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September 21, 2020

The Rise of Stealthy Malware in Public Organizations

Gain insights into how malware attempts to infiltrate public organizations to steal data and the defenses needed to combat these threats.
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
Max Heinemeyer
Global Field CISO
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21
Sep 2020

Cyber AI was recently deployed at a government organization in the EMEA region, where it was protecting over 10,000 devices by learning a sense of ‘self’ for each unique device in order to detect anomalous behavior. Just a week into the Darktrace trial, the AI detected a device which had been infected with malware beaconing to C2 endpoints via HTTP and SSL before downloading a suspicious file.

The attackers were using a strain of Glupteba malware in an attempt to steal sensitive information from browsers such as passwords and credit card information, as well as email account credentials. Given that this was a government agency, the consequences had the attackers been able to gain access to an employees’ account credentials could have been severe.

Darktrace’s Autonomous Response technology, Antigena, would have taken action to contain the threatening behavior, enforcing the device’s ‘pattern of life’ for five minutes and escalating its response as the severity of the threat escalated.

The attack occurred over the course of an hour on a Sunday, meaning the security team’s response time was likely slower than it would have been during a weekday.

Figure 1: A timeline of events

Details of the attack

Darktrace detected a device initiating encrypted connections to an external domain never seen before across the organization. The device had likely been infected before Darktrace was deployed, most likely through a malicious email attachment or link.

Newer strains of Glupteba also use malvertising which directs the user to a rare endpoint and forces an anomalous file download.

Darktrace’s AI detected the device downloading an executable file, atx777.exe, which appears to be associated with the stealer Taurus, accredited to the cyber-criminal group ‘Predator the Thief’.

Following this file download, the device initiated further encrypted connections to suspicious endpoints over unusual communication channels. At the same time, the device downloaded another executable file from a domain with an unusual user agent, ‘CertUtil URL Agent’.

A stealthy stealer

Malicious actors are using more sophisticated techniques to avoid traditional security tools. The Glupteba malware framework, which has seen a resurgence over the past few months, utilizes several evasion techniques, including sandbox detection.

Shortly after the payload is dropped, the malware examines the environment where it has been installed and will not execute any further processes if it detects the host machine is a sandbox. The malware is able to further conceal itself by excluding Glupteba files from Windows Defender, altering Firewall rules to allow command and control traffic, and by ‘Living off the Land,’ using tools preinstalled on the device such as CertUtil.

Despite these attempts at evasion, Darktrace’s Cyber AI easily detected the suspicious activity, which fell outside the ‘pattern of life’ for the device and the wider organization. Darktrace identified the activity as suspicious at the first stages of the attack, and the Cyber AI Analyst investigated the incident in full, revealing some crucial metrics, including the endpoints contacted.

Figure 2: AI Analyst’s detection and summary of the command and control traffic

Antigena responds

In this case, the malware had been installed on the device before Darktrace started monitoring the environment, however had Antigena been active it would have taken a precise response at every stage of the attack. At the beginning of the attack, Antigena would have blocked connections to the suspicious domain, zvwxstarserver17km[.]xyz for two hours, preventing any additional malicious downloads.

As the activity escalated, Antigena would have enforced a ‘pattern of life’ on the infected device and stopped any malicious command and control communications by blocking all outgoing traffic for one hour.

Concluding thoughts

As the race between cyber-criminals and security analysts continues, malware authors are employing increasingly sophisticated techniques to avoid detection. Although the Taurus stealer utilizes a number of these evasion techniques, Darktrace’s AI technology was able to not only alert and act on the malicious activity without disrupting business continuity, but did so despite the malware already being present on a device before the customer began leveraging Darktrace for cyber defense.

Had Antigena been deployed in active mode during this incident, it would have stopped the malware in its tracks at the initial stages, preventing any sensitive data from being removed from the government network. Critically, Antigena updated and escalated its actions in light of the evolving activity, and yet was still precise enough to ensure normal business operations were allowed to continue.

Despite Antigena being in passive mode, this case study demonstrates the power of Autonomous Response in intelligently acting to stop cyber-threats when human security resources are limited, or when the team is out of office. As both public and private organizations continue to be targeted with ransomware and other fast and stealthy threats, the need for Autonomous Response is greater than ever.

Thanks to Darktrace analyst Tom Priest for his insights on the above threat find.

Learn more about Darktrace Antigena

Darktrace model detections

  • Device / New Failed External Connections
  • Device / New User Agent and New IP
  • Antigena / Network::External Threat::Antigena Suspicious File Block
  • Anomalous File / EXE from Rare External Location
  • Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach
  • Antigena / Network::External Threat::Antigena File then New Outbound Block
  • Anomalous Connection / Application Protocol on Uncommon Port
  • Device / Long Agent Connection to New Endpoint
  • Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block
  • Anomalous Connection / Lots of New Connections
  • Device / Large Number of Model Breaches
  • Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block
  • Device / Initial Breach Chain Compromise
  • Antigena / Network::External Threat::Antigena Suspicious Activity Block
  • Compliance / CertUtil External Connection

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
Max Heinemeyer
Global Field CISO

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September 5, 2025

Cyber Assessment Framework v4.0 Raises the Bar: 6 Questions every security team should ask about their security posture

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What is the Cyber Assessment Framework?

The Cyber Assessment Framework (CAF) acts as guide for organizations, specifically across essential services, critical national infrastructure and regulated sectors, across the UK for assessing, managing and improving their cybersecurity, cyber resilience and cyber risk profile.

The guidance in the Cyber Assessment Framework aligns with regulations such as The Network and Information Systems Regulations (NIS), The Network and Information Security Directive (NIS2) and the Cyber Security and Resilience Bill.

What’s new with the Cyber Assessment Framework 4.0?

On 6 August 2025, the UK’s National Cyber Security Centre (NCSC) released Cyber Assessment Framework 4.0 (CAF v4.0) a pivotal update that reflects the increasingly complex threat landscape and the regulatory need for organisations to respond in smarter, more adaptive ways.

The Cyber Assessment Framework v4.0 introduces significant shifts in expectations, including, but not limited to:

  • Understanding threats in terms of the capabilities, methods and techniques of threat actors and the importance of maintaining a proactive security posture (A2.b)
  • The use of secure software development principles and practices (A4.b)
  • Ensuring threat intelligence is understood and utilised - with a focus on anomaly-based detection (C1.f)
  • Performance of proactive threat hunting with automation where appropriate (C2.a)

This blog post will focus on these components of the framework. However, we encourage readers to get the full scope of the framework by visiting the NCSC website where they can access the full framework here.

In summary, the changes to the framework send a clear signal: the UK’s technical authority now expects organisations to move beyond static rule-based systems and embrace more dynamic, automated defences. For those responsible for securing critical national infrastructure and essential services, these updates are not simply technical preferences, but operational mandates.

At Darktrace, this evolution comes as no surprise. In fact, it reflects the approach we've championed since our inception.

Why Darktrace? Leading the way since 2013

Darktrace was built on the principle that detecting cyber threats in real time requires more than signatures, thresholds, or retrospective analysis. Instead, we pioneered a self-learning approach powered by artificial intelligence, that understands the unique “normal” for every environment and uses this baseline to spot subtle deviations indicative of emerging threats.

From the beginning, Darktrace has understood that rules and lists will never keep pace with adversaries. That’s why we’ve spent over a decade developing AI that doesn't just alert, it learns, reasons, explains, and acts.

With Cyber Assessment Framework v4.0, the bar has been raised to meet this new reality. For technical practitioners tasked with evaluating their organisation’s readiness, there are five essential questions that should guide the selection or validation of anomaly detection capabilities.

6 Questions you should ask about your security posture to align with CAF v4

1. Can your tools detect threats by identifying anomalies?

Cyber Assessment Framework v4.0 principle C1.f has been added in this version and requires that, “Threats to the operation of network and information systems, and corresponding user and system behaviour, are sufficiently understood. These are used to detect cyber security incidents.”

This marks a significant shift from traditional signature-based approaches, which rely on known Indicators of Compromise (IOCs) or predefined rules to an expectation that normal user and system behaviour is understood to an extent enabling abnormality detection.

Why this shift?

An overemphasis on threat intelligence alone leaves defenders exposed to novel threats or new variations of existing threats. By including reference to “understanding user and system behaviour” the framework is broadening the methods of threat detection beyond the use of threat intelligence and historical attack data.

While CAF v4.0 places emphasis on understanding normal user and system behaviour and using that understanding to detect abnormalities and as a result, adverse activity. There is a further expectation that threats are understood in terms of industry specific issues and that monitoring is continually updated  

Darktrace uses an anomaly-based approach to threat detection which involves establishing a dynamic baseline of “normal” for your environment, then flagging deviations from that baseline — even when there’s no known IoCs to match against. This allows security teams to surface previously unseen tactics, techniques, and procedures in real time, whether it’s:

  • An unexpected outbound connection pattern (e.g., DNS tunnelling);
  • A first-time API call between critical services;
  • Unusual calls between services; or  
  • Sensitive data moving outside normal channels or timeframes.

The requirement that organisations must be equipped to monitor their environment, create an understanding of normal and detect anomalous behaviour aligns closely with Darktrace’s capabilities.

2. Is threat hunting structured, repeatable, and improving over time?

CAF v4.0 introduces a new focus on structured threat hunting to detect adverse activity that may evade standard security controls or when such controls are not deployable.  

Principle C2.a outlines the need for documented, repeatable threat hunting processes and stresses the importance of recording and reviewing hunts to improve future effectiveness. This inclusion acknowledges that reactive threat hunting is not sufficient. Instead, the framework calls for:

  • Pre-determined and documented methods to ensure threat hunts can be deployed at the requisite frequency;
  • Threat hunts to be converted  into automated detection and alerting, where appropriate;  
  • Maintenance of threat hunt  records and post-hunt analysis to drive improvements in the process and overall security posture;
  • Regular review of the threat hunting process to align with updated risks;
  • Leveraging automation for improvement, where appropriate;
  • Focus on threat tactics, techniques and procedures, rather than one-off indicators of compromise.

Traditionally, playbook creation has been a manual process — static, slow to amend, and limited by human foresight. Even automated SOAR playbooks tend to be stock templates that can’t cover the full spectrum of threats or reflect the specific context of your organisation.

CAF v4.0 sets the expectation that organisations should maintain documented, structured approaches to incident response. But Darktrace / Incident Readiness & Recovery goes further. Its AI-generated playbooks are bespoke to your environment and updated dynamically in real time as incidents unfold. This continuous refresh of “New Events” means responders always have the latest view of what’s happening, along with an updated understanding of the AI's interpretation based on real-time contextual awareness, and recommended next steps tailored to the current stage of the attack.

The result is far beyond checkbox compliance: a living, adaptive response capability that reduces investigation time, speeds containment, and ensures actions are always proportionate to the evolving threat.

3. Do you have a proactive security posture?

Cyber Assessment Framework v4.0 does not want organisations to detect threats, it expects them to anticipate and reduce cyber risk before an incident ever occurs. That is s why principle A2.b calls for a security posture that moves from reactive detection to predictive, preventative action.

A proactive security posture focuses on reducing the ease of the most likely attack paths in advance and reducing the number of opportunities an adversary has to succeed in an attack.

To meet this requirement, organisations could benefit in looking for solutions that can:

  • Continuously map the assets and users most critical to operations;
  • Identify vulnerabilities and misconfigurations in real time;
  • Model likely adversary behaviours and attack paths using frameworks like MITRE ATT&CK; and  
  • Prioritise remediation actions that will have the highest impact on reducing overall risk.

When done well, this approach creates a real-time picture of your security posture, one that reflects the dynamic nature and ongoing evolution of both your internal environment and the evolving external threat landscape. This enables security teams to focus their time in other areas such as  validating resilience through exercises such as red teaming or forecasting.

4. Can your team/tools customize detection rules and enable autonomous responses?

CAF v4.0 places greater emphasis on reducing false positives and acting decisively when genuine threats are detected.  

The framework highlights the need for customisable detection rules and, where appropriate, autonomous response actions that can contain threats before they escalate:

The following new requirements are included:  

  • C1.c.: Alerts and detection rules should be adjustable to reduce false positives and optimise responses. Custom tooling and rules are used in conjunction with off the shelf tooling and rules;
  • C1.d: You investigate and triage alerts from all security tools and take action – allowing for improvement and prioritization of activities;
  • C1.e: Monitoring and detection personnel have sufficient understanding of operational context and deal with workload effectively as well as identifying areas for improvement (alert or triage fatigue is not present);
  • C2.a: Threat hunts should be turned into automated detections and alerting where appropriate and automation should be leveraged to improve threat hunting.

Tailored detection rules improve accuracy, while automation accelerates response, both of which help satisfy regulatory expectations. Cyber AI Analyst allows for AI investigation of alerts and can dramatically reduce the time a security team spends on alerts, reducing alert fatigue, allowing more time for strategic initiatives and identifying improvements.

5. Is your software secure and supported?  

CAF v4.0 introduced a new principle which requires software suppliers to leverage an established secure software development framework. Software suppliers must be able to demonstrate:  

  • A thorough understanding of the composition and provenance of software provided;  
  • That the software development lifecycle is informed by a detailed and up to date understanding of threat; and  
  • They can attest to the authenticity and integrity of the software, including updates and patches.  

Darktrace is committed to secure software development and all Darktrace products and internally developed systems are developed with secure engineering principles and security by design methodologies in place. Darktrace commits to the inclusion of security requirements at all stages of the software development lifecycle. Darktrace is ISO 27001, ISO 27018 and ISO 42001 Certified – demonstrating an ongoing commitment to information security, data privacy and artificial intelligence management and compliance, throughout the organisation.  

6. Is your incident response plan built on a true understanding of your environment and does it adapt to changes over time?

CAF v4.0 raises the bar for incident response by making it clear that a plan is only as strong as the context behind it. Your response plan must be shaped by a detailed, up-to-date understanding of your organisation’s specific network, systems, and operational priorities.

The framework’s updates emphasise that:

  • Plans must explicitly cover the network and information systems that underpin your essential functions because every environment has different dependencies, choke points, and critical assets.
  • They must be readily accessible even when IT systems are disrupted ensuring critical steps and contact paths aren’t lost during an incident.
  • They should be reviewed regularly to keep pace with evolving risks, infrastructure changes, and lessons learned from testing.

From government expectation to strategic advantage

Cyber Assessment Framework v4.0 signals a powerful shift in cybersecurity best practice. The newest version sets a higher standard for detection performance, risk management, threat hunting software development and proactive security posture.

For Darktrace, this is validation of the approach we have taken since the beginning: to go beyond rules and signatures to deliver proactive cyber resilience in real-time.

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Disclaimer:

This document has been prepared on behalf of Darktrace Holdings Limited. It is provided for information purposes only to provide prospective readers with general information about the Cyber Assessment Framework (CAF) in a cyber security context. It does not constitute legal, regulatory, financial or any other kind of professional advice and it has not been prepared with the reader and/or its specific organisation’s requirements in mind. Darktrace offers no warranties, guarantees, undertakings or other assurances (whether express or implied)  that: (i) this document or its content are  accurate or complete; (ii) the steps outlined herein will guarantee compliance with CAF; (iii) any purchase of Darktrace’s products or services will guarantee compliance with CAF; (iv) the steps outlined herein are appropriate for all customers. Neither the reader nor any third party is entitled to rely on the contents of this document when making/taking any decisions or actions to achieve compliance with CAF. To the fullest extent permitted by applicable law or regulation, Darktrace has no liability for any actions or decisions taken or not taken by the reader to implement any suggestions contained herein, or for any third party products, links or materials referenced. Nothing in this document negates the responsibility of the reader to seek independent legal or other advice should it wish to rely on any of the statements, suggestions, or content set out herein.  

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content without notice.

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September 5, 2025

Rethinking Signature-Based Detection for Power Utility Cybersecurity

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Lessons learned from OT cyber attacks

Over the past decade, some of the most disruptive attacks on power utilities have shown the limits of signature-based detection and reshaped how defenders think about OT security. Each incident reinforced that signatures are too narrow and reactive to serve as the foundation of defense.

2015: BlackEnergy 3 in Ukraine

According to CISA, on December 23, 2015, Ukrainian power companies experienced unscheduled power outages affecting a large number of customers — public reports indicate that the BlackEnergy malware was discovered on the companies’ computer networks.

2016: Industroyer/CrashOverride

CISA describes CrashOverride malwareas an “extensible platform” reported to have been used against critical infrastructure in Ukraine in 2016. It was capable of targeting industrial control systems using protocols such as IEC‑101, IEC‑104, and IEC‑61850, and fundamentally abused legitimate control system functionality to deliver destructive effects. CISA emphasizes that “traditional methods of detection may not be sufficient to detect infections prior to the malware execution” and recommends behavioral analysis techniques to identify precursor activity to CrashOverride.

2017: TRITON Malware

The U.S. Department of the Treasury reports that the Triton malware, also known as TRISIS or HatMan, was “designed specifically to target and manipulate industrial safety systems” in a petrochemical facility in the Middle East. The malware was engineered to control Safety Instrumented System (SIS) controllers responsible for emergency shutdown procedures. During the attack, several SIS controllers entered a failed‑safe state, which prevented the malware from fully executing.

The broader lessons

These events revealed three enduring truths:

  • Signatures have diminishing returns: BlackEnergy showed that while signatures can eventually identify adapted IT malware, they arrive too late to prevent OT disruption.
  • Behavioral monitoring is essential: CrashOverride demonstrated that adversaries abuse legitimate industrial protocols, making behavioral and anomaly detection more effective than traditional signature methods.
  • Critical safety systems are now targets: TRITON revealed that attackers are willing to compromise safety instrumented systems, elevating risks from operational disruption to potential physical harm.

The natural progression for utilities is clear. Static, file-based defenses are too fragile for the realities of OT.  

These incidents showed that behavioral analytics and anomaly detection are far more effective at identifying suspicious activity across industrial systems, regardless of whether the malicious code has ever been seen before.

Strategic risks of overreliance on signatures

  • False sense of security: Believing signatures will block advanced threats can delay investment in more effective detection methods.
  • Resource drain: Constantly updating, tuning, and maintaining signature libraries consumes valuable staff resources without proportional benefit.
  • Adversary advantage: Nation-state and advanced actors understand the reactive nature of signature defenses and design attacks to circumvent them from the start.

Recommended Alternatives (with real-world OT examples)

 Alternative strategies for detecting cyber attacks in OT
Figure 1: Alternative strategies for detecting cyber attacks in OT

Behavioral and anomaly detection

Rather than relying on signatures, focusing on behavior enables detection of threats that have never been seen before—even trusted-looking devices.

Real-world insight:

In one OT setting, a vendor inadvertently left a Raspberry Pi on a customer’s ICS network. After deployment, Darktrace’s system flagged elastic anomalies in its HTTPS and DNS communication despite the absence of any known indicators of compromise. The alerting included sustained SSL increases, agent‑beacon activity, and DNS connections to unusual endpoints, revealing a possible supply‑chain or insider risk invisible to static tools.  

Darktrace’s AI-driven threat detection aligns with the zero-trust principle of assuming the risk of a breach. By leveraging AI that learns an organization’s specific patterns of life, Darktrace provides a tailored security approach ideal for organizations with complex supply chains.

Threat intelligence sharing & building toward zero-trust philosophy

Frameworks such as MITRE ATT&CK for ICS provide a common language to map activity against known adversary tactics, helping teams prioritize detections and response strategies. Similarly, information-sharing communities like E-ISAC and regional ISACs give utilities visibility into the latest tactics, techniques, and procedures (TTPs) observed across the sector. This level of intel can help shift the focus away from chasing individual signatures and toward building resilience against how adversaries actually operate.

Real-world insight:

Darktrace’s AI embodies zero‑trust by assuming breach potential and continually evaluating all device behavior, even those deemed trusted. This approach allowed the detection of an anomalous SharePoint phishing attempt coming from a trusted supplier, intercepted by spotting subtle patterns rather than predefined rules. If a cloud account is compromised, unauthorized access to sensitive information could lead to extortion and lateral movement into mission-critical systems for more damaging attacks on critical-national infrastructure.

This reinforces the need to monitor behavioral deviations across the supply chain, not just known bad artifacts.

Defense-in-Depth with OT context & unified visibility

OT environments demand visibility that spans IT, OT, and IoT layers, supported by risk-based prioritization.

Real-world insight:

Darktrace / OT offers unified AI‑led investigations that break down silos between IT and OT. Smaller teams can see unusual outbound traffic or beaconing from unknown OT devices, swiftly investigate across domains, and get clear visibility into device behavior, even when they lack specialized OT security expertise.  

Moreover, by integrating contextual risk scoring, considering real-world exploitability, device criticality, firewall misconfiguration, and legacy hardware exposure, utilities can focus on the vulnerabilities that genuinely threaten uptime and safety, rather than being overwhelmed by CVE noise.  

Regulatory alignment and positive direction

Industry regulations are beginning to reflect this evolution in strategy. NERC CIP-015 requires internal network monitoring that detects anomalies, and the standard references anomalies 15 times. In contrast, signature-based detection is not mentioned once.

This regulatory direction shows that compliance bodies understand the limitations of static defenses and are encouraging utilities to invest in anomaly-based monitoring and analytics. Utilities that adopt these approaches will not only be strengthening their resilience but also positioning themselves for regulatory compliance and operational success.

Conclusion

Signature-based detection retains utility for common IT malware, but it cannot serve as the backbone of security for power utilities. History has shown that major OT attacks are rarely stopped by signatures, since each campaign targets specific systems with customized tools. The most dangerous adversaries, from insiders to nation-states, actively design their operations to avoid detection by signature-based tools.

A more effective strategy prioritizes behavioral analytics, anomaly detection, and community-driven intelligence sharing. These approaches not only catch known threats, but also uncover the subtle anomalies and novel attack techniques that characterize tomorrow’s incidents.

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About the author
Daniel Simonds
Director of Operational Technology
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