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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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September 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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About the author
Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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About the author
Dr. Tim Bazalgette
Chief AI Officer, Darktrace
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