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September 24, 2020

Detecting OT Threats: ICS Attack at International Airport

Learn how Darktrace's OT Threat Detection technology identified a sophisticated ICS attack on an international airport. Read more on Darktrace's blog.
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
David Masson
VP, Field CISO
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24
Sep 2020

As Industrial Control Systems (ICS) and traditional IT networks converge, the number of cyber-attacks that start in the corporate network before spreading to operational technology has increased dramatically in the last 12 months. From North Korean hackers targeting a nuclear power plant in India to ransomware shutting down operations at a US gas facility, and across Honda’s manufacturing sites, 2020 has been the year OT attacks have become mainstream.

Darktrace recently detected a simulation of a state-of-the-art attack at an international airport, identifying ICS reconnaissance, lateral movement, vulnerability scanning and protocol fuzzing – a technique in which the attacker sends nonsensical commands over an ICS communication channel in order to confuse the target device, causing it to fail or reboot.

Darktrace’s Industrial Immune System detected every stage of the sophisticated attack, using AI-powered anomaly detection to identify ICS attack vectors without a list of known exploits, company assets, or firmware versions. The attacker leveraged tools at every stage of the ICS kill chain, including ICS-specific attack techniques.

Any unusual attempts to read or reprogram single coils, objects, or other data blocks were detected by Cyber AI, and Darktrace’s Cyber AI Analyst also automatically identified the activity and created summary reports detailing the key actions taken.

The attack spanned multiple days and targeted the Building Management System (BMS) and the Baggage Reclaim network, with attackers utilizing two common ICS protocols (BacNet and S7Comm) and leveraging legitimate tools (such as ICS reprogramming commands and connections through SMB service pipes) to evade traditional, signature-based security tools.

Attack details

Figure 1: Timeline of the attack

In the first stage of the attack, a new device was introduced to the network, using ARP spoofing to evade detection from traditional security tools. At 11.40am, the attacker scanned a target device and attempted to bruteforce open services. Once the target device had been hijacked, the attacker then sought to establish an external connection to the Internet. External connections should not be possible in ICS networks, but attackers often seek to bypass firewalls and network segregation rules in order to create a command and control (C2) channel.

Figure 2: Darktrace Threat Tray 15 minutes after the pentest commenced. High level model breaches have already alerted the analyst team to the attack device.

The hijacked device then began performing ICS reconnaissance using Discover and Read commands. Darktrace identified new objects and data blocks being targeted as part of this reconnaissance, and detected ICS devices targeted with unusual BacNet and Siemens S7Comm protocol commands.

Figure 3: Model alerts associated with ICS reconnaissance over BacNet. Machine learning at the ICS command level detected new and unusual BacNet objects being targeted by the attacker.

The attacker enumerated through multiple ICS devices in order to perform lateral movement throughout the ICS system. Once they had learned device settings and configurations, they used ICS Reprogram and Write commands to reconfigure machines. The attacker attempted to use known vulnerabilities to exploit the target devices, such as the use of SMB, SMBv1, HTTP, RDP, and ICS protocol fuzzing.

Figure 4: Visualization of the device enumeration performed by the attacker against multiple ICS controllers. The attacker used ICS Discover commands as part of the initial reconnaissance.

The attacker took deliberate actions to evade the airport’s cyber security stack, including making connections using ICS protocols commonly used on the network to devices which commonly use those protocols. While legacy security tools failed to pick up on this activity, Darktrace’s deep packet inspection was able to identify unusual commands used by the attacker within those ‘normal’ connections.

The attacker used ARP spoofing to slow any investigation using asset management-based security tools – including two other solutions being trialed by the airport at the time of the attack. They also used multiple devices throughout the intrusion to throw defense teams off the scent.

Darktrace’s AI technology also launched an automated investigation into the incident. The Cyber AI Analyst identified all of the attack devices and produced summary reports for each, showcasing its ability to not only save crucial time for security teams, but bridge the skills gap between IT teams and ICS engineers.

Figure 5: The Cyber AI Analyst threat tray at the end of day 1. Both devices used by the attacker have been identified.

The Cyber AI Analyst immediately began investigating after the first model breach, and continued to stitch together disparate events across the network to produce a natural language summary of the incident, including recommendations for action.

Figure 6: AIA incident summary at the end of day 2, detailing the use of SMB exploits as part of the attack chain against one of the ICS devices.

Potential ramifications

Had the attack been allowed to continue, the attackers – potentially activist groups, terrorist organizations, and organized criminals – could have caused significant operational disruption to the airport. For example, the BMS is likely to manage temperature settings, the sprinkler system, fire alarms and fire exits, lighting, and doors in and out of secure access areas. Meddling with any one of these could cause severe disruption at an airport, with significant financial and reputational effects. Similarly, access to baggage reclaim networks could be used by criminals seeking to smuggle illegal goods or steal valuable cargo.

This simulation showcases the possibilities for an advanced cyber-criminal looking to compromise integrated IT and OT networks. The majority of leading ICS ‘security’ vendors are signature-based, and fail to pick up on novel techniques and utilization of common protocols to pursue malicious ends – this is why ICS attacks have continued to hit the headlines this year.

The incident showcases the extent of Cyber AI’s detections in a real-world ICS environment, and the level of detail Darktrace can provide following an attack. As Industrial Control Systems become increasingly integrated with the wider IT network, the importance of securing these critical systems is paramount. Darktrace provides a unified security umbrella with visibility and detection across the entire digital environment.

Thanks to Darktrace analyst Oakley Cox for his insights on the above investigation.

Learn more about the Industrial Immune System

Darktrace model detections:

  • ICS / Unusual ICS Commands
  • ICS / Multiple New Reprograms
  • ICS / Multiple New Discover Commands
  • ICS / Rare External from OT Device
  • ICS / Uncommon ICS Protocol Warning
  • ICS / Multiple Failed Connections to ICS Device
  • ICS / Anomalous IT to ICS 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
David Masson
VP, Field CISO

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July 24, 2026

Darktrace / EMAIL Expands Behavioral Defense Across Email and Collaboration Workflows

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Email and collaboration tools do more than carry messages. They are where organizations approve payments, share sensitive data, reset credentials, and make thousands of everyday decisions. Increasingly, they are interfaces through which humans direct AI agents in their daily activity. Email, Slack and Teams are high volume, rich with sensitive data, and an easy place to hide malicious activity.

The opportunity isn’t lost on bad actors. Darktrace / EMAIL detected more than 32 million high-confidence phishing emails globally in 2025, and 70% of those messages passed DMARC authentication.  Phishing is increasingly difficult to detect and familiar trust signals alone are not enough. People and security teams need to understand how a message fits the normal behavior of the sender, recipient, and organization. They also need to correlate activity across platforms to spot threats that span multiple channels.

To effectively secure against today’s evolved threats, security teams need to act at two levels: they need to help each employee make a safer decision ‘in the moment’, and they need to understand the wider patterns that may expose the business to risk.

Darktrace is introducing four new capabilities in Darktrace / EMAIL to address both challenges. The new features explain suspicious content more clearly to end users, strengthen the capabilities of Darktrace / Adaptive Human Defense with richer guidance, let organizations define their own patterns for detecting sensitive data in messages, and give security teams a process-level view of risk across email and collaboration workflows.

Darktrace / EMAIL Inbox Analysis highlights risky content within your emails

A warning is more useful when it explains what the user should look at. To help do that, we’ve expanded Darktrace / EMAIL’s Inbox Analysis Add-In to highlight potentially dangerous content within the body of emails that Darktrace / EMAIL flags as potentially suspicious or high risk.  

The add-in can highlight language designed to create urgency, financial references, requests for payment, suspicious links, and content that is unusual for the sender. Each highlighted element includes a pop up that explains why it may be suspicious. Instead of asking an employee to accept a verdict without context, the analysis helps them examine the message and make a more informed decision.

Enhanced Just-In-Time Training Banners in Darktrace / Adaptive Human Defense

Enhanced Just-In-Time Training Banners build on the same principle. The banners now include a contextual header, actionable advice, and specific detection context. This gives employees more useful guidance at the point of risk without adding unnecessary information or cognitive load.

Together, the capabilities help turn a warning into a short learning moment. Employees can see what looks unusual, understand what action to take, and build their judgment.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention

Sensitive data is different for every business. Standard categories such as payment card details or government identifiers matter, but organizations also have their own customer codes, project names, research formats, account structures, and internal identifiers.

Custom Sensitive Data Detection in Darktrace / EMAIL - Data Loss Prevention allows administrators to write custom expressions for the data their organization needs to protect. Matched content can trigger existing model actions and data loss prevention (DLP) workflows, extending Darktrace's DLP capabilities.

This extends data loss detection beyond a fixed library of common data types. Security teams can apply controls to information that is sensitive in the context of their own organization and adapt those controls as the business changes.

Introducing Email and Collaboration Workflow Risk Posture Dashboards

Some of the most important risks are not isolated events. They are repeated ways of working that create an opening for error, misuse, or attack. For example, a payment request may be one suspicious message, but a recurring approval workflow that relies on weak verification is a business process risk.

The new Email and Collaboration Workflow Risk Posture Dashboard analyzes email and collaboration data across Email, Microsoft Teams, Slack and Zoom to provide a process-level view of risk in the organization. These may include financial authorization workflows, sensitive data sharing patterns, and activity that could expose credentials.

The dashboard brings these patterns into a view and provides actionable recommendations. This helps security teams determine where to investigate or strengthen controls, where ownership needs to be clarified, and where the business may need to change a risky process. It gives CISOs a clearer view of how human and communication risk is embedded in everyday operations, not only where individual alerts occur.

Behavior connects the individual decision to the wider risk

These capabilities build on Darktrace’s unique behavioral approach to security. We use Adaptive AI to learn how people and AI normally behave within an organization, creating the context needed to recognize when activity changes.

Within the Darktrace Behavioral Defense Platform, Darktrace / EMAIL helps protect people against phishing, account takeover, data exfiltration, and human risk across email and collaboration tools. The new capabilities extend that protection in both directions. They give employees clearer context for the decision in front of them, while giving security leaders a broader view of the workflows and behavior that create risk across the organization.

The result is not simply more alerts. It is a better understanding of why something is risky, what action to take, and where the organization can reduce risk before a familiar process becomes an easy route for an attacker.

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Carlos Gray
Senior Product Marketing Manager, Email

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July 23, 2026

When AI Agents Go Off Script: What the OpenAI and Hugging Face Incident Means for Defenders

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The recent security incident disclosed jointly by OpenAI and Hugging Face is quickly becoming one of the most discussed AI security stories of the year; it's one that has generated great discussion amongst our researchers and technical teams here at Darktrace.

During an internal evaluation of advanced OpenAI models, an autonomous agent reportedly escaped its intended testing boundaries, obtained internet access, and compromised Hugging Face infrastructure while attempting to evaluate and benchmark the model's cyber capabilities. OpenAI has described it as an unprecedented cyber incident and is collaborating with Hugging Face to investigate and share its findings publicly to help defenders understand the implications and improve AI safety across the community.

What this means for organizations deploying AI agents

The most important takeaway for security leaders is not that the AI model is capable of complex and effective chained attacks. It's what this tells us about the guardrails and protections that need to be put in place to ensure these increasingly capable models are safely used within our businesses.

According to OpenAI's investigation, the models were not acting with malicious intent. They were attempting to achieve a legitimate assigned objective. In pursuing that objective, they identified a path that involved obtaining information from a third party's environment and followed it to completion. From the model's perspective, that appears to have been an effective solution to the problem it was given.

Many organizations still hold the assumption that giving an AI agent a legitimate goal will naturally result in legitimate behavior. Increasingly, that is not something defenders can safely assume.

A human who encounters a "permission denied" message generally understands the technical restriction as well as the social meaning behind it. An agent may interpret that message only as evidence that one method to achieve its objective has failed. What makes this incident notable is the agent’s ability to keep pursuing the objective, revise its plan, and combine many individually useful actions into an outcome its designers did not intend.

This is not an isolated challenge. Recent research from the UK's AI Security Institute found that frontier models often attempt to "cheat" evaluations by finding unintended shortcuts to success. More interestingly, that behavior is not always reflected in chain-of-thought outputs and is not always admitted when models are questioned about their actions. One lesson for defenders is that understanding what an AI system says it is doing is not always the same as understanding its behavior and what it is actually doing.

Why behavioral security is an essential security foundation

That's the principle behind Darktrace / SECURE AI and our broader approach to behavioral security. Understanding intent or putting in place static guardrails is not enough; security teams need visibility into how agents behave over time, what resources they interact with, how their behavior changes, and whether they begin pursuing objectives in unexpected ways. Research released by NIST earlier this summer reinforced that rules alone are not sufficient to defend against AI threats, with one conclusion being that "there will always be a way to prompt an AI system to disregard its rules - it's just a matter of finding it."

Another interesting lesson is that nothing described in OpenAI's preliminary findings appears fundamentally impossible for a skilled human attacker. But it does highlight the accessibility and scale that could be unlocked for attackers as increasingly capable models become more widely available. Resource constraints that attackers deal with will decrease as frontier models can help execute elements of those attack workflows autonomously, persist through failure, and iterate towards an objective. Capabilities that were once limited to skilled attackers can increasingly be supplemented with these systems, lowering the barrier to entry to conduct complex cyber operations.

It is incredibly positive to see OpenAI and Hugging Face investigating this incident collaboratively and sharing their findings publicly. Transparency and collaboration around incidents like these helps the entire security community better understand how rapidly AI capabilities are evolving and where we need to improve safeguards. It also reinforces why Darktrace is excited to be partnering with OpenAI through the Daybreak Cyber Partner Program: to explore how advanced AI can be used safely and responsibly in support of defenders while developing the visibility, governance, and security controls needed for increasingly autonomous systems.

This incident is an important reminder that capable AI systems will not always behave in the ways we expect. As organizations deploy AI agents across critical workflows, understanding behavior is an even more important foundation for effective security.

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