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July 9, 2019

Insights on Shamoon 3 Data-Wiping Malware

Gain insights into Shamoon 3 and learn how to protect your organization from its destructive capabilities.
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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09
Jul 2019

Responsible for some of the “most damaging cyber-attacks in history” since 2012, the Shamoon malware wipes compromised hard drives and overwrites key system processes, intending to render infected machines unusable. During a trial period in the network of a global company, Darktrace observed a Shamoon-powered cyber-attack on December 10, 2018 — when several Middle Eastern firms were impacted by a new variant of the malware.

While there has been detailed reporting on the malware files and wiper modules that these latest Shamoon attacks employed, the complete cyber kill chain involved remains poorly understood, while the intrusions that led to the malware’s eventual “detonation” last December has not received nearly as much coverage. As a consequence, this blog post will focus on the insights that Darktrace’s cyber AI generated regarding (a) the activity of the infected devices during the “detonation” and (b) the indicators of compromise that most likely represent lateral movement activity during the weeks prior.

A high-level overview of major events leading up to the detonation on December 10th.

In the following, we will dive into that timeline more deeply in reverse chronological order, going back in time to trace the origins of the attack. Let’s begin with zero hour.

December 10: 42 devices “detonate”

A bird's-eye perspective of how Darktrace identified the alerts in December 2018.

What immediately strikes the analyst’s eye is the fact that a large accumulation of alerts, indicated by the red rectangle above, took place on December 10, followed by complete network silence over the subsequent four days.

These highlighted alerts represent Darktrace’s detection of unusual network scans on remote port 445 that were conducted by 42 infected devices. These devices proceeded to scan more machines — none of which were among those already infected. Such behavior indicates that the compromised devices started scanning and were wiped independently from each other, instead of conducting worming-style activity during the detonation of the malware. The initial scanning device started its scan at 12:56 p.m. UTC, while the last scanning device started its scan at 2:07 p.m. UTC.

Not only was this activity readily apparent from the bird’s-eye perspective shown above, the detonating devices also created the highest-priority Darktrace alerts over a several day period: “Device / Network Scan” and “Device / Expanded Network Scan”:

Moreover, when investigating “Devices — Overall Score,” the detonating devices rank as the most critical assets for the time period December 8–11:

Darktrace AI generated all of the above alerts because they represented significant anomalies from the normal ‘pattern of life’ that the AI had learned for each user and device on the company’s network. Crucially, none of the alerts were the product of predefined ‘rules and signatures’ — the mechanism that conventional security tools rely on to detect cyber-threats. Rather, the AI revealed the activity because the scans were unusual for the devices given their precise nature and timing, demonstrating the necessity of the such a nuanced approach in catching elusive threats like Shamoon. Of further importance is that the company’s network consists of around 15,000 devices, meaning that a rules-based approach without the ability to prioritize the most serious threats would have drowned out the Shamoon alerts in noise.

Now that we’ve seen how cyber AI sounded the alarms during the detonation itself, let’s investigate the various indicators of suspicious lateral movement that precipitated the events of December 10. Most of this activity happened in brief bursts, each of which could have been spotted and remediated if Darktrace had been closely monitored.

November 19: Unusual Remote Powershell Usage (WinRM)

One such burst of unusual activity occurred on November 19, when Darktrace detected 14 devices — desktops and servers alike — that all successfully used the WinRM protocol. None of these devices had previously used WinRM, which is also unusual for the organization’s environment as a whole. Conversely, Remote PowerShell is quite often abused in intrusions during lateral movement. The devices involved did not classify as traditional administrative devices, making their use of WinRM even more suspicious.

Note the clustering of the WinRM activity as indicated by the timestamp on the left.

October 29–31: Scanning, Unusual PsExec & RDP Brute Forcing

Another burst of likely lateral movement occurred between October 29 and 31, when two servers were seen using PsExec in an unusual fashion. No PsExec activity had been observed in the network before or after these detections, prompting Darktrace to flag the behavior. One of the servers conducted an ICMP Ping sweep shortly before the lateral movement. Not only did both servers start using PsExec on the same day, they also used SMBv1 — which, again, was very unusual for the network.

Most legitimate administrative activity involving PsExec these days uses SMBv2. The graphic below shows several Darktrace alerts on one of the involved servers — take note of the chronology of detections at the bottom of the graphic. This clearly reads like an attacker’s diary: ICMP scan, SMBv1 usage, and unusual PsExec usage, followed by new remote service controls. This server was among the top five highest ranking devices during the analyzed time period and was easy to identify.

Following the PsExec use, the servers also started an anomalous amount of remote services via the srvsvc and svcctl pipes over SMB. They did so by starting services on remote devices with which they usually did not communicate — using SMBv1, of course. Some of the attempted communication failed due to access violation and access permission errors. Both are often seen during malicious lateral movement.

Additional context around the SMBv1 and remote srvsvc pipe activity. Note the access failure.

Thanks to Darktrace’s deep packet inspection, we can see exactly what happened on the application layer. Darktrace highlights any unusual or new activity in italics below the connections — we can easily see that the SMB activity is not only unusual because of SMBv1 being used, but also because this server had never used this type of SMB activity remotely to those particular destinations before. We can also observe remote access to the winreg pipe — likely indicating more lateral movement and persistence mechanisms being established.

The other server conducted some targeted address scanning on the network on October 29, employing typical lateral movement ports 135, 139 and 445:

Another device was observed to conduct RDP brute forcing on October 29 around the same time as the above address scan. The desktop made an unusual amount of RDP connections to another internal server.

A clear plateau in increased internal connections (blue) can be seen. Every colored dot on top represents an RDP brute force detection. This was again a clear-cut detection not drowned in other noise — these were the only RDP brute force detections for a several-month monitoring time window.

October 9–11: Unusual Credential Usage

Darktrace identifies the unusual use of credentials — for instance, if administrative credentials are used on client device on which they are not commonly used. This might indicate lateral movement where service accounts or local admin accounts have been compromised.

Darktrace identified another cluster of activity that is likely representing lateral movement, this time involving unusual credential usage. Between October 9 and 11, Darktrace identified 17 cases of new administrative credentials being used on client devices. While new administrative credentials were being used from time to time on devices as part of normal administrative activity, this strong clustering of unusual admin credential usage was outstanding. Additionally, Darktrace also identified the source of some of the credentials being used as unusual.

Conclusion

Having observed a live Shamoon infection within Darktrace, there are a few key takeaways. While the actual detonation on December 10 was automated, the intrusion that built up to it was most likely manual. The fact that all detonating devices started their malicious activity roughly at the same time — without scanning each other — indicates that the payload went off based on a trigger like a scheduled task. This is in line with other reporting on Shamoon 3.

In the weeks leading up to December 10, there were various significant signs of lateral movement that occurred in disparate bursts — indicating a ‘low-and-slow’ manual intrusion.

The adversaries used classic lateral movement techniques like RDP brute forcing, PsExec, WinRM usage, and the abuse of stolen administrative credentials.

While the organization in question had a robust security posture, an attacker only needs to exploit one vulnerability to bring down an entire system. During the lifecycle of the attack, the Darktrace Enterprise Immune System identified the threatening activity in real time and provided numerous suggested actions that could have prevented the Shamoon attack at various stages. However, human action was not taken, while the organization had yet to activate Antigena, Darktrace’s autonomous response solution, which could have acted in the security team’s stead.

Despite having limited scope during the trial period, the Enterprise Immune System was able to detect the lateral movement and detonation of the payload, which was indicative of the malicious Shamoon virus activity. A junior analyst could have easily identified the activity, as high-severity alerts were consistently generated, and the likely infected devices were at the top of the suspicious devices list.

Darktrace Antigena would have prevented the movement responsible for the spread of the virus, while also sending high-severity alerts to the security team to investigate the activity. Even the scanning on port 445 from the detonating devices would have been shut down, as it presented a significant deviation from the known behavior of all scanning devices, which would have further limited the virus’s spread, and ultimately, spared the company and its devices from attack.

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 17, 2026

The Problem of Re-defining Human Value in the Agentic Age

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Newsfeeds are constantly informing us about the rapid escalation of agentic AI systems. These systems move far beyond simple machine-based computations, and  the focused, defined and bounded assistance that most AI systems started out as.

The next evolution of AI will harness the agentic properties of orchestration, automation, and heightened value-chains in IT, taking on the burden of workflow management, not just workflow delivery.

In nearly all of these instances, promises are made such as ‘this will free up human time’ or ‘this will allow people to focus on higher-order strategy’. However that message is delivered, one thing is clear: in ceding the orchestration and management of work to increasingly sophisticated AI agents and agentic systems, human value will be elevated to a particular and specific layer: the ability to judge its outputs.  As AI takes on more tasks, humans should be able to focus on a higher level of governance; making sure the decisions that AI offers us are ethical, responsible and worthwhile.  

But there are two major problems with that approach.

This blog discusses the problem of how Agentic systems are re-shaping how we review information, where we fit in, and when we make decisions.  It also discusses the problem of how the increased flow of confident, generated information affects the way we make judgement.  This blog considers how human judgement needs to adapt, and how a behavioral defense approach – using techniques pioneered by Darktrace – can help us do that.

The challenge of knowing where human judgement belongs

As we confer more automated decision-making to agentic systems, it might look increasingly less like ‘granting permissions’, and more like ‘surrendering authority’.  

The judgement layer for AI-generated work is not a fixed boundary. We have become used to the idea of a ‘human in the loop’ (HITL) and, historically, relationships between humans and IT systems were reasonably clear and bounded.  Computer and software systems were programmed to carry out certain tasks or automated functions, and humans could control the gates and decision points where actions were undertaken. Even across highly complex computational workflows, human interaction was a controllable node within the process; we were able to configure and regulate. But in the agentic age, where that human interaction sits, and what it can influence shifts every time AI systems are granted autonomy.  

This leads us to the first problem: if humans are moving themselves (or are being moved) into the ‘judgement’ part of the value chain, exactly where and when do we exercise that judgement?  

Humans are no longer the sole shepherds of computer-based or software-controlled outputs.  We are at times at least one step further (and slower) behind the new agentic shepherds.  We might also be blind to what they are doing.  Not only might we be removed and blind to the actions of our AI shepherds, but with the challenge of unknown, unapproved AI systems operating beyond our control, humans might not even know that our work is being shepherded by an AI at all.  Simply put, with the advent of greater levels of autonomy and orchestration, humans are at risk of not even knowing where to apply our newly-extended powers of strategic judgement.

Shadow AI – the use of unapproved AI systems or processes – is a growing threat to the role of effective governance and oversight. Shadow AI isn't just the AI you can't see. Its the AI you already know about being used in an unapproved way. The ability to generate effective oversight of the AI systems you use (or that are used on your behalf) will be increasingly important to ensure that human judgement in the AI value chain is effective, and deliberately placed.

The problem of what makes good judgement

The second problem lies in how flawed human judgement can be.  Humans are historically, notoriously, and, sometimes dangerously, unreliable when it comes to exercising judgement.  Humans are prone to the worst kinds of bias, the seduction of malign influence, and the sometimes-overwhelming urge to succeed. AI has long had a known flaw of operating with sycophancy, providing outputs that tend to agree with or flatter the human user.  But as AI grows ever more effective, there is a risk of both hyper-enablement (where humans increasingly and knowingly enable AI despite potential harm), as well as the greater risk of suggestion. Both of these aspects could skew the newly-elevated input of human judgement.

Imagine a highly competent AI system that has just orchestrated and managed a dizzying array of processes and workflows.  The AI is designed to present the human decision-maker with recommendations; based on analysis, comparison and other programmed factors.  This is where the human judgement layer is enabled.  But what if that judgement is summarily diffused by an AI-based recommendation that emulates the decision, provides plausible but unattractive alternatives, then suggests (or, worse, directs) the human end-user to take a particular course of action.

The risk here is that you are given a recommendation, tailored to your preferences (which the AI has learned, or which you have divulged), and which appears to make perfect sense.  It appears to be a well-weighted recommendation, with sound arguments that tap into our inherent biases or inclinations so that a specific decision-path is followed. With the growth of agentic systems specifically designed to match user profiles (from Cowork agents to ‘digital twin’ models), the likelihood of agentic influence could badly skew human judgement or, at the least, devalue the proposition that humans are taking a higher-layer of strategic control over AI-based decisions.

If AI convincingly recommends something that may be problematic, it can be difficult to discern both accurate data, and the context required to make the right judgement.

Given the two problems described above, the job of exercising valuable human judgement in the agentic age can draw down to these two questions:

  • When should humans intervene in the agentic process?  
  • How can we make the best possible judgement calls?

What humans contribute that AI cannot

For all the flaws that make human judgement unreliable, people have the edge over even the most sophisticated and powerful AI systems when it comes to issues such as ethics and social context.  An AI system can, with startling granularity, rank the value of adopting a new business proposal: offering predictive metrics on costs, returns, market value, time-to-deliver operations, conformance with legal registers, etc.  But it can’t tell if the business proposal is ethically sound, or if the business venture will potentially affect groups outside of the analyzed proposal. It can’t tell you if the CEO has a ‘bad feeling’ about this effort.  It can’t tell you if this is the right thing to do.  

The ability to add social context, balance complex interpersonal dynamics, understand nuance, and to go beyond what seems economically reasonable is where human judgement can add value.  

Human judgement is difficult to encapsulate in metrics. And the way we train our development may need to adapt too. Rather than building up a gradual, experiential knowledge base, we should think about training the skill of judgement itself; especially for an agentic age.

How behavioral security strengthens AI governance

If this all feels like a vicious circle (‘I need AI help to make good judgements’ / ‘AI can twist what I need to judge’) it needn’t be. The key to this is having a defense-in-depth approach, with tools that can actually help.

This is precisely where behavioral security becomes important. The complex and nuanced way that humans exercise judgement is often rooted in our ability to recognize behavior that doesn't look right. We may not always be able to articulate it immediately, but we can often identify when an action, recommendation, or outcome feels inconsistent with the context around it. As AI systems take on more responsibility across the decision chain, preserving that ability to recognize meaningful deviations becomes increasingly important.

Darktrace’s / SECURE AI is designed to do exactly that. It applies behavioral security to AI ecosystems, helping organizations understand how people, AI tools, identities, and agents interact across the business. By learning the patterns of normal AI usage and surfacing activity that deviates from those patterns, it provides security teams with the context needed to investigate risk, understand unusual behavior, and make informed governance decisions. Rather than relying solely on predefined rules or assumptions, this behavioral understanding helps organizations distinguish between expected AI activity and behavior that warrants closer scrutiny.

This matters because we are already in an era of information overload. If humans are expected to elevate their value through strategic judgement, the ability to do this without being overwhelmed by data (good or bad) will be critical.  

We need the ability to discern when we're being misled by AI, and whether our judgement calls are being made on the basis of accurate, contextual information. Darktrace / SECURE AI provides that additional layer of defensive security for activity we cannot easily see. Whether it is suspected Shadow AI or skewed recommendations, the net result is a protected organization, where users can more effectively use AI to make positive judgements.

For those where that judgement is a critical skill (both individuals, as well as those working in security teams), improving our metacognition - the ability to understand information in a broader context - will supercharge the value of human judgement. When those judgements are grounded in context rather than assumptions we have better information to make sound decisions.

Conclusion

Human judgement is a skill that is honed over time and experience.  Darktrace’s / SECURE AI employs the same principles, but at machine-speed. Rather than influencing or directing, Darktrace / SECURE AI offers AI-enabled assurance; providing human-based judgement with the right context to make a balanced decision.  

What we judge can be valued by the legitimacy of its outputs. For AI, those outputs are valued on the speed and accuracy of the information provided.  Increasingly for humans, the value of our outputs will be based on the validity of our judgement, and how we justify our decisions in ways that engineer confidence.  

Humans often know more than we can express, while AI is prone to expressing more than it truly understands. Humans can bridge the context AI often fails to appreciate. When that judgement is supported by relevant, impartial AI systems, this is the future space where good AI governance will be exercised.

Discover Darktrace / SECURE AI.

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About the author
Jason Lusted
AI Governance Advisor

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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