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September 6, 2023

The Rise of the Lumma Info-Stealer | Malware-as-a-Service

The emergence of Lumma Stealer, an information stealer that has recently been observed across the Darktrace fleet. Learn more about this new 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
Emily Megan Lim
Cyber Analyst
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06
Sep 2023

What are Malware-as-a-Service information stealers?

The Malware-as-a-Service (MaaS) model continues provide would-be threat actors with an inexpensive and relatively straightforward way to carry out sophisticated cyber attacks and achieve their nefarious goals. One common type of MaaS are information stealers that specialize in gathering and exfiltrating sensitive data, such as login credentials and bank details, from affected devices, potentially resulting in significant financial losses for organizations and individuals alike.

What is Lumma Information Stealer?

One such information stealer, dubbed “Lumma”, has been advertised and sold on numerous dark web forums since 2022. Lumma stealer primarily targets cryptocurrency wallets, browser extensions and two-factor authentication (2FA), before ultimately stealing sensitive information from compromised machines. The number of sightings of this malware being distributed on dark web forums is on the rise [1], and thus far, more than a dozen command-and-control (C2) servers have been observed in the wild.

Between January and April 2023, Darktrace observed and investigated multiple instances of Lumma stealer activity across the customer base. Thanks to its anomaly-based approach to threat detection, Darktrace / NETWORK is able to successfully identify and provide visibility over activity associated with such info-stealers, from C2 activity through to the eventual exfiltration of sensitive data.

Lumma Stealer Background

Lumma stealer, previously known as LummaC2, is a subscription-based information stealer that has been observed in the wild since 2022.

It is believed to have been developed by the threat actor “Shamel”, under the the alias “Lumma”. The info-stealer has been advertised on dark web forums and also a channel on the Telegram messenger server, which boasts over a thousand subscribers as of May 2023 [2], and is also available on Lumma’s official seller page for as little as USD 250 (Figure 1).

LummaC2’s official seller website
Figure 1: LummaC2’s official seller website [3].

Research on the Russian Market selling stolen credentials has shown that Lumma stealer has been an emerging since early 2023, and joins the list of info stealers that have been on the rise, including Vidar and Racoon [1].

Similar to other info-stealers, Lumma is able to obtain system and installed program data from compromised devices, alongside sensitive information such as cookies, usernames and passwords, credit card numbers, connection history, and cryptocurrency wallet data.

Between January and April 2023, Darktrace has observed Lumma malware activity across multiple customer deployments mostly in the EMEA region, but also in the US. This included data exfiltration to external endpoints related to the Lumma malware. It is likely that this activity resulted from the download of trojanized software files or users falling victim to malicious emails containing Lumma payloads.

Lumma Attack Details and Darktrace Coverage

Typically, Lumma has been distributed disguised as cracked or fake popular software like VLC or ChatGPT. Recently though, threat actors have also delivered the malware through emails containing payloads in the form of attachments or links impersonating well-known companies. For example, in February 2023, a streamer in South Korea was targeted with a spear-phishing email in which the sender impersonated the video game company Bandai Namco [4].

Lumma is known to target Windows operating systems from Windows 7 to 11 and at least 10 different browsers including Google Chrome, Microsoft Edge, and Mozilla Firefox [5]. It has also been observed targeting crypto wallets like Binance and Ethereum, as well as crypto wallet and 2FA browser extensions like Metamask and Authenticator respectively [6]. Data from applications such as AnyDesk or KeePass can also be exfiltrated by the malware [7].

An infection with Lumma can lead to the user's information being abused for fraud, for example, using stolen credentials to hijack bank accounts, which in turn could result in significant financial losses.

Once the targeted data is obtained, it is exfiltrated to a C2 server, as Darktrace has observed on multiple customer environments affected with Lumma stealer. Darktrace identified multiple infected devices exfiltrating data via HTTP POST requests to known Lumma C2 servers. During these connections, Darktrace commonly observed the URI “/c2sock” and the user agent “TeslaBrowser/5.5”.

In one instance, Darktrace detected a device using the “TeslaBrowser/5.5” user agent, which it recognized as a new user agent for this device, whilst making a HTTP post request to an unusual IP address, 82.117.255[.]127 (Figure 3). Darktrace’s Self-Learning AI understood that this represented a deviation from expected behavior for this device and brought it to the attention of the customer’s security team.

Device Event Log on the Darktrace Threat Visualizer showing activity from a device infected with Lumma stealer and the models it breached.
Figure 2: Device Event Log on the Darktrace Threat Visualizer showing activity from a device infected with Lumma stealer and the models it breached.

Further investigation revealed that accessing the IP address using a web browser and changing the the URI to “/login”, would take a user to a Russian Lumma control panel access page (Figure 4)

 One of Lumma stealer’s C2 servers accessed via a web browser in a secured environment.
Figure 3: One of Lumma stealer’s C2 servers accessed via a web browser in a secured environment.

A deep dive into the packet captures (PCAP) of the HTTP POST requests taken from one device also confirmed that browser data, including Google Chrome history files, system information in the form of a System.txt file, and other program data such as AnyDesk configuration files were being exfiltrated from the customer’s network(Figures 5 and 6).

HTTP objects observed during Lumma Stealer POSTing of data to another one of its  C2 servers.
Figure 4: HTTP objects observed during Lumma Stealer POSTing of data to another one of its  C2 servers.
PCAP of HTTP stream showing the different types of data being exfiltrated.
Figure 5: PCAP of HTTP stream showing the different types of data being exfiltrated.

Additionally, on one particular device, Darktrace observed malicious external connections related to other malware strains, like Laplas Clipper, Raccoon Stealer, Vidar, RedLine info-stealers and trojans, around the same time as the Lumma C2 connections. These info-stealers are commonly marketed as MaaS and can be bought and used for a relatively inexpensive price by even the most inexperienced threat actors. It is also likely that the developers of these info-stealers have been making efforts to integrate their strains into the activities of traffer teams [8], organized cybercrime groups who specialize in credential theft with the use of info-stealers.

Conclusion

Mirroring the general emergence and rise of information stealers across the cyber threat landscape, Lumma stealer continues to represent a significant concern to orgaizations and individuals alike.

Moreover, as yet another example of MaaS, Lumma is readily available for threat actors to launch their attacks, regardless of their level of expertise, meaning the number of incidents is only likely to rise. As such, it is essential for organizations to have security measures in place that are able to recognize unusual behavior that may be indicative of an info-stealer compromise, while not relying on a static list of indicators of compromise (IoCs).

Darktrace's anomaly-based detection enabled it to uncover the presence of Lumma across multiple customer environments across different regions and industries. From the detection of unusual connections to C2 infrastructure to the ultimate exfiltration of customer data, Darktrace provided affected customers full visibility over Lumma infections, allowing them to identify compromised devices and take action to prevent further data loss and reduce the risk of incurring significant financial losses.

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Appendices

Credit to: Emily Megan Lim, Cyber Security Analyst, Signe Zaharka, Senior Cyber Security Analyst

Darktrace DETECT Models

·      Anomalous Connection / New User Agent to IP Without Hostname  

·      Device / New User Agent and New IP

·      Device / New User Agent

·      Anomalous Connection / Posting HTTP to IP Without Hostname

Cyber AI Analyst Incidents

·      Possible HTTP Command and Control

·      Possible HTTP Command and Control to Multiple Endpoints

List of IoCs

IoC - Type - Description + Confidence

144.76.173[.]247

IP address

Lumma C2 Infrastructure

45.9.74[.]78

IP address

Lumma C2 Infrastructure

77.73.134[.]68

IP address

Lumma C2 Infrastructure

82.117.255[.]127

IP address

Lumma C2 Infrastructure

82.117.255[.]80

IP address

Lumma C2 Infrastructure

82.118.23[.]50

IP address

Lumma C2 Infrastructure

/c2sock

URI

Lumma C2 POST Request

TeslaBrowser/5.5

User agent

Lumma C2 POST Request

MITRE ATT&CK Mapping

Tactic: Command and Control -

Technique: T1071.001 – Web Protocols

References

[1] https://www.kelacyber.com/wp-content/uploads/2023/05/KELA_Research_Infostealers_2023_full-report.pdf

[2] https://www.bleepingcomputer.com/news/security/the-new-info-stealing-malware-operations-to-watch-out-for/

[3] https://blog.cyble.com/2023/01/06/lummac2-stealer-a-potent-threat-to-crypto-users/

[4] https://medium.com/s2wblog/lumma-stealer-targets-youtubers-via-spear-phishing-email-ade740d486f7

[5] https://socradar.io/malware-analysis-lummac2-stealer/

[6] https://outpost24.com/blog/everything-you-need-to-know-lummac2-stealer

[7] https://asec.ahnlab.com/en/50594/

[8] https://blog.sekoia.io/bluefox-information-stealer-traffer-maas/

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
Emily Megan Lim
Cyber Analyst

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April 21, 2025

Why Asset Visibility and Signature-Based Threat Detection Fall Short in ICS Security

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In the realm of Industrial Control System (ICS) security, two concepts often dominate discussions:

  1. Asset visibility
  2. Signature-based threat detection

While these are undoubtedly important components of a cybersecurity strategy, many organizations focus on them as the primary means to enhance ICS security. However, this is more of a short-term approach and these organizations often realize too late that these efforts do not translate into actually securing their environment.

To truly secure your environment, organizations should focus their efforts on anomaly detection across core network segments. This shift enables enhanced threat detection, while also providing a more meaningful and dynamic view of asset communication.

By prioritizing anomaly detection, organizations can build a more resilient security posture, detecting and mitigating threats before they escalate into serious incidents.

The shortcomings of asset visibility and signature-based threat detection

Asset visibility is frequently touted as the foundation of ICS security. The idea is that you cannot protect what you cannot see.

However, organizations that invest heavily in asset discovery tools often end up with extensive inventories of connected devices but little actionable insight into their security posture or risk level, let alone any indication as to whether these assets have been compromised.

Simply knowing what assets exist does not equate to securing them.

Worse, asset discovery is often a time-consuming static process. By the time practitioners complete their inventory, not only is there likely to have been changes to their assets, but the threat landscape may have already evolved, introducing new vulnerabilities and attack vectors  that were not previously accounted for.

Signature-based detection is reactive, not proactive

Traditional signature-based threat detection relies on known attack patterns and predefined signatures to identify malicious activity. This approach is fundamentally reactive because it can only detect threats that have already been identified elsewhere.

In an ICS environment where cyber-attacks on OT systems have become more frequent, sophisticated, and destructive, signature-based detection provides a false sense of security while failing to detect sophisticated, previously unseen threats:

Additionally, adversaries often dwell within OT networks for extended periods, studying their specific conditions to identify the most effective way to cause disruption. This means that the likelihood of any attack within OT network looking the same as a previous attack is unlikely.

Implementation effort vs. actual security gains

Many organizations spend considerable time and resources implementing asset visibility solutions and signature-based detection systems only to be required to constantly tune and adjust the sensitivity of the solution.

Despite these efforts, these tools often fail to deliver the level of protection expected, leaving gaps in detection, an overwhelming amount of asset data, and a constant stream of false positives and false negatives from signature-based systems.

A more effective approach: Anomaly detection at core network segments

While it's important to understand the type of device involved during alert triage, organizations should shift their focus from static asset visibility and threat signatures to anomaly detection across critical network segments. This method provides a superior approach to ICS security for several reasons:

Proactive threat detection

Anomaly detection monitors network behavior in real time and identifies deviations . This means that even novel or previously unseen threats can be detected based on unusual network activity, rather than relying on predefined signatures.

Granular security insights

By analyzing traffic patterns across key network segments, organizations can gain deeper insights into how assets interact. This not only improves threat detection but also organically enhances asset visibility. Instead of simply cataloging devices, organizations gain meaningful visibility into how they behave within the network, understanding their unique pattern of life, and making it easier to detect malicious activity.

Efficiency and scalability

Implementing anomaly detection allows security teams to focus on real threats rather than sifting through massive inventories of assets or managing signature updates. It scales better with evolving threats and provides continuous monitoring without requiring constant manual intervention.

Enhanced threat detection for critical infrastructure

Unlike traditional security approaches that rely on static baselines or threat intelligence that doesn't reflect the unique behaviors of your OT environment, Darktrace / OT uses multiple AI techniques to continuously learn and adapt to your organization’s real-world activity across IT, OT, and IoT.

By building a dynamic understanding of each device’s pattern of life, it detects threats at every stage of the kill chain — from known malware to zero-days and insider attacks — without overwhelming your team with false positives or unnecessary alerts. This ensures scalable protection as your environment evolves, without a significant increase in operational overhead.

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About the author
Jeffrey Macre
Industrial Security Solutions Architect

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April 16, 2025

Introducing Version 2 of Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2)

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DEMIST-2 is Darktrace’s latest embedding model, built to interpret and classify security data with precision. It performs highly specialized tasks and can be deployed in any environment. Unlike generative language models, DEMIST-2 focuses on providing reliable, high-accuracy detections for critical security use cases.

DEMIST-2 Core Capabilities:  

  • Enhances Cyber AI Analyst’s ability to triage and reason about security incidents by providing expert representation and classification of security data, and as a part of our broader multi-layered AI system
  • Classifies and interprets security data, in contrast to language models that generate unpredictable open-ended text responses  
  • Incorporates new innovations in language model development and architecture, optimized specifically for cybersecurity applications
  • Deployable across cloud, on-prem, and edge environments, DEMIST-2 delivers low-latency, high-accuracy results wherever it runs. It enables inference anywhere.

Cybersecurity is constantly evolving, but the need to build precise and reliable detections remains constant in the face of new and emerging threats. Darktrace’s Embedding Model for Investigation of Security Threats (DEMIST-2) addresses these critical needs and is designed to create stable, high-fidelity representations of security data while also serving as a powerful classifier. For security teams, this means faster, more accurate threat detection with reduced manual investigation. DEMIST-2's efficiency also reduces the need to invest in massive computational resources, enabling effective protection at scale without added complexity.  

As an embedding language model, DEMIST-2 classifies and creates meaning out of complex security data. This equips our Self-Learning AI with the insights to compare, correlate, and reason with consistency and precision. Classifications and embeddings power core capabilities across our products where accuracy is not optional, as a part of our multi-layered approach to AI architecture.

Perhaps most importantly, DEMIST-2 features a compact architecture that delivers analyst-level insights while meeting diverse deployment needs across cloud, on-prem, and edge environments. Trained on a mixture of general and domain-specific data and designed to support task specialization, DEMIST-2 provides privacy-preserving inference anywhere, while outperforming larger general-purpose models in key cybersecurity tasks.

This proprietary language model reflects Darktrace's ongoing commitment to continually innovate our AI solutions to meet the unique challenges of the security industry. We approach AI differently, integrating diverse insights to solve complex cybersecurity problems. DEMIST-2 shows that a refined, optimized, domain-specific language model can deliver outsized results in an efficient package. We are redefining possibilities for cybersecurity, but our methods transfer readily to other domains. We are eager to share our findings to accelerate innovation in the field.  

The evolution of DEMIST-2

Key concepts:  

  • Tokens: The smallest units processed by language models. Text is split into fragments based on frequency patterns allowing models to handle unfamiliar words efficiently
  • Low-Rank Adaptors (LoRA): Small, trainable components added to a model that allow it to specialize in new tasks without retraining the full system. These components learn task-specific behavior while the original foundation model remains unchanged. This approach enables multiple specializations to coexist, and work simultaneously, without drastically increasing processing and memory requirements.

Darktrace began using large language models in our products in 2022. DEMIST-2 reflects significant advancements in our continuous experimentation and adoption of innovations in the field to address the unique needs of the security industry.  

It is important to note that Darktrace uses a range of language models throughout its products, but each one is chosen for the task at hand. Many others in the artificial intelligence (AI) industry are focused on broad application of large language models (LLMs) for open-ended text generation tasks. Our research shows that using LLMs for classification and embedding offers better, more reliable, results for core security use cases. We’ve found that using LLMs for open-ended outputs can introduce uncertainty through inaccurate and unreliable responses, which is detrimental for environments where precision matters. Generative AI should not be applied to use cases, such as investigation and threat detection, where the results can deeply matter. Thoughtful application of generative AI capabilities, such as drafting decoy phishing emails or crafting non-consequential summaries are helpful but still require careful oversight.

Data is perhaps the most important factor for building language models. The data used to train DEMIST-2 balanced the need for general language understanding with security expertise. We used both publicly available and proprietary datasets.  Our proprietary dataset included privacy-preserving data such as URIs observed in customer alerts, anonymized at source to remove PII and gathered via the Call Home and aianalyst.darktrace.com services. For additional details, read our Technical Paper.  

DEMIST-2 is our way of addressing the unique challenges posed by security data. It recognizes that security data follows its own patterns that are distinct from natural language. For example, hostnames, HTTP headers, and certificate fields often appear in predictable ways, but not necessarily in a way that mirrors natural language. General-purpose LLMs tend to break down when used in these types of highly specialized domains. They struggle to interpret structure and context, fragmenting important patterns during tokenization in ways that can have a negative impact on performance.  

DEMIST-2 was built to understand the language and structure of security data using a custom tokenizer built around a security-specific vocabulary of over 16,000 words. This tokenizer allows the model to process inputs more accurately like encoded payloads, file paths, subdomain chains, and command-line arguments. These types of data are often misinterpreted by general-purpose models.  

When the tokenizer encounters unfamiliar or irregular input, it breaks the data into smaller pieces so it can still be processed. The ability to fall back to individual bytes is critical in cybersecurity contexts where novel or obfuscated content is common. This approach combines precision with flexibility, supporting specialized understanding with resilience in the face of unpredictable data.  

Along with our custom tokenizer, we made changes to support task specialization without increasing model size. To do this, DEMIST-2 uses LoRA . LoRA is a technique that integrates lightweight components with the base model to allow it to perform specific tasks while keeping memory requirements low. By using LoRA, our proprietary representation of security knowledge can be shared and reused as a starting point for more highly specialized models, for example, it takes a different type of specialization to understand hostnames versus to understand sensitive filenames. DEMIST-2 dynamically adapts to these needs and performs them with purpose.  

The result is that DEMIST-2 is like having a room of specialists working on difficult problems together, while sharing a basic core set of knowledge that does not need to be repeated or reintroduced to every situation. Sharing a consistent base model also improves its maintainability and allows efficient deployment across diverse environments without compromising speed or accuracy.  

Tokenization and task specialization represent only a portion of the updates we have made to our embedding model. In conjunction with the changes described above, DEMIST-2 integrates several updated modeling techniques that reduce latency and improve detections. To learn more about these details, our training data and methods, and a full write-up of our results, please read our scientific whitepaper.

DEMIST-2 in action

In this section, we highlight DEMIST-2's embeddings and performance. First, we show a visualization of how DEMIST-2 classifies and interprets hostnames, and second, we present its performance in a hostname classification task in comparison to other language models.  

Embeddings can often feel abstract, so let’s make them real. Figure 1 below is a 2D visualization of how DEMIST-2 classifies and understands hostnames. In reality, these hostnames exist across many more dimensions, capturing details like their relationships with other hostnames, usage patterns, and contextual data. The colors and positions in the diagram represent a simplified view of how DEMIST-2 organizes and interprets these hostnames, providing insights into their meaning and connections. Just like an experienced human analyst can quickly identify and group hostnames based on patterns and context, DEMIST-2 does the same at scale.  

DEMIST-2 visualization of hostname relationships from a large web dataset.
Figure 1: DEMIST-2 visualization of hostname relationships from a large web dataset.

Next, let’s zoom in on two distinct clusters that DEMIST-2 recognizes. One cluster represents small businesses (Figure 2) and the other, Russian and Polish sites with similar numerical formats (Figure 3). These clusters demonstrate how DEMIST-2 can identify specific groupings based on real-world attributes such as regional patterns in website structures, common formats used by small businesses, and other properties such as its understanding of how websites relate to each other on the internet.

Cluster of small businesses
Figure 2: Cluster of small businesses
Figure 3: Cluster of Russian and Polish sites with a similar numerical format

The previous figures provided a view of how DEMIST-2 works. Figure 4 highlights DEMIST-2’s performance in a security-related classification task. The chart shows how DEMIST-2, with just 95 million parameters, achieves nearly 94% accuracy—making it the highest-performing model in the chart, despite being the smallest. In comparison, the larger model with 2.78 billion parameters achieves only about 89% accuracy, showing that size doesn’t always mean better performance. Small models don’t mean poor performance. For many security-related tasks, DEMIST-2 outperforms much larger models.

Hostname classification task performance comparison against comparable open source foundation models
Figure 4: Hostname classification task performance comparison against comparable open source foundation models

With these examples of DEMIST-2 in action, we’ve shown how it excels in embedding and classifying security data while delivering high performance on specialized security tasks.  

The DEMIST-2 advantage

DEMIST-2 was built for precision and reliability. Our primary goal was to create a high-performance model capable of tackling complex cybersecurity tasks. Optimizing for efficiency and scalability came second, but it is a natural outcome of our commitment to building a strong, effective solution that is available to security teams working across diverse environments. It is an enormous benefit that DEMIST-2 is orders of magnitude smaller than many general-purpose models. However, and much more importantly, it significantly outperforms models in its capabilities and accuracy on security tasks.  

Finding a product that fits into an environment’s unique constraints used to mean that some teams had to settle for less powerful or less performant products. With DEMIST-2, data can remain local to the environment, is entirely separate from the data of other customers, and can even operate in environments without network connectivity. The size of our model allows for flexible deployment options while at the same time providing measurable performance advantages for security-related tasks.  

As security threats continue to evolve, we believe that purpose-built AI systems like DEMIST-2 will be essential tools for defenders, combining the power of modern language modeling with the specificity and reliability that builds trust and partnership between security practitioners and AI systems.

Conclusion

DEMIST-2 has additional architectural and deployment updates that improve performance and stability. These innovations contribute to our ability to minimize model size and memory constraints and reflect our dedication to meeting the data handling and privacy needs of security environments. In addition, these choices reflect our dedication to responsible AI practices.

DEMIST-2 is available in Darktrace 6.3, along with a new DIGEST model that uses GNNs and RNNs to score and prioritize threats with expert-level precision.

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About the author
Margaret Cunningham, PhD
Director, Security & AI Strategy, Field CISO
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