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February 13, 2022

REvil's Ransomware Business Model & Staying Ahead with AI

Learn more about REvil by exploring a REvil ransomware campaign discovered by Darktrace's AI. Find out how the recent arrests impact cyber security.
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
Oakley Cox
Director of Product
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13
Feb 2022

REvil, also known as Sodinokibi, is a Ransomware-as-a-Service (RaaS) gang responsible for one of the largest ransomware attacks in history. On 14th January 2022, Russia announced it had arrested 14 members of the criminal gang. The move came at the request of the US authorities, who have worked hard with international partners to crack down on the gang. Last year, multiple high-profile attacks were attributed to the REvil group, including the JBS ransomware and Kaseya supply chain incidents.

The arrests are certainly a victory for western law enforcement agencies, and follows November’s announcement from Europol that seven arrests of REvil affiliates had been made in the preceding months. The question is: to what extent will these arrests disrupt the gang’s operations, and for how long?

Early indications from security researchers at ReversingLabs indicates REvil activity has been unaffected. Statistics on REvil implants two weeks after the Russian arrests are unchanged, and if anything indicate a modest increase.

This continued activity implies one of two scenarios:

  • The flurry of arrests have only impacted ‘middle men’ within the criminal gang’s hierarchy
  • REvil’s ransomware-as-a-service model is resilient enough to survive disruption from law enforcement

Both scenarios are worrisome to those who may fall prey to ransomware gangs, and the reality is likely to be a far more complex mixture of these and other factors. The crackdown on ransomware is long overdue, but the battle is likely to be a long one. Law enforcement agencies need to disrupt the business model to such an extent that it no longer becomes profitable or favorable to be in the ransomware business, and this is likely to take months or even years.

So as the crackdown on ransomware plays out on the biggest stage, what comfort, if any, can security teams take from recent events?

Staying ahead of the evolving RaaS model with AI

A joint report on ransomware issued recently by the FBI, CISA, the NCSC, the ACSC and the NSA highlighted key trends over the past year:

  • RaaS has become increasingly professionalized, with business models and processes now well established.
  • The business model complicates attribution because there are complex networks of developers, affiliates, and freelancers.
  • Ransomware groups are sharing victim information with each other, diversifying the threat to targeted organizations.

In summary, the report illuminates how ransomware gangs have become increasingly adaptable when it comes to evading law enforcement and maximizing profit from ransom payments. Multiple groups have faded away, or retired, only to reappear under a different name and with a slightly updated playbook. The tactics, techniques, and procedures (TTPs) differ from victim to victim, largely because attacks are conducted by different ransomware operators and affiliates.

This is troubling for law enforcement bodies trying to crack down on the individuals behind these attacks. When a RaaS group like REvil consists of an amorphous and ever-changing web of associates, making individual arrests is a constant game of catch up, and will be unlikely to bring down the group as a whole.

The same battle is being played out on the scale of individual attack campaigns. Security tools focused on the hallmarks of previously encountered threats are also in a continuous state of catch up: by the time a single attack is detected, fingerprinted, and stored for next time, attackers and their techniques have moved on.

But there is another option available to defenders, who are increasingly turning to Self-Learning AI to stay one step ahead of attackers. By learning its digital surroundings and identifying subtle deviations indicative of an attack, this technology can detect and respond to novel attacks on the first encounter. Below is an example of how Self-Learning AI detected an attack launched by REvil without the use of rules or signatures.

REvil threat find

In the summer of 2021, a REvil affiliate launched an attack against a health and social care organization – a sector that has seen a big increase in cyber-attacks since the start of the global pandemic. While the attack was detected by Darktrace’s AI without using rules or signatures, the security team was not monitoring Darktrace at the time. In the absence of Autonomous Response – which would have taken targeted action to contain the threat – the attack was allowed to progress.

After gaining access to the network via the laptop of a remote worker, the attacker was able to abuse a legitimate remote desktop (RDP) connection to a corporate jump server to bruteforce additional credentials.

Once equipped with more credentials, the attacker connected to multiple internal devices via RDP, including a second jump server. Data exfiltration began from the initially compromised server over RDP port 3389.

Two weeks later, the attacker identified the organization’s crown jewels, stored on a third server, and attempted to initiate command and control (C2) communications. The server made a number of unusual external connections, including attempts to connect to a rare domain that resembled the pattern of activity associated with REvil’s earlier Kaseya ransomware campaign.

Darktrace for Endpoint, which was running on remote user devices, provided additional visibility, enabling the security team to determine the initially compromised user device. Had Antigena been active on the endpoint, it would have intervened to stop this unusual activity by blocking the specific unusual connections – containing the attack without impacting normal business operations.

Connecting the dots of a low-and-slow attack

The total dwell time of the attacker was 22 days. They were patient, and undertook actions in bursts of activity often with days in between. This pattern of behavior is not uncommon for ransomware attacks, particularly those using the RaaS model in which each step may be performed by different gang members or affiliates.

Darktrace’s Cyber AI Analyst was able to track in real time the complete attack lifecycle over several weeks, stitching together the separate phases of the attack into a coherent security incident.

Figure 1: Cyber AI Analyst reveals the complete attack kill chain

New name, same game

This attack is another case of threat actors living off the land: using legitimate programs and processes that were already in use in the environment to perform malicious activity. This can be very difficult to detect with traditional tools that are based on static use cases and cannot differentiate a legitimate RDP session from a malicious one.

As cyber-criminal groups like REvil continue to defy law enforcement efforts, defenders need to stay ahead with AI technology that learns its environment, adapts as it changes and grows, and responds to threats based on subtle deviations that indicate an emerging attack. Autonomous Response has been adopted by over thousands of organizations across all areas of the digital estate – from email and cloud services to endpoint devices, stopping ransomware attacks early, before encryption is achieved.

Thanks to Darktrace analyst Petal Beharry for her insights on the above threat find.

Technical details

Darktrace model detections:

  • Device / RDP Scan
  • Device / Bruteforce Activity
  • Compliance / Outbound Remote Desktop
  • Anomalous Connection / Upload via Remote Desktop
  • Anomalous Connection / Download and Upload
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Active Remote Desktop Tunnel
  • Device / New or Uncommon SMB Named Pipe
  • Device / Large Number of Connections to New Endpoints

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
Oakley Cox
Director of Product

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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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