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September 13, 2022

Compliance Threat: RedLine Information Stealer

Darktrace reveals the compliance risks posed by the RedLine information stealer. Read about their analysis and how to defend against this cyber threat.
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
Steven Sosa
Analyst Team Lead
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13
Sep 2022

With the continued rise of malware as a service (MaaS), it is now easier than ever to find and deploy information stealers [1]. Given this, it is crucial that companies begin to prioritize good cyber hygiene, and address compliance issues within their environments. Thanks to MaaS, attackers with little to no experience can amplify what might seem like a low-risk attack, into a significant compromise. This blog will investigate a compromise that could have been mitigated with better cyber hygiene and enhanced awareness around compliance issues.

Figure 1: Timeline of the attack

In May 2022 Darktrace DETECT/Network identified a device linked with multiple compliance alerts for ‘torrent’ activity within a Latin American telecommunications company. This culminated in the device downloading a suspicious executable file from an archived webpage. At first, analysis of the downloaded file indicated that it could be a legitimate, albeit outdated software relevant to the client’s industry vertical (SNMPc management tool for GeoDesy GD-300). However, as this was the first event before further suspicious activities, it was also possible that the software downloaded was packaged with malware and marked an initial compromise. Since early April, the device had regularly breached compliance alerts for both BitTorrent and uTorrent (a BitTorrent client). These connections occurred over a common torrenting port, 6881, and may have represented the infection vector.  

Figure 2: View of archived webpage which the suspicious executable was downloaded from

Shortly after the executable was downloaded, Darktrace DETECT alerted a new outbound SSH connection with the following notice in Advanced Search: ‘SSH::Heuristic_Login_Success’. This was highlighted because the breach device did not commonly make connections over this protocol and the destination was a never-before-seen Bulgarian IP address (79.142.70[.]239). The connection lasted 4 minutes, and the device downloaded 31.36 MB of data. 

Following this, the breach device was seen making unusual HTTP connections to rare Russian and Danish endpoints using suspicious user agents. The Russian endpoint was noted for hosting a text file (‘incricinfo[.]com') that listed a single domain which was recently registered. The connections to the Danish endpoint were made to an IP with a URI that OSINT connected to the use of the BeamWinHTTP loader [2]. This loader can be used to download and execute other malware strains, in particular information stealers [3]. 

Figure 3: Screenshot of Russian endpoint with link to incricinfo[.]com 
Figure 4: Cyber AI Analyst highlighting the unusual HTTP connectivity that occurred prior to the multiple suspicious file downloads

At the same time as the connections with the unusual user agents, the device was also seen downloading an executable file from the endpoint, ‘Yuuichirou-hanma[.]s3[.]pl-waw[.]scw[.]cloud’. Analysis of the file indicated that it may be used to deploy further malware and potentially unwanted programs (PUPs). BeamWinHTTP also causes installation of these PUPs which helps to load more nefarious programs and spread compromise. 

This behavior was then seen as the device downloaded 5 different executable files from the endpoint, ‘hakhaulogistics[.]com’. This domain is linked to a Vietnamese logistics company that Darktrace had marked as new within the environment; it is possible that this domain was compromised and being used to host malicious infrastructure. At the point of compromise, several of the downloads were labeled as malicious by popular OSINT [4]. Additionally, at least one of the files was explicitly linked to the RedLine Information Stealer.  

Shortly after, the device made connections to a known Tor relay node. Tor is commonly used as an avenue for C2 communication as it offers a way for attackers to anonymize and obfuscate their activity. It was at this point that the first Proactive Threat Notification (PTN) for this activity occurred. This ensured immediate follow-up investigation from Darktrace SOC and a timeline of events and impacted devices were issued to the customer’s security team directly. 

Figure 5: Cyber AI Analyst highlighting the unusual executable downloads as well as the subsequent Tor connections. The file poweroff[.]exe has been highlighted by several OSINT sources as being potentially malicious

By this point, Darktrace had identified a large volume of unusual outbound HTTP POSTs to a variety of endpoints that seemed to have no obvious function or service. Following these POST requests, the compromised device was seen initiating a long SSL connection to the domain, ‘www[.]qfhwji6fnpiad3gs[.]com’, which is likely to have be generated by an algorithm (DGA). Lastly, a little while after the SSL connections, the device was seen downloading another executable file from the Russian domain ‘test-hf[.]su’. Research on the file again suggested that it was associated with RedLine Stealer [5].  

Figure 6: AIA highlighting additional unusual HTTP connections that were linked with the numeric exe download

Dangers of Non-Compliance 

Whilst the RedLine compromise was a matter of customer concern, the gap in their security was not visibility but rather best practice. It is important to note that prior to these events, the device was commonly seen sending and receiving connections associated with torrenting. In the past it has been observed that RedLine Stealer masquerades as ‘cracked’ software (software that has had its copy protection removed) [6]. In this instance, the initial download of the false ‘SNMPc’ executable may have been proof of this behavior. 

This is a reminder that torrenting is also extremely popular as a peer-to-peer vector for transferring malicious files. Combined with the possibility of network throttling or unapproved VPN use, torrents are usually considered non-compliant within corporate settings. Whether the events here were kickstarted due to a user unwittingly downloading malicious software, or exposure to a malicious actor via BitTorrent use, both cases represent a user circumventing existing compliance controls or a lack of compliance control in general. It is important for organizations to make sure that their users are acting in ways that limit the company’s exposure to nefarious actors. Companies should routinely encourage proper cyber hygiene and implement access controls that block certain activities such as torrenting if threats like these are to be stopped in the future.  

Regardless of what users are doing, Darktrace is positioned to detect and take action on compliance breaches and activity resulting from lack of compliance. The variety of C2 domains used in this blog incident were too quick for most security tools to alert on or for human teams to triage. However, this was no problem for Cyber AI analyst, which was able to draw together aspects of the attack across the kill chain and save a significant amount of time for both the customer security team and Darktrace SOC analysts. If active, Darktrace RESPOND could have blocked activities like the initial BitTorrent connections and incoming download, but with the right preventative measures, it wouldn’t have to. Darktrace PREVENT works continuously to harden defenses and preempt attackers, closing any vulnerabilities before they can be exploited. This includes performing attack surface management, attack path modelling, and security awareness training. In this case, Darktrace PREVENT could have highlighted torrenting activity as part of a potentially harmful attack path and recommended the best actions to mitigate it.

‘No Prior Experience required’ 

In the past, only highly skilled attackers could create and use the tools needed to attack organizations. With Ransomware-as-a-Service (RaaS) proving highly profitable, however, it is no surprise that malware is also becoming a lucrative business. As SaaS can help legitimate companies with no development experience to use and maintain apps, MaaS can help attackers with little to no hacking experience compromise organizations and achieve their goals. RedLine Stealer is readily available, and not prohibitively expensive, meaning attacks can be carried out more frequently, and on a wider range of victims. The incident explored in this blog is proof of this, and a strong indication that security comes not only from strong visibility but also compliance and best practice too. With a powerful defensive tool like PREVENT, security teams can save time while feeling confident that they are keeping ahead of these aspects of security.

Thanks to Adam Stevens for his contributions to this blog.

Appendices

Darktrace Model Breaches

·      Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External 

·      Anomalous File / Numeric Exe Download

·      Anomalous Server Activity / New User Agent from Internet Facing System

·      Compliance / SSH to Rare External Destination

·      Compromise / Anomalous File then Tor 

·      Compromise / Possible Tor Usage 

·      Device / Initial Breach Chain Compromise

·      Device / Long Agent Connection to New Endpoint

References

[1] https://blog.sonicwall.com/en-us/2021/12/the-rise-and-growth-of-malware-as-a-service/

[2] https://asec.ahnlab.com/en/33679/  

[3] https://asec.ahnlab.com/en/20930/

[4] https://www.virustotal.com/gui/file/acfc06b4bcda03ecf4f9dc9b27c510b58ae3a6a9baf1ee821fc624467944467b & https://www.virustotal.com/gui/file/dad6311f96df65f40d9599c84907bae98306f902b1489b03768294b7678a5e79 

[5] https://www.virustotal.com/gui/file/ff7574f9f1d15594e409bee206f5db6c76db7c90dda2ae4f241b77cd0c7b6bf6

[6] https://asec.ahnlab.com/en/30445/

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
Steven Sosa
Analyst Team Lead

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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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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.

Sign up today to stay informed about innovations across securing AI.

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Jamie Bali
Technical Author (AI) Developer
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