Blog
/
Network
/
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
picture of someone doing a authentication password login Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
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.

[related-resource]

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

More in this series

No items found.

Blog

/

AI

/

December 23, 2025

How to Secure AI in the Enterprise: A Practical Framework for Models, Data, and Agents

How to secure AI in the enterprise: A practical framework for models, data, and agents Default blog imageDefault blog image

Introduction: Why securing AI is now a security priority

AI adoption is at the forefront of the digital movement in businesses, outpacing the rate at which IT and security professionals can set up governance models and security parameters. Adopting Generative AI chatbots, autonomous agents, and AI-enabled SaaS tools promises efficiency and speed but also introduces new forms of risk that traditional security controls were never designed to manage. For many organizations, the first challenge is not whether AI should be secured, but what “securing AI” actually means in practice. Is it about protecting models? Governing data? Monitoring outputs? Or controlling how AI agents behave once deployed?  

While demand for adoption increases, securing AI use in the enterprise is still an abstract concept to many and operationalizing its use goes far beyond just having visibility. Practitioners need to also consider how AI is sourced, built, deployed, used, and governed across the enterprise.

The goal for security teams: Implement a clear, lifecycle-based AI security framework. This blog will demonstrate the variety of AI use cases that should be considered when developing this framework and how to frame this conversation to non-technical audiences.  

What does “securing AI” actually mean?

Securing AI is often framed as an extension of existing security disciplines. In practice, this assumption can cause confusion.

Traditional security functions are built around relatively stable boundaries. Application security focuses on code and logic. Cloud security governs infrastructure and identity. Data security protects sensitive information at rest and in motion. Identity security controls who can access systems and services. Each function has clear ownership, established tooling, and well-understood failure modes.

AI does not fit neatly into any of these categories. An AI system is simultaneously:

  • An application that executes logic
  • A data processor that ingests and generates sensitive information
  • A decision-making layer that influences or automates actions
  • A dynamic system that changes behavior over time

As a result, the security risks introduced by AI cuts across multiple domains at once. A single AI interaction can involve identity misuse, data exposure, application logic abuse, and supply chain risk all within the same workflow. This is where the traditional lines between security functions begin to blur.

For example, a malicious prompt submitted by an authorized user is not a classic identity breach, yet it can trigger data leakage or unauthorized actions. An AI agent calling an external service may appear as legitimate application behavior, even as it violates data sovereignty or compliance requirements. AI-generated code may pass standard development checks while introducing subtle vulnerabilities or compromised dependencies.

In each case, no single security team “owns” the risk outright.

This is why securing AI cannot be reduced to model safety, governance policies, or perimeter controls alone. It requires a shared security lens that spans development, operations, data handling, and user interaction. Securing AI means understanding not just whether systems are accessed securely, but whether they are being used, trained, and allowed to act in ways that align with business intent and risk tolerance.

At its core, securing AI is about restoring clarity in environments where accountability can quickly blur. It is about knowing where AI exists, how it behaves, what it is allowed to do, and how its decisions affect the wider enterprise. Without this clarity, AI becomes a force multiplier for both productivity and risk.

The five categories of AI risk in the enterprise

A practical way to approach AI security is to organize risk around how AI is used and where it operates. The framework below defines five categories of AI risk, each aligned to a distinct layer of the enterprise AI ecosystem  

How to Secure AI in the Enterprise:

  • Defending against misuse and emergent behaviors
  • Monitoring and controlling AI in operation
  • Protecting AI development and infrastructure
  • Securing the AI supply chain
  • Strengthening readiness and oversight

Together, these categories provide a structured lens for understanding how AI risk manifests and where security teams should focus their efforts.

1. Defending against misuse and emergent AI behaviors

Generative AI systems and agents can be manipulated in ways that bypass traditional controls. Even when access is authorized, AI can be misused, repurposed, or influenced through carefully crafted prompts and interactions.

Key risks include:

  • Malicious prompt injection designed to coerce unwanted actions
  • Unauthorized or unintended use cases that bypass guardrails
  • Exposure of sensitive data through prompt histories
  • Hallucinated or malicious outputs that influence human behavior

Unlike traditional applications, AI systems can produce harmful outcomes without being explicitly compromised. Securing this layer requires monitoring intent, not just access. Security teams need visibility into how AI systems are being prompted, how outputs are consumed, and whether usage aligns with approved business purposes

2. Monitoring and controlling AI in operation

Once deployed, AI agents operate at machine speed and scale. They can initiate actions, exchange data, and interact with other systems with little human oversight. This makes runtime visibility critical.

Operational AI risks include:

  • Agents using permissions in unintended ways
  • Uncontrolled outbound connections to external services or agents
  • Loss of forensic visibility into ephemeral AI components
  • Non-compliant data transmission across jurisdictions

Securing AI in operation requires real-time monitoring of agent behavior, centralized control points such as AI gateways, and the ability to capture agent state for investigation. Without these capabilities, security teams may be blind to how AI systems behave once live, particularly in cloud-native or regulated environments.

3. Protecting AI development and infrastructure

Many AI risks are introduced long before deployment. Development pipelines, infrastructure configurations, and architectural decisions all influence the security posture of AI systems.

Common risks include:

  • Misconfigured permissions and guardrails
  • Insecure or overly complex agent architectures
  • Infrastructure-as-Code introducing silent misconfigurations
  • Vulnerabilities in AI-generated code and dependencies

AI-generated code adds a new dimension of risk, as hallucinated packages or insecure logic may be harder to detect and debug than human-written code. Securing AI development means applying security controls early, including static analysis, architectural review, and continuous configuration monitoring throughout the build process.

4. Securing the AI supply chain

AI supply chains are often opaque. Models, datasets, dependencies, and services may come from third parties with varying levels of transparency and assurance.

Key supply chain risks include:

  • Shadow AI tools used outside approved controls
  • External AI agents granted internal access
  • Suppliers applying AI to enterprise data without disclosure
  • Compromised models, training data, or dependencies

Securing the AI supply chain requires discovering where AI is used, validating the provenance and licensing of models and data, and assessing how suppliers process and protect enterprise information. Without this visibility, organizations risk data leakage, regulatory exposure, and downstream compromise through trusted integrations.

5. Strengthening readiness and oversight

Even with strong technical controls, AI security fails without governance, testing, and trained teams. AI introduces new incident scenarios that many security teams are not yet prepared to handle.

Oversight risks include:

  • Lack of meaningful AI risk reporting
  • Untested AI systems in production
  • Security teams untrained in AI-specific threats

Organizations need AI-aware reporting, red and purple team exercises that include AI systems, and ongoing training to build operational readiness. These capabilities ensure AI risks are understood, tested, and continuously improved, rather than discovered during a live incident.

Reframing AI security for the boardroom

AI security is not just a technical issue. It is a trust, accountability, and resilience issue. Boards want assurance that AI-driven decisions are reliable, explainable, and protected from tampering.

Effective communication with leadership focuses on:

  • Trust: confidence in data integrity, model behavior, and outputs
  • Accountability: clear ownership across teams and suppliers
  • Resilience: the ability to operate, audit, and adapt under attack or regulation

Mapping AI security efforts to recognized frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework helps demonstrate maturity and aligns AI security with broader governance objectives.

Conclusion: Securing AI is a lifecycle challenge

The same characteristics that make AI transformative also make it difficult to secure. AI systems blur traditional boundaries between software, users, and decision-making, expanding the attack surface in subtle but significant ways.

Securing AI requires restoring clarity. Knowing where AI exists, how it behaves, who controls it, and how it is governed. A framework-based approach allows organizations to innovate with AI while maintaining trust, accountability, and control.

The journey to secure AI is ongoing, but it begins with understanding the risks across the full AI lifecycle and building security practices that evolve alongside the technology.

Continue reading
About the author
Brittany Woodsmall
Product Marketing Manager, AI & Attack Surface

Blog

/

AI

/

December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

2026 cyber threat trendsDefault blog imageDefault blog image

Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple:  to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.  

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.  

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.  

Agentic AI is the next big insider risk

In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy

Prompt Injection moves from theory to front-page breach

We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.  

-- Collin Chapleau, Senior Director of Security & AI Strategy

Humans are even more outpaced, but not broken

When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

AI removes the attacker bottleneck—smaller organizations feel the impact

One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.  

-- Max Heinemeyer, Global Field CISO

SaaS platforms become the preferred supply chain target

Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.  

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.  

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.  

Continue reading
About the author
The Darktrace Community
Your data. Our AI.
Elevate your network security with Darktrace AI