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September 6, 2023

The Rise of MaaS & Lumma Info Stealer

Discover the rise of the Lumma info stealer and its implications for cybersecurity. Learn how this malware targets sensitive information.
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 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).

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, DETECT 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.

Figure 2: Device Event Log on the Darktrace DETECT Threat Visualizer showing activity from a device infected with Lumma stealer and the DETECT 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)

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).

Figure 4: HTTP objects observed during Lumma Stealer POSTing of data to another one of its  C2 servers.
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 indicactive 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.

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

Appendices

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

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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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May 20, 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

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May 20, 2026

State of AI Cybersecurity 2026: 77% of security stacks include AI, but trust is lagging

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Findings in this blog are taken from Darktrace’s annual State of AI Cybersecurity Report 2026.

AI is a contributing member of nearly every modern cybersecurity team. As we discussed earlier in this blog series, rapid AI adoption is expanding the attack surface in ways that security professionals have never before experienced while also empowering attackers to operate at unprecedented speed and scale. It’s only logical that defenders are harnessing the power of AI to fight back.

After all, AI can help cybersecurity teams spot the subtle signs of novel threats before humans can, investigate events more quickly and thoroughly, and automate response. But although AI has been widely adopted, this technology is also frequently misunderstood, and occasionally viewed with suspicion.

For CISOs, the cybersecurity marketplace can be noisy. Making sense of competing vendors’ claims to distinguish the solutions that truly deliver on AI’s full potential from those that do not isn’t always easy. Without a nuanced understanding of the different types of AI used across the cybersecurity stack, it is difficult to make informed decisions about which vendors to work with or how to gain the most value from their solutions. Many security leaders are turning to Managed Security Service Providers (MSSPs) for guidance and support.

The right kinds of AI in the right places?

Back in 2024, when we first conducted this annual survey, more than a quarter of respondents were only vaguely familiar with generative AI or hadn’t heard of it at all. Today, GenAI plays a role in 77% of security stacks. This percentage marks a rapid increase in both awareness and adoption over a relatively short period of time.

According to security professionals, different types of AI are widely integrated into cybersecurity tooling:

  • 67% report that their organization’s security stack uses supervised machine learning
  • 67% report that theirs uses agentic AI
  • 58% report that theirs uses natural language processing (NLP)
  • 35% report that theirs uses unsupervised machine learning

But their responses suggest that organizations aren’t always using the most valuable types of AI for the most relevant use cases.

Despite all the recent attention AI has gotten, supervised machine learning isn’t new. Cybersecurity vendors have been experimenting with models trained on hand-labeled datasets for over a decade. These systems are fed large numbers of examples of malicious activity – for instance, strains of ransomware – and use these examples to generalize common indicators of maliciousness – such as the TTPs of multiple known ransomware strains – so that the models can identify similar attacks in the future. This approach is more effective than signature-based detection, since it isn’t tied to an individual byte sequence or file hash. However, supervised machine learning models can miss patterns or features outside the training data set. When adversarial behavior shifts, these systems can’t easily pivot.

Unsupervised machine learning, by contrast, can identify key patterns and trends in unlabeled data without human input. This enables it to classify information independently and detect anomalies without needing to be taught about past threats. Unsupervised learning can continuously learn about an environment and adapt in real time.

One key distinction between supervised and unsupervised machine learning is that supervised learning algorithms require periodic updating and re-training, whereas unsupervised machine learning trains itself while it works.

The question of trust

Even as AI moves into the mainstream, security professionals are eyeing it with a mix of enthusiasm and caution. Although 89% say they have good visibility into the reasoning behind AI-generated outputs, 74% are limiting AI’s ability to take autonomous action in their SOC until explainability improves. 86% do not allow AI to take even small remediation actions without human oversight.

This model, commonly known as “human in the loop,” is currently the norm across the industry. It seems like a best-of-both-worlds approach that allows teams to experience the benefits of AI-accelerated response without relinquishing control – or needing to trust an AI system.

Keeping humans somewhat in the loop is essential for getting the best out of AI. Analysts will always need to review alerts, make judgement calls, and set guardrails for AI's behavior. Their input helps AI models better understand what “normal” looks like, improving their accuracy over time.

However, relying on human confirmation has real costs – it delays response, increases the cognitive burden analysts must bear, and creates potential coverage gaps when security teams are overwhelmed or unavailable. The traditional model, in which humans monitor and act on every alert, is no longer workable at scale.

If organizations depend too heavily on in-the-loop humans, they risk recreating the very problem AI is meant to solve: backlogs of alerts waiting for analyst review. Removing the human from the loop can buy back valuable time, which analysts can then invest in building a proactive security posture. They can also focus more closely on the most critical incidents, where human attention is truly needed.

Allowing AI to operate autonomously requires trust in its decision-making. This trust can be built gradually over time, with autonomous operations expanding as trust grows. But it also requires knowledge and understanding of AI — what it is, how it works, and how best to deploy it at enterprise scale.

Looking for help in all the right places

To gain access to these capabilities in a way that’s efficient and scalable, growing numbers of security leaders are looking for outsourced support. In fact, 85% of security professionals prefer to obtain new SOC capabilities in the form of a managed service.

This makes sense: Managed Security Service Providers (MSSPs) can deliver deep, continuously available expertise without the cost and complexity of building an in-house team. Outsourcing also allows organizations to scale security coverage up or down as needs change, stay current with evolving threats and regulatory requirements, and leverage AI-native detection and response without needing to manage the AI tools themselves.

Preferences for MSSP-delivered security operations are particularly strong in the education, energy (87%), and healthcare sectors. This makes sense: all are high-value targets for threat actors, and all tend to have limited cybersecurity budgets, so the need for a partner who can deliver affordable access to expertise at scale is strong. Retailers also voiced a strong preference for MSSP-delivered services. These companies are tasked with managing large volumes of consumer personal and financial data, and with transforming an industry traditionally thought of as a late adopter to a vanguard of cyber defense. Technology companies, too, have a marked preference for SOC capabilities delivered by MSSPs. This may simply be because they understand the complexity of the threat landscape – and the advantages of specialized expertise — so well.

In order to help as many organizations as possible – from major enterprises to small and midmarket companies – benefit from enterprise-grade, AI-native security, Darktrace is making it easier for MSSPs to deliver its technology. The ActiveAI Security Portal introduces an alert dashboard designed to increase the speed and efficiency of alert triage, while a new AI-powered managed email security solution is giving MSSPs an edge in the never-ending fight against advanced phishing attacks – helping partners as well as organizations succeed on the frontlines of cyber defense.

Explore the full State of AI Cybersecurity 2026 report for deeper insights into how security leaders are responding to AI-driven risks.

Learn more about securing AI in your enterprise.

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