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February 12, 2018

The Rise of Cryptocurrency Attacks & Cyber Defense Solutions

Darktrace can detect cryptocurrency-related attacks with machine learning. Identify nefarious use of resources and protect against Coinhive drive-by mining.
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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12
Feb 2018

Prelude

The last 12 months have shown tremendous volatility in the value of cryptocurrencies, of which Bitcoin is the most prominent example. At the start of 2017, Bitcoin lingered around the $2,000 mark before suddenly taking off, climbing to historic highs of close to $20,000 in December 2017. Demand has since subsided, and at the time of writing, the price of Bitcoin is near to $10,772.

While Bitcoin is the most popular cryptocurrency, numerous alternatives, often called ‘altcoins’ have emerged and grown in value in the last 12 months. For example, Dogecoin, originally created to be a spoof cryptocurrency after a widespread internet meme, reached a notable market capitalization milestone of $2bn in January 2018.

Nowadays it is almost impossible to profitably mine Bitcoin on commodity hardware such as laptops, smartphones or desktop computers. At this late state, it just takes too long to perform the relevant calculations, and the cost of electricity is higher than the anticipated revenue in most cases. Other altcoins such as Monero use different algorithms, making them viable alternatives for aspiring crypto miners. It is often still feasible to mine altcoins on commodity hardware and see a return on investment.

The value of most altcoins is closely tied to the value of Bitcoin and, in many cases, the relationship is broadly proportional – a rise in Bitcoin prompting a similar lift in the altcoins. Monero, which has been rapidly adopted by Darknet markets, has profited from this effect. While Monero was valued at around $10 in January 2017, its price has been pumped up to $419 a year later.

There is much that is still not clear about the cryptocurrency phenomenon. Debate as to its relative value and its status as a currency rages, and will not be resolved any time soon. However, from a cyber security perspective there can be no doubt that the combination of altcoins being mineable on commodity hardware, the fact that mining is now becoming profitable as a side-effect of Bitcoin’s rise, and a maturity in cryptocurrency-related tech has led to a surge in cryptocurrency-related attacks.

Attack vectors

Darktrace has observed an abrupt increase of cryptocurrency-related attacks over the last 12 months. Both the frequency and the diversity of these attacks has grown significantly and largely mirrors the remarkable rise in the value of Bitcoin over that period.

Previously, cyber-criminals monetized their operations via banking Trojans/credit card fraud, selling stolen data and ransomware on the Darknet. However, criminals are notoriously adaptable and will follow the money wherever it leads, leading to an increase in cryptojacking’s popularity.

Cryptocurrency mining might not be as profitable as ransomware is upfront, but it can be secretly pursued for months without creating the havoc that characterizes ransomware attacks. Most users and security products might not notice a cryptocurrency miner being installed on a corporate device as it does not show obvious threats or messages to a user, except for an occasional increase in CPU or RAM usage.

Identifying these attacks can be very difficult for traditional security tools as they were not originally designed to catch this type of threat. Nor was Darktrace, but its approach – which relies on its evolving understanding of patterns of behavior – means that it can detect such attacks without having to know what to look for in advance.

Darktrace has detected a number of different attack vectors related to cryptocurrency attacks.

  1. Nefarious use of corporate resources
    Darktrace has detected a range of incidents where employees were intentionally installing cryptocurrency mining software on their corporate devices to mine for personal gain. These employees do not have to pay for the electricity used to run the corporate device in the office – they are basically turning their employer’s electricity into cash by commandeering it for mining operations.

    This is commonly seen as a compliance breach and increases the attack surface of a device that has mining software installed. It puts the corporate device at risk and also increases operational costs as the power consumption usually goes up for mining devices. The most popular cryptocurrency choices for this kind of mining in the last 12 months were Etherium and Monero – altcoins that can profitably be mined without the need for inordinate electricity.
  2. Coinhive drive-by mining
    Coinhive is a technology that allows website owners to use their visitors’ computing power to mine a tiny fraction of cryptocurrency for the website owner. Visitors will experience a small increase in computer resource consumption while browsing the website. Some websites experiment with this model to create new forms of revenue streams alternative to advertisement and banner placements.

    Coinhive usage is often not an opt-in process. Darktrace has observed various customer devices that regularly visit websites leveraging Coinhive technology. While the power consumption increase for a device browsing a website with Coinhive is ultimately negligible, the cumulative effect of a sizeable portion of the workforce unwittingly browsing websites using Coinhive results in increased power consumption cost for the organization as a whole.
  3. Malicious insider
    A malicious insider compromised his employer’s website to put a Coinhive script on there. This then mined Monero for every visitor on the employer’s website for the malicious insider’s personal gain.
  4. Traditional malware
    Cyber criminals are constantly looking to improve the return on investment of their operations. Reports suggest that criminals are starting to adjust their monetization methods based on the financial means of their targets. Suppose you can’t pay the fee extorted in a ransomware attack? They’ll just install a crypto miner on your device instead to ensure that the attack is not completely fruitless.

    As malware authors become more sophisticated, they often deploy multi-staged malware that can swap weaponized payloads. Once malware has infected a system successfully, its authors can often decide what actions to take next. Encrypt the device and extort a ransom? Install a banking Trojan to harvest credit card details? Install more spyware modules to look for data exfiltration? Or, now, install a cryptocurrency miner.

    These pieces of malware operate stealthily and often go undetected for several weeks. An infection might start with a phishing email that contains a macro-enabled document. As soon as a user enabled the macro, the malware will download a file-less stager that lives in memory and cannot be detected by traditional antivirus. Command and control communication is usually maintained via IP addresses that change on a daily basis in order to outrun threat intelligence and blacklisting attempts. As no obvious damage is done straight away, these attacks often stay under the radar for prolonged times, so long as self-learning technology such as Darktrace is not employed.

    This becomes much more concerning as malware authors could swap one payload for another overnight if they deem it more profitable, switching from a furtive crypto mining Trojan to ransomware the next day. While we have not observed this kind of attack in the wild yet, it is plausible, and in cyberspace what can be done, will be done.

Conclusions

Revolutionary technologies like cryptocurrencies have both their dark and light aspects. For all of the creative energy released by the crypto-blockchain revolution, Bitcoin and its alternatives have quickly become the universal currency of the criminal underworld. Indeed, the former Chief Economist of the World Bank, Joseph Stiglitz – an adamant critic of cryptocurrencies – has said that the whole value of Bitcoin resides in its “potential for circumvention” and “lack of oversight”.

While Stiglitz’s case may be overstated, there can be no question that cyber criminals have sensed a new opportunity to make money. A lot of organizations still regard crypto mining as a compliance incident. This can lead to grave consequences as a cryptocurrency mining device might lead to more severe incidents that can have a serious effect on business operations.

This kind of threat is difficult to detect as no obvious damage is done. However, with Darktrace’s machine learning we can correlate even the weakest indicators of such an attack into a compelling picture of threat. While traditional tools may struggle to see these deviations, Darktrace can pinpoint the changes in behavior effected by cryptocurrency miners without having to rely on any blacklists or signatures.

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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May 21, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response

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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

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Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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