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July 25, 2021

Detecting Lateral Movement in Crypto-Botnets

Explore how crypto botnets move laterally within networks and the implications for cybersecurity and threat detection.
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
Max Heinemeyer
Global Field CISO
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25
Jul 2021

Botnets have increasingly become the vehicle of choice to deliver crypto-mining malware. By infecting various corporate assets such as servers and IoT devices, cyber-criminals can use the collective processing power of hundreds – or thousands – of machines to mine cryptocurrency and spread to further devices.

This blog explores how an Internet-facing server was breached in a company in Singapore. The threat actors used the device to move laterally and deploy crypto-mining software. Within two days, several devices in the company had begun cryptocurrency mining.

Creating the botnet

Only a few days after Darktrace had been installed in a Proof of Value (POV) trial, it detected a server in the company downloading a malicious executable from a rare endpoint, 167.71.87[.]85.

Figure 1: Timeline of the attack.

The server was observed making HTTP connections to a range of rare external endpoints, without a user agent header. The main hostname was t[.]amynx[.]com, a domain on open-source intelligence (OSINT) associated with crypto-mining trojans.

The device initiated repeated external connections to a range of external IPs over the TCP port 445 (SMB). This was followed by an unusually large number of internal connection attempts to a wide range of devices, suggesting scanning activity.

Figure 2: Details for the TCP scanning activity in a similar incident — note the consolidation of six relevant events into one summary.

Growing the botnet

The malware began to move laterally from the initially infected server, predominantly by establishing chains of unsual RDP connections. Subsequently, the server started making external SMB and RPC connections to rare endpoints on the Internet, in an attempt to find further vulnerable hosts.

Other lateral movement activities included the repeated failing attempts to access multiple internal devices over the SMB file-sharing protocol, with a range of different usernames. This implies bruteforce network access, as the threat actor attempted to guess correct account details through trial and error.

Existing tools such as RDP and Windows Service Control reveal that the attacker was employing ‘Living off the Land’ techniques. This makes a system administrator’s job inherently harder, as they must distinguish the malicious use of built-in tools versus their legitimate application.

Crypto-mining begins

Finally, the compromised server completed the lateral movement by transferring suspicious executable files over SMB to multiple internal devices, with names that appear randomly generated (e.g. gMtWAvEc.exe, daSsZhPf.exe) to deploy crypto-mining malware using the Minergate protocol.

Minergate is a public mining pool utilized for several types of cryptocurrency including Bitcoin, Monero, Ethereum, Zcash, and Grin. In recent months, ransomware actors have begun shifting away from Bitcoin towards Monero and other more anonymous cryptocurrences – but crypto-miners have been using altcoins for years.

Figure 3: The graph shows a clear increase in model breaches on a similar device, which easily identifies the time frame for the compromise.

As this was part of a trial, Antigena – Darktrace’s Autonomous Response capability – was not in active mode and so could not take action to stop the initial vector of infection. However, the Antigena model “Antigena / Network / External Threat / Antigena Suspicious File Block” was breached on July 18 at 03:55:45. If active, Antigena would have instantly blocked connections to 167.71.87[.]85 on port 80 for two hours, allowing the security team enough time to remediate the breach.

Crypto-mining malware: All the rage

Crypto-mining attacks are extremely common. Although not as destructive as ransomware, they can have a serious impact on network latency and take a long time to detect and clean up. While the infection remains unnoticed, it provides a backdoor into the victim organization – and could switch from conducting crypto-mining to delivering ransomware at any moment. In this case, it is clear the attacker aimed to create maximum disruption by transferring malicious software with targets such as internal servers and domain controllers.

Darktrace detected every step of the attack without relying on known indicators of threat. Cyber AI Analyst automated the complete investigation process, saving the security team crucial time during the live incident.

Especially with the recent crackdowns on Bitcoin farms in China, underground botnets and cloud-based crypto-mining are likely to become more prominent. As we see more of these intrusions in the near future, AI-powered detection, investigation, and response, will prove critical in defending organizations of all sizes, at all times.

Learn more about crypto-mining malware

IoCs:

IoCComment167.71.87[.]85Malware Download — SHA1: 6a4c477ba19a7bb888540d02acdd9be0d5d3fd02VirusTotalt[.]amynx[.]comHTTP Command and Control – recently created domain with suspicious indicators on OSINT sites (associated with cryptomining trojans)AlienVaultVirusTotallplp[.]ackng[.]comCrypto Currency Mining Activity (Minergate)VirusTotalgMtWAvEc.exedaSsZhPf.exeyAElKPQi.exeExamples of malicious executables

Darktrace model breaches:

  • Antigena / Network / Insider Threat / Antigena Network Scan Block
  • Device / Suspicious Network Scan Activity
  • Device / Large Number of Model Breaches
  • Device / Multiple Lateral Movement Model Breaches (x2)
  • Unusual Activity / Successful Admin Bruteforce Activity
  • Anomalous Connection / SMB Enumeration
  • Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach (x2)
  • Antigena / Network / External Threat / Antigena Suspicious File Block
  • Compromise / Beacon to Young Endpoint (x4)
  • Device / Possible RPC Lateral Movement
  • Antigena / Network / Insider Threat / Antigena SMB Enumeration Block
  • Compromise / Beaconing Activity To External Rare (x5)
  • Anomalous Server Activity / Denial of Service Activity
  • Antigena / Network / External Threat / Antigena Suspicious Activity Block (x4)
  • Device / Large Number of Connections to New Endpoints
  • Device / Network Scan - Low Anomaly Score
  • Anomalous Connection / New or Uncommon Service Control (x3)
  • Device / New User Agent To Internal Server
  • Device / Anomalous RDP Followed By Multiple Model Breaches (x3)
  • Device / Anomalous SMB Followed By Multiple Model Breaches (x3)
  • Device / SMB Session Bruteforce (x2)
  • Device / Increased External Connectivity
  • Device / Network Scan
  • Compromise / High Volume of Connections with Beacon Score (x5)
  • Unusual Activity / Unusual External Activity (x3)
  • Anomalous Connection / Unusual Admin SMB Session
  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block
  • Compliance / SMB Drive Write (x3)
  • Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block (x14)
  • Compliance / Internet Facing RDP Server
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint (x5)
  • Compliance / Outbound RDP (x3)
  • Anomalous Server Activity / Rare External from Server (x5)
  • Compromise / Slow Beaconing Activity To External Rare (x8)
  • Anomalous Server Activity / Outgoing from Server (x2)
  • Device / New User Agent
  • Anomalous Connection / New Failed External Windows Connection (x5)
  • Compliance / External Windows Communications
  • Device / New Failed External Connections (x7)
  • Compliance / Crypto Currency Mining Activity (x9)
  • Compliance / Incoming Remote Desktop (x9)

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
Max Heinemeyer
Global Field CISO

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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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