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May 11, 2023

Securing OT Systems: The Limits of the Air Gap Approach

Air-gapped security measures are not enough for resilience against cyber attacks. Read about how to gain visibility & reduce your cyber vulnerabilities.
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 Lesser
Head of U.S. Policy Analysis and Engagement
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11
May 2023

At a Glance:

  • Air gaps reduce cyber risk, but they do not prevent modern cyber attacks
  • Having visibility into an air-gapped network is better than assuming your defenses are impenetrable and having zero visibility
  • Darktrace can provide visibility and resiliency without jeopardizing the integrity of the air gap

What is an 'Air Gap'?

Information technology (IT) needs to fluidly connect with the outside world in order channel a flow of digital information across everything from endpoints and email systems to cloud and hybrid infrastructures. At the same time, this high level of connectivity makes IT systems particularly vulnerable to cyber-attacks.  

Operational technology (OT), which controls the operations of physical processes, are considerably more sensitive. OT often relies on a high degree of regularity to maintain continuity of operations. Even the slightest disturbance can lead to disastrous results. Just a few seconds of delay on a programmable logic controller (PLC), for example, can significantly disrupt a manufacturing assembly line, leading to downtime at a considerable cost. In worst-case scenarios, disruptions to OT can even threaten human safety. 

An air gap is a ‘digital moat’ where data cannot enter or leave OT environments unless it is transferred manually.

Organizations with OT have traditionally tried to reconcile this conflict between IT and OT by attempting to separate them completely. Essentially, the idea is to let IT do what IT does best — facilitate activities like communication and data transfer at rapid speeds, thus allowing people to connect with each other and access information and applications in an efficient capacity. But at the same time, erect an air gap between IT and OT so that any cyber threats that slip into IT systems do not then spread laterally into highly sensitive, mission-critical OT systems. This air gap is essentially a ‘digital moat’ where data cannot enter or leave OT environments unless it is transferred manually.

Limitations of the Air Gap

The air gap approach makes sense, but it is far from perfect. First, many organizations that believe they have completely air-gapped systems in fact have unknown points of IT/OT convergence, that is, connections between IT and OT networks of which they are unaware. 

Many organizations today are also intentionally embracing IT/OT convergence to reap the benefits of digital transformation of their OT, in what is often called Industry 4.0. Examples include the industrial cloud (or ICSaaS), the industrial internet of things (IIoT), and other types of cyber-physical systems that offer increased efficiency and expanded capabilities when compared to more traditional forms of OT. Organizations may also embrace IT/OT convergence due to a lack of human capital, as convergence can make processes simpler and more efficient.

Even when an organization does have a true air gap (which is nearly impossible to confirm without full visibility across IT and OT environments), the fact is that there are a variety of ways for attackers to ‘jump the air gap'. Full visibility across IT and OT ecosystems in a single pane of glass is thus essential for organizations seeking to secure their OT. This is not only to illuminate any points of IT/OT convergence and validate the fact that an air gap exists in the first place, but also to see when an attack slips through the air gap.

Figure 1: Darktrace/OT's unified view of IT and OT environments.

Air Gap Attack Vectors

Even a perfect air gap will be vulnerable to a variety of different attack vectors, including (but not limited to) the following: 

  • Physical compromise: An adversary bypasses physical security and gains access directly to the air-gapped network devices. Physical access is by far the most effective and obvious technique.
  • Insider threats: Someone who is part of an organization and has access to air-gapped secure systems intentionally or unintentionally compromises a system.
  • Supply chain compromise: A vendor with legitimate access to air-gapped systems unwittingly is compromised and brings infected devices into a network. 
  • Misconfiguration: Misconfiguration of access controls or permissions allows an attacker to access the air-gapped system through a separate device on the network.
  • Social engineering (media drop): If an attacker was able to successfully conduct a malicious USB/media drop and an employee was to use that media within the air-gapped system, the network could be compromised. 
  • Other advanced tactics: Thermal manipulation, covert surface vibrations, LEDs, ultrasonic transmissions, radio signals, and magnetic fields are among a range of advanced tactics documented and demonstrated by researchers at Ben Gurion University. 

Vulnerabilities of Air-Gapped Systems

Aside from susceptibility to advanced techniques, tactics, and procedures (TTPs) such as thermal manipulation and magnetic fields, more common vulnerabilities associated with air-gapped environments include factors such as unpatched systems going unnoticed, lack of visibility into network traffic, potentially malicious devices coming on the network undetected, and removable media being physically connected within the network. 

Once the attack is inside OT systems, the consequences can be disastrous regardless of whether there is an air gap or not. However, it is worth considering how the existence of the air gap can affect the time-to-triage and remediation in the case of an incident. For example, the existence of an air gap may seriously limit an incident response vendor’s ability to access the network for digital forensics and response. 

Kremlin Hackers Jumping the Air Gap 

In 2018, the U.S. Department of Homeland Security (DHS) issued an alert documenting the TTPs used by Russian threat actors known as Dragonfly and Energetic Bear. Further reporting alleged that these groups ‘jumped the air gap,’ and, concerningly, gained the ability to disable the grid at the time of their choosing. 

These attackers successfully gained access to sensitive air-gapped systems across the energy sector and other critical infrastructure sectors by targeting vendors and suppliers through spear-phishing emails and watering hole attacks. These vendors had legitimate access to air-gapped systems, and essentially brought the infection into these systems unintentionally when providing support services such as patch deployment.

This incident reveals that even if a sensitive OT system has complete digital isolation, this robust air gap still cannot fully eliminate one of the greatest vulnerabilities of any system—human error. Human error would still hold if an organization went to the extreme of building a faraday cage to eliminate electromagnetic radiation. Air-gapped systems are still vulnerable to social engineering, which exploits human vulnerabilities, as seen in the tactics that Dragonfly and Energetic Bear used to trick suppliers, who then walked the infection right through the front door. 

Ideally, a technology would be able to identify an attack regardless of whether it is caused by a compromised supplier, radio signal, or electromagnetic emission. By spotting subtle deviations from a device, human, or network’s normal ‘pattern of life’, Self-Learning AI detects even the most nuanced forms of threatening behavior as they emerge — regardless of the source or cause of the threat.

Darktrace/OT for Air-Gapped Environments

Darktrace/OT for air-gapped environments is a physical appliance that deploys directly to the air-gapped system. Using raw digital data from an OT network to understand the normal pattern of life, Darktrace/OT does not need any data or threat feeds from external sources because the AI builds an innate understanding of self without third-party support. 

Because all data-processing and analytics are performed locally on the Darktrace appliance, there is no requirement for Darktrace to have a connection out to the internet. As a result, Darktrace/OT provides visibility and threat detection to air-gapped or highly segmented networks without jeopardizing their integrity. If a human or machine displays even the most nuanced forms of threatening behavior, the solution can illuminate this in real time. 

Security professionals can then securely access Darktrace alerts from anywhere within the network, using a web browser and encrypted HTTPS, and in line with your organization’s network policies.

Figure 2: Darktrace/OT detecting anomalous connections to a SCADA ICS workstation.

With this deployment, Darktrace offers all the critical insights demonstrated in other Darktrace/OT deployments, including (but not limited to) the following:

Organizations seeking to validate whether they have an air gap in the first place and maintain the air gap as their IT and OT environments evolve will greatly benefit from the comprehensive visibility and continuous situational awareness offered by Darktrace’s Self-Learning AI. Also, organizations looking to poke holes in their air gap to embrace the benefits of IT/OT convergence will find that Self-Learning AI’s vigilance spots cyber-attacks that slip through. 

Whatever your organizations goals—be it embracing IIoT or creating a full-blown DMZ—by learning ‘you’, Darktrace’s Self-Learning AI can help you achieve them safely and securely. 

Learn more about Darktrace/OT

Credit to: Daniel Simonds and Oakley Cox for their contribution to this 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
Max Lesser
Head of U.S. Policy Analysis and Engagement

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April 24, 2025

The Importance of NDR in Resilient XDR

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As threat actors become more adept at targeting and disabling EDR agents, relying solely on endpoint detection leaves critical blind spots.

Network detection and response (NDR) offers the visibility and resilience needed to catch what EDR can’t especially in environments with unmanaged devices or advanced threats that evade local controls.

This blog explores how threat actors can disable or bypass EDR-based XDR solutions and demonstrates how Darktrace’s approach to NDR closes the resulting security gaps with Self-Learning AI that enables autonomous, real-time detection and response.

Threat actors see local security agents as targets

Recent research by security firms has highlighted ‘EDR killers’: tools that deliberately target EDR agents to disable or damage them. These include the known malicious tool EDRKillShifter, the open source EDRSilencer, EDRSandblast and variants of Terminator, and even the legitimate business application HRSword.

The attack surface of any endpoint agent is inevitably large, whether the software is challenged directly, by contesting its local visibility and access mechanisms, or by targeting the Operating System it relies upon. Additionally, threat actors can readily access and analyze EDR tools, and due to their uniformity across environments an exploit proven in a lab setting will likely succeed elsewhere.

Sophos have performed deep research into the EDRShiftKiller tool, which ESET have separately shown became accessible to multiple threat actor groups. Cisco Talos have reported via TheRegister observing significant success rates when an EDR kill was attempted by ransomware actors.

With the local EDR agent silently disabled or evaded, how will the threat be discovered?

What are the limitations of relying solely on EDR?

Cyber attackers will inevitably break through boundary defences, through innovation or trickery or exploiting zero-days. Preventive measures can reduce but not completely stop this. The attackers will always then want to expand beyond their initial access point to achieve persistence and discover and reach high value targets within the business. This is the primary domain of network activity monitoring and NDR, which includes responsibility for securing the many devices that cannot run endpoint agents.

In the insights from a CISA Red Team assessment of a US CNI organization, the Red Team was able to maintain access over the course of months and achieve their target outcomes. The top lesson learned in the report was:

“The assessed organization had insufficient technical controls to prevent and detect malicious activity. The organization relied too heavily on host-based endpoint detection and response (EDR) solutions and did not implement sufficient network layer protections.”

This proves that partial, isolated viewpoints are not sufficient to track and analyze what is fundamentally a connected problem – and without the added visibility and detection capabilities of NDR, any downstream SIEM or MDR services also still have nothing to work with.

Why is network detection & response (NDR) critical?

An effective NDR finds threats that disable or can’t be seen by local security agents and generally operates out-of-band, acquiring data from infrastructure such as traffic mirroring from physical or virtual switches. This means that the security system is extremely inaccessible to a threat actor at any stage.

An advanced NDR such as Darktrace / NETWORK is fully capable of detecting even high-end novel and unknown threats.

Detecting exploitation of Ivanti CS/PS with Darktrace / NETWORK

On January 9th 2025, two new vulnerabilities were disclosed in Ivanti Connect Secure and Policy Secure appliances that were under malicious exploitation. Perimeter devices, like Ivanti VPNs, are designed to keep threat actors out of a network, so it's quite serious when these devices are vulnerable.

An NDR solution is critical because it provides network-wide visibility for detecting lateral movement and threats that an EDR might miss, such as identifying command and control sessions (C2) and data exfiltration, even when hidden within encrypted traffic and which an EDR alone may not detect.

Darktrace initially detected suspicious activity connected with the exploitation of CVE-2025-0282 on December 29, 2024 – 11 days before the public disclosure of the vulnerability, this early detection highlights the benefits of an anomaly-based network detection method.

Throughout the campaign and based on the network telemetry available to Darktrace, a wide range of malicious activities were identified, including the malicious use of administrative credentials, the download of suspicious files, and network scanning in the cases investigated.

Darktrace / NETWORK’s autonomous response capabilities played a critical role in containment by autonomously blocking suspicious connections and enforcing normal behavior patterns. At the same time, Darktrace Cyber AI Analyst™ automatically investigated and correlated the anomalous activity into cohesive incidents, revealing the full scope of the compromise.

This case highlights the importance of real-time, AI-driven network monitoring to detect and disrupt stealthy post-exploitation techniques targeting unmanaged or unprotected systems.

Unlocking adaptive protection for evolving cyber risks

Darktrace / NETWORK uses unique AI engines that learn what is normal behavior for an organization’s entire network, continuously analyzing, mapping and modeling every connection to create a full picture of your devices, identities, connections, and potential attack paths.

With its ability to uncover previously unknown threats as well as detect known threats using signatures and threat intelligence, Darktrace is an essential layer of the security stack. Darktrace has helped secure customers against attacks including 2024 threat actor campaigns against Fortinet’s FortiManager , Palo Alto firewall devices, and more.  

Stay tuned for part II of this series which dives deeper into the differences between NDR types.

Credit to Nathaniel Jones VP, Security & AI Strategy, FCISO & Ashanka Iddya, Senior Director of Product Marketing for their contribution to this blog.

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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April 22, 2025

Obfuscation Overdrive: Next-Gen Cryptojacking with Layers

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Out of all the services honeypotted by Darktrace, Docker is the most commonly attacked, with new strains of malware emerging daily. This blog will analyze a novel malware campaign with a unique obfuscation technique and a new cryptojacking technique.

What is obfuscation?

Obfuscation is a common technique employed by threat actors to prevent signature-based detection of their code, and to make analysis more difficult. This novel campaign uses an interesting technique of obfuscating its payload.

Docker image analysis

The attack begins with a request to launch a container from Docker Hub, specifically the kazutod/tene:ten image. Using Docker Hub’s layer viewer, an analyst can quickly identify what the container is designed to do. In this case, the container is designed to run the ten.py script which is built into itself.

 Docker Hub Image Layers, referencing the script ten.py.
Figure 1: Docker Hub Image Layers, referencing the script ten.py.

To gain more information on the Python file, Docker’s built in tooling can be used to download the image (docker pull kazutod/tene:ten) and then save it into a format that is easier to work with (docker image save kazutod/tene:ten -o tene.tar). It can then be extracted as a regular tar file for further investigation.

Extraction of the resulting tar file.
Figure 2: Extraction of the resulting tar file.

The Docker image uses the OCI format, which is a little different to a regular file system. Instead of having a static folder of files, the image consists of layers. Indeed, when running the file command over the sha256 directory, each layer is shown as a tar file, along with a JSON metadata file.

Output of the file command over the sha256 directory.
Figure 3: Output of the file command over the sha256 directory.

As the detailed layers are not necessary for analysis, a single command can be used to extract all of them into a single directory, recreating what the container file system would look like:

find blobs/sha256 -type f -exec sh -c 'file "{}" | grep -q "tar archive" && tar -xf "{}" -C root_dir' \;

Result of running the command above.
Figure 4: Result of running the command above.

The find command can then be used to quickly locate where the ten.py script is.

find root_dir -name ten.py

root_dir/app/ten.py

Details of the above ten.py script.
Figure 5: Details of the above ten.py script.

This may look complicated at first glance, however after breaking it down, it is fairly simple. The script defines a lambda function (effectively a variable that contains executable code) and runs zlib decompress on the output of base64 decode, which is run on the reversed input. The script then runs the lambda function with an input of the base64 string, and then passes it to exec, which runs the decoded string as Python code.

To help illustrate this, the code can be cleaned up to this simplified function:

def decode(input):
   reversed = input[::-1]

   decoded = base64.decode(reversed)
   decompressed = zlib.decompress(decoded)
   return decompressed

decoded_string = decode(the_big_text_blob)
exec(decoded_string) # run the decoded string

This can then be set up as a recipe in Cyberchef, an online tool for data manipulation, to decode it.

Use of Cyberchef to decode the ten.py script.
Figure 6: Use of Cyberchef to decode the ten.py script.

The decoded payload calls the decode function again and puts the output into exec. Copy and pasting the new payload into the input shows that it does this another time. Instead of copy-pasting the output into the input all day, a quick script can be used to decode this.

The script below uses the decode function from earlier in order to decode the base64 data and then uses some simple string manipulation to get to the next payload. The script will run this over and over until something interesting happens.

# Decode the initial base64

decoded = decode(initial)
# Remove the first 11 characters and last 3

# so we just have the next base64 string

clamped = decoded[11:-3]

for i in range(1, 100):
   # Decode the new payload

   decoded = decode(clamped)
   # Print it with the current step so we

   # can see what’s going on

   print(f"Step {i}")

   print(decoded)
   # Fetch the next base64 string from the

   # output, so the next loop iteration will

   # decode it

   clamped = decoded[11:-3]

Result of the 63rd iteration of this script.
Figure 7: Result of the 63rd iteration of this script.

After 63 iterations, the script returns actual code, accompanied by an error from the decode function as a stopping condition was never defined. It not clear what the attacker’s motive to perform so many layers of obfuscation was, as one round of obfuscation versus several likely would not make any meaningful difference to bypassing signature analysis. It’s possible this is an attempt to stop analysts or other hackers from reverse engineering the code. However,  it took a matter of minutes to thwart their efforts.

Cryptojacking 2.0?

Cleaned up version of the de-obfuscated code.
Figure 8: Cleaned up version of the de-obfuscated code.

The cleaned up code indicates that the malware attempts to set up a connection to teneo[.]pro, which appears to belong to a Web3 startup company.

Teneo appears to be a legitimate company, with Crunchbase reporting that they have raised USD 3 million as part of their seed round [1]. Their service allows users to join a decentralized network, to “make sure their data benefits you” [2]. Practically, their node functions as a distributed social media scraper. In exchange for doing so, users are rewarded with “Teneo Points”, which are a private crypto token.

The malware script simply connects to the websocket and sends keep-alive pings in order to gain more points from Teneo and does not do any actual scraping. Based on the website, most of the rewards are gated behind the number of heartbeats performed, which is likely why this works [2].

Checking out the attacker’s dockerhub profile, this sort of attack seems to be their modus operandi. The most recent container runs an instance of the nexus network client, which is a project to perform distributed zero-knowledge compute tasks in exchange for cryptocurrency.

Typically, traditional cryptojacking attacks rely on using XMRig to directly mine cryptocurrency, however as XMRig is highly detected, attackers are shifting to alternative methods of generating crypto. Whether this is more profitable remains to be seen. There is not currently an easy way to determine the earnings of the attackers due to the more “closed” nature of the private tokens. Translating a user ID to a wallet address does not appear to be possible, and there is limited public information about the tokens themselves. For example, the Teneo token is listed as “preview only” on CoinGecko, with no price information available.

Conclusion

This blog explores an example of Python obfuscation and how to unravel it. Obfuscation remains a ubiquitous technique employed by the majority of malware to aid in detection/defense evasion and being able to de-obfuscate code is an important skill for analysts to possess.

We have also seen this new avenue of cryptominers being deployed, demonstrating that attackers’ techniques are still evolving - even tried and tested fields. The illegitimate use of legitimate tools to obtain rewards is an increasingly common vector. For example,  as has been previously documented, 9hits has been used maliciously to earn rewards for the attack in a similar fashion.

Docker remains a highly targeted service, and system administrators need to take steps to ensure it is secure. In general, Docker should never be exposed to the wider internet unless absolutely necessary, and if it is necessary both authentication and firewalling should be employed to ensure only authorized users are able to access the service. Attacks happen every minute, and even leaving the service open for a short period of time may result in a serious compromise.

References

1. https://www.crunchbase.com/funding_round/teneo-protocol-seed--a8ff2ad4

2. https://teneo.pro/

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About the author
Nate Bill
Threat Researcher
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