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January 30, 2023

Qakbot Resurgence in the Cyber Landscape

Stay informed on the evolving threat Qakbot. Protect yourself from the Qakbot resurgence! Learn more from our Darktrace AI Cybersecurity experts!
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
Nahisha Nobregas
SOC Analyst
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30
Jan 2023

In June 2022, Darktrace observed a surge in Qakbot infections across its client base. The detected Qakbot infections, which in some cases led to the delivery of secondary payloads such as Cobalt Strike and Dark VNC, were initiated through novel delivery methods birthed from Microsoft’s default blocking of XL4 and VBA macros in early 2022 [1]/[2]/[3]/[4] and from the public disclosure in May 2022 [5] of the critical Follina vulnerability (CVE-2022-30190) in Microsoft Support Diagnostic Tool (MSDT). Despite the changes made to Qakbot’s delivery methods, Qakbot infections still inevitably resulted in unusual patterns of network activity. In this blog, we will provide details of these network activities, along with Darktrace/Network’s coverage of them. 

Qakbot Background 

Qakbot emerged in 2007 as a banking trojan designed to steal sensitive data such as banking credentials.  Since then, Qakbot has developed into a highly modular triple-threat powerhouse used to not only steal information, but to also drop malicious payloads and to serve as a backdoor. The malware is also versatile, with its delivery methods regularly changing in response to the changing threat landscape.  

Threat actors deliver Qakbot through email-based delivery methods. In the first half of 2022, Microsoft started rolling out versions of Office which block XL4 and VBA macros by default. Prior to this change, Qakbot email campaigns typically consisted in the spreading of deceitful emails with Office attachments containing malicious macros.  Opening these attachments and then enabling the macros within them would lead users’ devices to install Qakbot.  

Actors who deliver Qakbot onto users’ devices may either sell their access to other actors, or they may leverage Qakbot’s capabilities to pursue their own objectives [6]. A common objective of actors that use Qakbot is to drop Cobalt Strike beacons onto infected systems. Actors will then leverage the interactive access provided by Cobalt Strike to conduct extensive reconnaissance and lateral movement activities in preparation for widespread ransomware deployment. Qakbot’s close ties to ransomware activity, along with its modularity and versatility, make the malware a significant threat to organisations’ digital environments.

Activity Details and Qakbot Delivery Methods

During the month of June, variationsof the following pattern of network activity were observed in several client networks:

1.     User’s device contacts an email service such as outlook.office[.]com or mail.google[.]com

2.     User’s device makes an HTTP GET request to 185.234.247[.]119 with an Office user-agent string and a ‘/123.RES' target URI. The request is responded to with an HTML file containing a exploit for the Follina vulnerability (CVE-2022-30190)

3.     User’s device makes an HTTP GET request with a cURL User-Agent string and a target URI ending in ‘.dat’ to an unusual external endpoint. The request is responded to with a Qakbot DLL sample

4.     User’s device contacts Qakbot Command and Control servers over ports such as 443, 995, 2222, and 32101

In some cases, only steps 1 and 4 were seen, and in other cases, only steps 1, 3, and 4 were seen. The different variations of the pattern correspond to different Qakbot delivery methods.

Figure 1: Geographic distribution of Darktrace clients affected by Qakbot

Qakbot is known to be delivered via malicious email attachments [7]. The Qakbot infections observed across Darktrace’s client base during June were likely initiated through HTML smuggling — a method which consists in embedding malicious code into HTML attachments. Based on open-source reporting [8]-[14] and on observed patterns of network traffic, we assess with moderate to high confidence that the Qakbot infections observed across Darktrace’s client base during June 2022 were initiated via one of the following three methods:

  • User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a LNK file, which when opened, causes the user's device to make an external HTTP GET request with a cURL User-Agent string and a '.dat' target URI. If successful, the HTTP GET request is responded to with a Qakbot DLL.
  • User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a docx file, which when opened, causes the user's device to make an HTTP GET request to 185.234.247[.]119 with an Office user-agent string and a ‘/123.RES' target URI. If successful, the HTTP GET request is responded to with an HTML file containing a Follina exploit. The Follina exploit causes the user's device to make an external HTTP GET with a '.dat' target URI. If successful, the HTTP GET request is responded to with a Qakbot DL.
  • User opens HTML attachment which drops a ZIP file on their device. ZIP file contains a Qakbot DLL and a LNK file, which when opened, causes the DLL to run.

The usage of these delivery methods illustrate how threat actors are adopting to a post-macro world [4], with their malware delivery techniques shifting from usage of macros-embedding Office documents to usage of container files, Windows Shortcut (LNK) files, and exploits for novel vulnerabilities. 

The Qakbot infections observed across Darktrace’s client base did not only vary in terms of their delivery methods — they also differed in terms of their follow-up activities. In some cases, no follow-up activities were observed. In other cases, however, actors were seen leveraging Qakbot to exfiltrate data and to deliver follow-up payloads such as Cobalt Strike and Dark VNC.  These follow-up activities were likely preparation for the deployment of ransomware. Darktrace’s early detection of Qakbot activity within client environments enabled security teams to take actions which likely prevented the deployment of ransomware. 

Darktrace Coverage 

Users’ interactions with malicious email attachments typically resulted in their devices making cURL HTTP GET requests with empty Host headers and target URIs ending in ‘.dat’ (such as as ‘/24736.dat’ and ‘/noFindThem.dat’) to rare, external endpoints. In cases where the Follina vulnerability is believed to have been exploited, users’ devices were seen making HTTP GET requests to 185.234.247[.]119 with a Microsoft Office User-Agent string before making cURL HTTP GET requests. The following Darktrace DETECT/Network models typically breached as a result of these HTTP activities:

  • Device / New User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Device / New User Agent and New IP
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Numeric Exe Download 

These DETECT models were able to capture the unusual usage of Office and cURL User-Agent strings on affected devices, as well as the downloads of the Qakbot DLL from rare external endpoints. These models look for unusual activity that falls outside a device’s usual pattern of behavior rather than for activity involving User-Agent strings, URIs, files, and external IPs which are known to be malicious.

When enabled, Darktrace RESPOND/Network autonomously intervened, taking actions such as ‘Enforce group pattern of life’ and ‘Block connections’ to quickly intercept connections to Qakbot infrastructure. 

Figure 2: This ‘New User Agent to IP Without Hostname’ model breach highlights an example of Darktrace’s detection of a device attempting to download a file containing a Follina exploit
Figure 3: This ‘New User Agent to IP Without Hostname’ model breach highlights an example of Darktrace’s detection of a device attempting to download Qakbot
Figure 4: The Event Log for an infected device highlights the moment a connection to the endpoint outlook.office365[.]com was made. This was followed by an executable file transfer detection and use of a new User-Agent, curl/7.9.1

After installing Qakbot, users’ devices started making connections to Command and Control (C2) endpoints over ports such as 443, 22, 990, 995, 1194, 2222, 2078, 32101. Cobalt Strike and Dark VNC may have been delivered over some of these C2 connections, as evidenced by subsequent connections to endpoints associated with Cobalt Strike and Dark VNC. These C2 activities typically caused the following Darktrace DETECT/Network models to breach: 

  • Anomalous Connection / Application Protocol on Uncommon Port
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Compromise / Suspicious Beaconing Behavior
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / Large Number of Suspicious Successful Connections
  • Compromise / Sustained SSL or HTTP Increase
  • Compromise / SSL or HTTP Beacon
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Anomalous SSL without SNI to New External
  • Compromise / SSL Beaconing to Rare Destination
  • Compromise / Suspicious TLS Beaconing To Rare External
  • Compromise / Slow Beaconing Activity To External Rare
Figure 5: This Device Event Log illustrates the Command and Control activity displayed by a Qakbot-infected device

The Darktrace DETECT/Network models which detected these C2 activities do not look for devices making connections to known, malicious endpoints. Rather, they look for devices deviating from their ordinary patterns of activity, making connections to external endpoints which internal devices do not usually connect to, over ports which devices do not normally connect over. 

In some cases, actors were seen exfiltrating data from Qakbot-infected systems and dropping Cobalt Strike in order to conduct extensive discovery. These exfiltration activities typically caused the following models to breach:

  • Anomalous Connection / Data Sent to Rare Domain
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration to IP
  • Unusual Activity / Unusual External Data to New Endpoints

The reconnaissance and brute-force activities carried out by actors typically resulted in breaches of the following models:

  • Device / ICMP Address Scan
  • Device / Network Scan
  • Anomalous Connection / SMB Enumeration
  • Device / New or Uncommon WMI Activity
  •  Unusual Activity / Possible RPC Recon Activity
  • Device / Possible SMB/NTLM Reconnaissance
  •  Device / SMB Lateral Movement
  •  Device / Increase in New RPC Services
  •  Device / Spike in LDAP Activity
  • Device / Possible SMB/NTLM Brute Force
  • Device / SMB Session Brute Force (Non-Admin)
  • Device / SMB Session Brute Force (Admin)
  • Device / Anomalous NTLM Brute Force

Conclusion

June 2022 saw Qakbot swiftly mould itself in response to Microsoft's default blocking of macros and the public disclosure of the Follina vulnerability. The evolution of the threat landscape in the first half of 2022 caused Qakbot to undergo changes in its delivery methods, shifting from delivery via macros-based methods to delivery via HTML smuggling methods. The effectiveness of these novel delivery methods where highlighted in Darktrace's client base, where large volumes of Qakbot infections were seen during June 2022. Leveraging Self-Learning AI, Darktrace DETECT/Network was able to detect the unusual network behaviors which inevitably resulted from these novel Qakbot infections. Given that the actors behind these Qakbot infections were likely seeking to deploy ransomware, these detections, along with Darktrace RESPOND/Network’s autonomous interventions, ultimately helped to protect affected Darktrace clients from significant business disruption.  

Appendices

List of IOCs

References

[1] https://techcommunity.microsoft.com/t5/excel-blog/excel-4-0-xlm-macros-now-restricted-by-default-for-customer/ba-p/3057905

[2] https://techcommunity.microsoft.com/t5/microsoft-365-blog/helping-users-stay-safe-blocking-internet-macros-by-default-in/ba-p/3071805

[3] https://learn.microsoft.com/en-us/deployoffice/security/internet-macros-blocked

[4] https://www.proofpoint.com/uk/blog/threat-insight/how-threat-actors-are-adapting-post-macro-world

[5] https://twitter.com/nao_sec/status/1530196847679401984

[6] https://www.microsoft.com/security/blog/2021/12/09/a-closer-look-at-qakbots-latest-building-blocks-and-how-to-knock-them-down/

[7] https://www.zscaler.com/blogs/security-research/rise-qakbot-attacks-traced-evolving-threat-techniques

[8] https://www.esentire.com/blog/resurgence-in-qakbot-malware-activity

[9] https://www.fortinet.com/blog/threat-research/new-variant-of-qakbot-spread-by-phishing-emails

[10] https://twitter.com/pr0xylife/status/1539320429281615872

[11] https://twitter.com/max_mal_/status/1534220832242819072

[12] https://twitter.com/1zrr4h/status/1534259727059787783?lang=en

[13] https://isc.sans.edu/diary/rss/28728

[14] https://www.fortiguard.com/threat-signal-report/4616/qakbot-delivered-through-cve-2022-30190-follina

Credit to:  Hanah Darley, Cambridge Analyst Team Lead and Head of Threat Research and Sam Lister, Senior Cyber Analyst

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
Nahisha Nobregas
SOC 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
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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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