·      Unusual Activity::Sustained Anomalous SMB Activity

·      Device::ICMP Address Scan

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

·      Anomalous Connection::Suspicious Read Write Ratio

·      Anomalous File::Internal::Masqueraded Executable SMB Write

·      Compliance::Possible Unencrypted Password File On Server

·      User::New Admin Credentials on Client

·      Compliance::Remote Management Tool On Server

·      User::New Admin Credentials on Server

·      Anomalous Connection::Unusual Admin RDP Session

·      Anomalous Connection::Download and Upload

·      Anomalous Connection::Uncommon 1 GiB Outbound

·      Unusual Activity::Unusual File Storage Data Transfer

List of IoCs

IoC - Type - Description + Confidence

- ‘. LYNX’ -  File Extension -  Lynx Ransomware file extension appended to encrypted files

MITRE ATT&CK Mapping  

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

Taint Shared Content - LATERAL MOVEMENT - T1080

Data Encrypted for - Impact - IMPACT T1486

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

Get the latest insights on emerging cyber threats

This report explores the latest trends shaping the cybersecurity landscape and what defenders need to know in 2025.

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.
Written by
Justin Torres
Cyber Analyst

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May 8, 2025

Anomaly-based threat hunting: Darktrace's approach in action

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What is threat hunting?

Threat hunting in cybersecurity involves proactively and iteratively searching through networks and datasets to detect threats that evade existing automated security solutions. It is an important component of a strong cybersecurity posture.

There are several frameworks that Darktrace analysts use to guide how threat hunting is carried out, some of which are:

  • MITRE Attack
  • Tactics, Techniques, Procedures (TTPs)
  • Diamond Model for Intrusion Analysis
  • Adversary, Infrastructure, Victims, Capabilities
  • Threat Hunt Model – Six Steps
  • Purpose, Scope, Equip, Plan, Execute, Feedback
  • Pyramid of Pain

These frameworks are important in baselining how to run a threat hunt. There are also a combination of different methods that allow defenders diversity– regardless of whether it is a proactive or reactive threat hunt. Some of these are:

  • Hypothesis-based threat hunting
  • Analytics-driven threat hunting
  • Automated/machine learning hunting
  • Indicator of Compromise (IoC) hunting
  • Victim-based threat hunting

Threat hunting with Darktrace

At its core, Darktrace relies on anomaly-based detection methods. It combines various machine learning types that allows it to characterize what constitutes ‘normal’, based on the analysis of many different measures of a device or actor’s behavior. Those types of learning are then curated into what are called models.

Darktrace models leverage anomaly detection and integrate outputs from Darktrace Deep Packet Inspection, telemetry inputs, and additional modules, creating tailored activity detection.

This dynamic understanding allows Darktrace to identify, with a high degree of precision, events or behaviors that are both anomalous and unlikely to be benign.  On top of machine learning models for detection, there is also the ability to change and create models showcasing the tool’s diversity. The Model Editor allows security teams to specify values, priorities, thresholds, and actions they want to detect. That means a team can create custom detection models based on specific use cases or business requirements. Teams can also increase the priority of existing detections based on their own risk assessments to their environment.

This level of dexterity is particularly useful when conducting a threat hunt. As described above, and in previous ‘Inside the SOC’ blogs such a threat hunt can be on a specific threat actor, specific sector, or a  hypothesis-based threat hunt combined with ‘experimenting’ with some of Darktrace’s models.

Conducting a threat hunt in the energy sector with experimental models

In Darktrace’s recent Threat Research report “AI & Cybersecurity: The state of cyber in UK and US energy sectors” Darktrace’s Threat Research team crafted hypothesis-driven threat hunts, building experimental models and investigating existing models to test them and detect malicious activity across Darktrace customers in the energy sector.

For one of the hunts, which hypothesised utilization of PerfectData software and multi-factor authentication (MFA) bypass to compromise user accounts and destruct data, an experimental model was created to detect a Software-as-a-Service (SaaS) user performing activity relating to 'PerfectData Software’, known to allow a threat actor to exfiltrate whole mailboxes as a PST file. Experimental model alerts caused by this anomalous activity were analyzed, in conjunction with existing SaaS and email-related models that would indicate a multi-stage attack in line with the hypothesis.

Whilst hunting, Darktrace researchers found multiple model alerts for this experimental model associated with PerfectData software usage, within energy sector customers, including an oil and gas investment company, as well as other sectors. Upon further investigation, it was also found that in June 2024, a malicious actor had targeted a renewable energy infrastructure provider via a PerfectData Software attack and demonstrated intent to conduct an Operational Technology (OT) attack.

The actor logged into Azure AD from a rare US IP address. They then granted Consent to ‘eM Client’ from the same IP. Shortly after, the actor granted ‘AddServicePrincipal’ via Azure to PerfectData Software. Two days later, the actor created a  new email rule from a London IP to move emails to an RSS Feed Folder, stop processing rules, and mark emails as read. They then accessed mail items in the “\Sent” folder from a malicious IP belonging to anonymization network,  Private Internet Access Virtual Private Network (PIA VPN) [1]. The actor then conducted mass email deletions, deleting multiple instances of emails with subject “[Name] shared "[Company Name] Proposal" With You” from the  “\Sent folder”. The emails’ subject suggests the email likely contains a link to file storage for phishing purposes. The mass deletion likely represented an attempt to obfuscate a potential outbound phishing email campaign.

The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.
Figure 1: The Darktrace Model Alert that triggered for the mass deletes of the likely phishing email containing a file storage link.

A month later, the same user was observed downloading mass mLog CSV files related to proprietary and Operational Technology information. In September, three months after the initial attack, another mass download of operational files occurred by this actor, pertaining to operating instructions and measurements, The observed patience and specific file downloads seemingly demonstrated an intent to conduct or research possible OT attack vectors. An attack on OT could have significant impacts including operational downtime, reputational damage, and harm to everyday operations. Darktrace alerted the impacted customer once findings were verified, and subsequent actions were taken by the internal security team to prevent further malicious activity.

Conclusion

Harnessing the power of different tools in a security stack is a key element to cyber defense. The above hypothesis-based threat hunt and custom demonstrated intent to conduct an experimental model creation demonstrates different threat hunting approaches, how Darktrace’s approach can be operationalized, and that proactive threat hunting can be a valuable complement to traditional security controls and is essential for organizations facing increasingly complex threat landscapes.

Credit to Nathaniel Jones (VP, Security & AI Strategy, Field CISO at Darktrace) and Zoe Tilsiter (EMEA Consultancy Lead)

References

  1. https://spur.us/context/191.96.106.219

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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May 6, 2025

Combatting the Top Three Sources of Risk in the Cloud

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With cloud computing, organizations are storing data like intellectual property, trade secrets, Personally Identifiable Information (PII), proprietary code and statistics, and other sensitive information in the cloud. If this data were to be accessed by malicious actors, it could incur financial loss, reputational damage, legal liabilities, and business disruption.

Last year data breaches in solely public cloud deployments were the most expensive type of data breach, with an average of $5.17 million USD, a 13.1% increase from the year before.

So, as cloud usage continues to grow, the teams in charge of protecting these deployments must understand the associated cybersecurity risks.

What are cloud risks?

Cloud threats come in many forms, with one of the key types consisting of cloud risks. These arise from challenges in implementing and maintaining cloud infrastructure, which can expose the organization to potential damage, loss, and attacks.

There are three major types of cloud risks:

1. Misconfigurations

As organizations struggle with complex cloud environments, misconfiguration is one of the leading causes of cloud security incidents. These risks occur when cloud settings leave gaps between cloud security solutions and expose data and services to unauthorized access. If discovered by a threat actor, a misconfiguration can be exploited to allow infiltration, lateral movement, escalation, and damage.

With the scale and dynamism of cloud infrastructure and the complexity of hybrid and multi-cloud deployments, security teams face a major challenge in exerting the required visibility and control to identify misconfigurations before they are exploited.

Common causes of misconfiguration come from skill shortages, outdated practices, and manual workflows. For example, potential misconfigurations can occur around firewall zones, isolated file systems, and mount systems, which all require specialized skill to set up and diligent monitoring to maintain

2. Identity and Access Management (IAM) failures

IAM has only increased in importance with the rise of cloud computing and remote working. It allows security teams to control which users can and cannot access sensitive data, applications, and other resources.

Cybersecurity professionals ranked IAM skills as the second most important security skill to have, just behind general cloud and application security.

There are four parts to IAM: authentication, authorization, administration, and auditing and reporting. Within these, there are a lot of subcomponents as well, including but not limited to Single Sign-On (SSO), Two-Factor Authentication (2FA), Multi-Factor Authentication (MFA), and Role-Based Access Control (RBAC).

Security teams are faced with the challenge of allowing enough access for employees, contractors, vendors, and partners to complete their jobs while restricting enough to maintain security. They may struggle to track what users are doing across the cloud, apps, and on-premises servers.

When IAM is misconfigured, it increases the attack surface and can leave accounts with access to resources they do not need to perform their intended roles. This type of risk creates the possibility for threat actors or compromised accounts to gain access to sensitive company data and escalate privileges in cloud environments. It can also allow malicious insiders and users who accidentally violate data protection regulations to cause greater damage.

3. Cross-domain threats

The complexity of hybrid and cloud environments can be exploited by attacks that cross multiple domains, such as traditional network environments, identity systems, SaaS platforms, and cloud environments. These attacks are difficult to detect and mitigate, especially when a security posture is siloed or fragmented.  

Some attack types inherently involve multiple domains, like lateral movement and supply chain attacks, which target both on-premises and cloud networks.  

Challenges in securing against cross-domain threats often come from a lack of unified visibility. If a security team does not have unified visibility across the organization’s domains, gaps between various infrastructures and the teams that manage them can leave organizations vulnerable.

Adopting AI cybersecurity tools to reduce cloud risk

For security teams to defend against misconfigurations, IAM failures, and insecure APIs, they require a combination of enhanced visibility into cloud assets and architectures, better automation, and more advanced analytics. These capabilities can be achieved with AI-powered cybersecurity tools.

Such tools use AI and automation to help teams maintain a clear view of all their assets and activities and consistently enforce security policies.

Darktrace / CLOUD is a Cloud Detection and Response (CDR) solution that makes cloud security accessible to all security teams and SOCs by using AI to identify and correct misconfigurations and other cloud risks in public, hybrid, and multi-cloud environments.

It provides real-time, dynamic architectural modeling, which gives SecOps and DevOps teams a unified view of cloud infrastructures to enhance collaboration and reveal possible misconfigurations and other cloud risks. It continuously evaluates architecture changes and monitors real-time activity, providing audit-ready traceability and proactive risk management.

Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.
Figure 1: Real-time visibility into cloud assets and architectures built from network, configuration, and identity and access roles. In this unified view, Darktrace / CLOUD reveals possible misconfigurations and risk paths.

Darktrace / CLOUD also offers attack path modeling for the cloud. It can identify exposed assets and highlight internal attack paths to get a dynamic view of the riskiest paths across cloud environments, network environments, and between – enabling security teams to prioritize based on unique business risk and address gaps to prevent future attacks.  

Darktrace’s Self-Learning AI ensures continuous cloud resilience, helping teams move from reactive to proactive defense.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance
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