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February 13, 2025

Forensic Victory: Catching the Ransomware EDR Couldn't See

This blog details a simulation of a ransomware attack that bypassed EDR, simulated via a ClickFix social engineering technique. The attack used an obfuscated HTML and custom C++ binary to encrypt files and establish a reverse shell. Cado's forensic platform then demonstrated how to trace the attack chain, highlighting the need for robust DFIR beyond EDR.
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
Nate Bill
Threat Researcher
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13
Feb 2025

Introduction: Catching the ransomware EDR couldn't see

Endpoint Detection & Response (EDR) is frequently used by organizations as the first line of defense against cyberattacks. EDR platforms monitor organizations’ endpoints (servers, employee laptops, etc.) and detect and contain malicious activity running where possible. This blog will explore a ransomware attack in a lab environment, using payloads inspired from real attacks.

The incident

For this experiment, Cado Security Labs (now part of Darktrace) set up an up-to-date Windows machine, with a mainstream EDR tool installed, and simulated a ClickFix attack [1] against the user, which relies on socially engineering the user into running malicious commands.

During the first stage of the attack, the fake end user receives a phishing email with a ClickFix attachment:

Test Email Screenshot
Figure 1: Test Email

As this is a test, the email was kept fairly short. However, an attacker in a real-world setting would make the email far more convincing to view. In the real world, this type of attack is often seen being used with fake invoices being sent to finance staff.

After opening up the HTML, the end user is presented with the following page:

ClickFix HTML
Figure 2: The ClickFix HTML the user is presented with as part of our simulated attack

This is taken from a real attack where a Microsoft Word online page is mimicked, prompting the user to interact with it. The user needs to interact with the button, as most browsers will block clipboard writes unless the user has interacted with an element. Clicking the button copies a command to the user’s clipboard, and updates the instructions to tell them to press Win + R, Ctrl + V, and then Enter. If the user does this, it will open the run dialog, paste in the command, and execute it. This approach capitalizes on the typical user's lack of comprehension or uncritical adherence to directives, a tactic that has demonstrated efficacy in real-world cyberattacks.

It is worth noting that the EDR tool flagged this stage during initial testing. However, adding a layer of obfuscation to the HTML allowed for bypass detection. The page was able to be encoded, decoded and then written to the document using reflection to access methods that would normally be flagged.

Once the command is executed, PowerShell is invoked to download and run an .exe file from an attacker-controlled server.

The payload is a custom C++ binary that was developed for the purpose of this test. The binary spawns a reverse shell, as well as encrypting all of the files in the Documents folder for ransom. This binary was iteratively tested against the EDR tool, and the functionality was tweaked each time to bypass elements that were getting detected. Bypassing the EDR tool did not require any fancy techniques. Simply using a different Windows API to accomplish a goal that was previously flagged by the EDR tool, or altering the behavior, timing, and ordering of activities performed was sufficient to evade detection. This may seem surprising that sophisticated techniques aren’t strictly required to be undetected.

The aftermath of the attack can be seen in the images below, with a ransom note being written, and our important documents no longer being readable.

Ransom Note
Figure 3: The Ransom Note
Error Message
Figure 4: The aftermath of trying to open one of the PDFs

With no alerts to investigate from the EDR tool - how could a blue team uncover this attack chain after the fact for incident response?  

Investigating the artifacts with cado

Using Cado (acquired by Darktrace), we can import the affected VM directly with just a few clicks.

Cado UI
Figure 5: Import the affect VM  

The ransom note is a good starting point for the investigation. The timeline search feature quickly finds entries that show what process made the readme.txt file.

Event information
Figure 6: Timeline search feature

It shows that the ransom note was created by the process fix.exe, which can be used to pivot off and build a better understanding of what else the malware did, and how it got onto the system.

Reviewing events relating to the fix.exe payload shows that an event established a connection to a server, in this case, an attacker-controlled C2 server. It also spawned a command prompt instance, which provides a remote shell to the attacker.

Event information
Figure 7: Event Information
Event information
Figure 8: Event Information showing ransomware

Looking at the event information, it’s easy to spot the ransom attacks against the files. For example, the ransom attack modified the internal_draft_important.pdf document, which was seen before it can no longer be opened.

Event information
Figure 9:  Event information showing the modified document

And finally reaching the start of the log trail relating to the payload, it shows it initially being executed by PowerShell.

Event information
Figure 10: Event information showing PowerShell

However, this does not definitively show what caused the malware to run in the first place, and so the next step is running the pivot feature to find related events.

Pivoting off the event allows for quickly figuring out this was precipitated by a visit to obfuscated.html, which was downloaded from an email in Outlook online:

Related Events
Figure 11: Related events showing that the attack was precipiated by a visit to a obfuscated.html

The Cado Platform [2] also allows for directly jumping to the file in the file browser to conduct further analysis:

Cado UI screenshot
Figure 12: File seen in file browser

An EDR platform usually only provides an alert, process snapshot, and event details for a singular moment in time, missing the vital context needed to successfully understand the attack. Cado provides the vital context needed to successfully understand the full scope of the attack, not just its entry point.

Key takeaways

This research covered how Cado can provide the ability to forensically analyze systems and fully understand how attacks have occurred and unfolded. Defense-in-depth is a core component of cybersecurity, and being entirely reliant on an EDR platform as your only line of defense and insight into attacks can leave you without full  context.

This was an example only, and a finely tuned EDR platform would likely detect an attack similar to this. However, many organizations may overlook the forensics side of Digital Forensics and Incident Response [3], and remediate incidents solely using their EDR platform. This can result in organizations missing out on the complete picture of an attack, potentially leaving them open to re-infection. A DFIR platform is vital to respond quickly to incidents across Cloud, SaaS, and on-prem.

References

[1] https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic  

[2] https://www.darktrace.com/forensic-acquisition-investigation

[3] https://www.darktrace.com/cyber-ai-glossary/digital-forensics-incident-response

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
Nate Bill
Threat Researcher

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June 24, 2026

A New Security Challenge: The Curious Case of Prompt Language Analysis

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Why prompt analysis is emerging as a key AI security challenge

If securing AI has been one of the defining cybersecurity conversations of the past year, prompt analysis is quickly becoming one of its most interesting frontiers.

Security leaders are under pressure to understand how AI is being used across the business. In some organizations, that means governing employee use of chatbots. In others, it means overseeing copilots embedded into SaaS platforms, monitoring coding assistants, or assessing the growing footprint of autonomous agents. However different these use cases may appear on the surface, they share a common factor: humans and machines are usually interacting with enterprise systems through language.  

How prompt language differs from traditional security telemetry

For years, defenders have become used to working with familiar forms of telemetry: email traffic, network connections, API calls, endpoint processes, authentication events. Prompt language is different. It is not simply another log source. It is an expression of intent, instruction, curiosity, urgency, and sometimes manipulation. It reflects the end-goal of a user or agent, but not always with enough surrounding context to interpret the risk correctly.

Why existing security approaches only partially explain prompt risk

A growing number of vendors are approaching the task of securing AI from the angle they know best. Perimeter vendors are extending web or browser controls into AI usage. Identity vendors are emphasizing agent permissions and access governance. Data security and DLP providers are focusing on content inspection and exfiltration risk. All of these perspectives matter, but individually can’t fully explain the problem.

The challenge with securing AI is not just that a new application category has emerged. It is that language has become a new operating layer in the enterprise.

Employees now use prompts to summarize documents, generate code, analyze spreadsheets, query internal knowledge, and trigger multi-step actions through agents. In each case, prompt language acts as the interface between human intent and machine execution. That makes prompts incredibly valuable from a security perspective as they can hint at misuse, policy violations, data exposure, or attempts to circumvent controls. However, they can also be deeply ambiguous when viewed in isolation. That ambiguity is the heart of the issue.

Prompts as behavioral signals, not just text to classify

A prompt by itself tells you what was asked. It does not necessarily tell you whether the request is expected, risky, accidental, or entirely legitimate in context. Two nearly identical prompts can carry very different meanings depending on the role and function of who issued them, what systems they can access, and what actions followed. In other words, prompts are not just text to classify. They are behavioral signals to interpret.

Example: How context changes prompt risk entirely

Consider a common enterprise scenario. An employee is pulled into a new project with an aggressive deadline. Almost overnight, their use of AI tools spikes. They begin prompting more frequently, working across unfamiliar documents, querying new data sources, and interacting with more systems than usual to accelerate delivery. Viewed narrowly, this may look suspicious. Prompt volume increases, file access patterns change, API and SaaS activity rise. From some vantage points, it may resemble insider risk or unmanaged AI usage.

But now add context. Imagine that, earlier that day, the employee received instructions from a senior leader asking them to support a time-sensitive initiative. Their communication history shows that this leader is a legitimate reporting-line superior. Their recent collaboration patterns align with the new project team. Their subsequent activity, while unusual for that individual’s baseline, is consistent with the business task they were assigned.

What initially looked like a risk event may actually be a normal response to business pressure. Without the surrounding context of communication, organizational relationships, and broader behavioral patterns, prompt activity alone could generate more noise than insight.

The reverse is also true. A prompt may appear benign on the surface while the context around it suggests elevated risk. A request that seems routine could originate from a compromised user, a newly connected external agent, a shadow AI workflow, or a user acting outside their normal role. The language itself may not contain anything obviously malicious, but the surrounding conditions may tell a very different story.

What security teams need to analyze prompts effectively

The future of prompt analysis is not just about understanding language. It is about understanding language in context.

To do that well, security teams need more than prompt inspection. They need to understand:

  • Who is issuing the prompt, whether human or agent
  • How that identity normally behaves across the enterprise
  • What systems, data, and workflows are connected to the interaction
  • Which relationships and communications explain the surrounding activity
  • Whether the downstream actions align with expected business behavior

When those layers are absent, prompt analysis can become another isolated control surface: useful in theory, but limited in practice. Security teams may detect unusual wording but miss the operational function behind it, overreact to benign changes in behavior, or miss subtle misuse because the prompt itself did not appear dangerous.

How organizations should think about prompt analysis going forward

Security teams have seen this pattern before. In the cloud, posture without runtime context left important gaps. In identity, access control without behavioral understanding missed misuse that looked legitimate on paper. In data security, content inspection without business context often created friction without resolving risk. AI is exposing the same lesson again: controls are strongest when they are coordinated, not isolated. As organizations work to secure AI and identify gaps across their security operations, prompt analysis will become an increasingly important source of insight, but only as part of a broader strategy.

Prompt analysis will undoubtedly become more common, as prompts are one of the clearest windows into how people and agents are using AI systems. However, what matters most is not simply collecting prompts or filtering dangerous phrases, but being able to place that language inside a wider behavioral and operational picture.

Organizations that already have a broader understanding of how work gets done across the enterprise will be better positioned to make sense of prompt language as this category matures. They will be better able to distinguish urgency from abuse, experimentation from exfiltration, and productive AI adoption from hidden risk.

Figure 1: Darktrace / SECURE AI reconstructs the full sequence of events, showing every user and agent interaction in context, with risky prompts highlighted and categorized, including PII, sensitive data, and other policy violations.

At Darktrace, this is the key lesson emerging from the market: prompt language does matter, but it does not stand alone. It is most valuable when treated as a new behavioral input that can enrich understanding across the enterprise, not as a self-contained source of truth.

Why prompts become less useful when analyzed in isolation

The curious case of prompt language analysis, then, is this: the more important prompts become, the less useful they are in a vacuum.

The real opportunity is not just to see what was asked. It is to understand why it was asked, what it meant in that moment, and what happened next.

For a deeper look at how organizations are approaching this challenge from the strengths of prompt analysis to its limitations in isolation see Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches, which expands on the role prompt-level controls play within a broader, context-driven security strategy.

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About the author
Nabil Zoldjalali
VP, Field CISO

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June 23, 2026

Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations

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Darktrace joins the OpenAI Daybreak Cyber Partner Program

Today, we announced that Darktrace is joining the OpenAI Daybreak Cyber Partner Program. We’ll be partnering with OpenAI to explore how their cyber capabilities can be integrated within Darktrace products and services to bring new capabilities to our customers.

This partnership is an exciting opportunity to bring together Darktrace’s behavioral AI modelling of the organization with OpenAI’s advanced contextual capabilities to create a new level of understanding for security teams. To understand the impact, it’s helpful to start with how we think about the problem.  

At Darktrace, we built our AI in support of the core belief that cybersecurity needs to understand the business it is defending. That's why our Self-Learning AI is designed to help organizations understand normal and abnormal behavior for each organization across their digital environment, including users and identities, networks and cloud, email and collaboration tools, and now AI systems and agents with the rollout of Darktrace / SECURE AI™.  

Our goal was never simply to spot known attacks faster. It was to help defenders understand how their organization behaves, potential risks and impact, and where disruption could take hold so they could prepare for the unknown threats that they may not have seen or even imagined before.  

That’s exactly what is happening across the threat landscape today. Attacks keep changing; techniques shift, infrastructure evolves, and attackers move with more speed, precision, and context. And now they have even more AI and automation on their side. Attackers are exploiting identities, trusted services, SaaS applications, and business workflows. They are not always breaking in; often, the threat may come from within the organization in the form of insider threat or even rogue agents.  

In this reality, defenders need a combination of deep AI modelling of the organization and AI that can connect identified threats to concrete business context, translating this information into real world value, and allow action before risk becomes disruption.

That is the opportunity we see in partnering with OpenAI.  

What is the OpenAI Daybreak Cyber Partner Program and why is Darktrace joining

The OpenAI Daybreak Cyber Partner Program is focused on advancing the safe use of AI for cybersecurity. As part of the program’s next phase, OpenAI is working with a select group of trusted partners including Darktrace on scoped product integrations, managed services, and partner-delivered defensive capabilities. We’ll be exploring how OpenAI’s advanced frontier AI capabilities can support defenders in the tools and workflows they already use each day.

For Darktrace, this is a natural extension of our expertise and the work we have been doing for a decade: safely and securely applying the most effective AI techniques in combination to understand organizations, detecting malicious activity at the earliest indicators, and helping cyber defenders act faster.  

By using the advanced models and more precise safeguards available in the OpenAI Daybreak Cyber Partner Program, Darktrace and OpenAI will combine Darktrace’s real-time behavioral understanding of an organization's digital estate with OpenAI's ability to interpret wider business context.  

This is a unique and powerful combination of insights that could give organizations deeper context on technical risk and help them prioritize workloads and investigations based on potential impact to revenue, operations, and resilience. It can also provide security teams and executives with intelligence into which events matter most to the business, why they matter, and what action to take. Not just finding, for instance, that an agent is compromised, but highlighting that the compromised agent could shut down order fulfilment within the next three hours.  

Why the Darktrace and OpenAI partnership matters for defenders

Security teams today have more attack surface, more complex environments to protect, and an increasing volume of threats. The ability to act quickly is critical, but they also need to be able to focus on the risks that could have the greatest business impact.

That is especially important as attackers use AI to scale phishing, automate reconnaissance, find weaknesses, and blend into normal business activity. At the same time, organizations and their employees are using AI to innovate, which introduces an even broader attack surface and new set of risks. Defenders need AI that can operate across the same complexity, but safely, transparently, and in service of building more resilience. And they need a way to safely adopt, govern, and defend AI across their organizations.

Joining the OpenAI Daybreak Cyber Partner Program is another step in that direction. We are still early in this work, and we will take a careful, disciplined approach. But the direction is clear: protecting organizations requires AI that understands the business, not just the attack.

At Darktrace, that is exactly where we remain focused and why we are so excited about this partnership with OpenAI.  

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